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Data collection

data analysis

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• Academia - Data Analysis
• U.S. Department of Health and Human Services - Office of Research Integrity - Data Analysis
• Chemistry LibreTexts - Data Analysis
• IBM - What is Exploratory Data Analysis?
• Table Of Contents

data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “Big Data,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

• The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
• The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
• Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions? 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate . Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, L earn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate :

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. 

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months. 

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more : Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].

Get started with Coursera

If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization . Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate . In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Where is data analytics used ‎.

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance. ‎

What are the top skills for a data analyst? ‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python),  machine learning, and spreadsheets.

Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022 ‎

What is a data analyst job salary? ‎

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location. ‎

Do data analysts need to be good at math? ‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference? ‎

Article sources

World Economic Forum. " The Future of Jobs Report 2023 , https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 19, 2024.

Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed March 19, 2024.

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Research vs Analysis: What's the Difference and Why It Matters

When it comes to data-driven business decisions, research and analysis are often used interchangeably. However, these terms are not synonymous, and understanding the difference between them is crucial for making informed decisions.

Here are our five key takeaways:

• Research is the process of finding information, while analysis is the process of evaluating and interpreting that information to make informed decisions.
• Analysis is a critical step in the decision-making process, providing context and insights to support informed choices.
• Good research is essential to conducting effective analysis, but research alone is not enough to inform decision-making.
• Analysis requires a range of skills, including data modeling, statistics, and critical thinking.
• While analysis can be time-consuming and resource-intensive, it is a necessary step for making informed decisions based on data.

In this article, we'll explore the key differences between research and analysis and why they matter in the decision-making process.

Table of Contents

Understanding research vs analysis, why analysis matters in the decision-making process, the role of research in analysis, skills needed for effective analysis, the time and resource requirements for analysis, the unified stack for modern data teams, get a personalized platform demo & 30-minute q&a session with a solution engineer, introduction.

This is a guest post by Bill Inmon. Bill Inmon is a pioneer in data warehousing, widely known as the “Father of Data Warehousing.” He is also the author of more than 50 books and over 650 articles on data warehousing, data management, and information technology.

The search vendors will tell you that there is no difference. Indeed, when you do analysis you have to do research. But there are some very real and very important differences.

When it comes to the methodology of data science, understanding the main difference between research and analysis is crucial.

What is Research?

Research is the process of collecting and analyzing data, information, or evidence to answer a specific question or to solve a problem. It involves identifying a research question, designing a study or experiment, collecting and analyzing data, and drawing conclusions based on the results.

Research is typically focused on gathering information through various qualitative research methods, in order to develop an understanding of a particular topic or phenomenon.

In its simplest form, it means we go look for something. We go to a library and we find some books. Or we go to the Internet and find a good restaurant to go to. Or we go to the Bible and look up the story of Cain and Abel. To research means to go to a body of elements and find the one or two that we need for our purposes.

What are some common research methods?

There are many research methods, but some common ones include surveys, experiments, observational studies, case studies, and interviews. Each method has its strengths and weaknesses, and the choice of method depends on the research question, the type of data needed, and the available resources.

What is Analysis?

Analysis is the process of breaking down complex information into smaller parts to gain a better understanding of it. Then take that information and apply statistical analysis and other methods to draw conclusions and make predictions.

Somewhat similar to research, we go to a body of elements and find one or two that are of interest to us. Then after finding what we are looking for we do further investigation. 

That further investigation may take many forms. 

• We may compare and contrast the elements
• We may simply count and summarize the elements
• We may look at many elements and qualify some of them and disqualify the others 

The goal of analysis is to answer questions or solve problems. Analysis often involves examining and interpreting data sets, identifying patterns and trends, and drawing predictive conclusions based on the evidence.

In contrast to research, which is focused on gathering data, analysis is focused on making sense of the data that has already been collected.

What are some common analysis methods?

In the analysis process, data scientists use a variety of techniques and tools to explore and analyze the data, such as regression analysis, clustering, and machine learning algorithms. These techniques are used to uncover patterns, relationships, and trends in the data that can help inform business decisions and strategies.

There are many analysis methods, but some common ones include descriptive statistics, inferential statistics, content analysis, thematic analysis, and discourse analysis. Each method has its strengths and weaknesses, and the choice of method depends on the type of data collected, the research question, and the available resources.

Analysis is a critical step in the decision-making process. It provides context and insights to support informed choices. Without analysis, decision-makers risk making choices based on incomplete or inaccurate information, leading to poor outcomes. Effective analysis helps decision-makers understand the impact of different scenarios, identify potential risks, and identify opportunities for improvement.

In almost every case, the analysis starts with quantitative research. So it’s almost like differentiating between baiting a hook and catching a fish. If you are going to catch a fish, you have to start by baiting a hook.

Although that might not be the best analogy, the role of research in analysis works in the same order. Good research is essential to conducting effective analysis. It provides a foundation of knowledge and understanding, helping analysts identify patterns, trends, and relationships in data collection. However, research alone is not enough to inform decision-making. Just like baiting a hook alone is not enough to catch a fish. 

Effective analysis requires a range of skills, including data modeling, statistics, and critical thinking. Data modeling involves creating a conceptual framework for understanding the data, while statistics helps data analysts identify patterns and relationships in the data sets. Critical thinking is essential for evaluating data analytics and drawing insights that support informed decision-making.

Related Reading : The Best Data Modeling Tools: Advice & Comparison

Just because you search for something does not mean you are going to analyze it.

Analysis can be time-consuming and resource-intensive, requiring significant investments in technology, talent, and infrastructure. However, It is necessary to analyze something when you need to extract meaningful insights or draw conclusions based on big data or information gathered through quantitative research.

Whether you're conducting research or performing statistical analysis, having a solid understanding of your data and how to interpret it is essential for success. Data scientists play a critical role in this process, as they have the skills and expertise to apply statistical methods and other techniques to make sense of complex data sets.

Organizations that invest in effective analysis capabilities are better positioned to make predictive data-driven business decisions that support their strategic goals. Without quantitative analysis, research may remain incomplete or inconclusive, and the data gathered may not be effectively used.

Related Reading : 7 Best Data Analysis Tools

How Integrate.io Can Help

When it comes to search and analysis, having access to accurate and reliable data is essential for making informed decisions. This is where Integrate.io comes in - as a big data integration platform, it enables businesses to connect and combine data from a variety of sources, making it easier to search for and analyze the information that's most relevant to their needs. By streamlining the data integration process, Integrate.io helps businesses get the most out of their data collection, enabling them to make more informed decisions and gain a competitive edge in their respective industries.

In conclusion, the main difference between research and analysis lies in the approach to data collection and interpretation. While research is focused on gathering information through qualitative research methods, analysis is focused on drawing predictive conclusions based on statistical analysis and other techniques. By leveraging the power of data science and tools like Integrate.io , businesses can make better decisions based on data-driven insights.

Tags: big data, data-analytics, Versus

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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

QuestionPro offers solutions for every issue and industry, making it more than just survey software. For handling data, we also have systems like our InsightsHub research library.

You may make crucial decisions quickly while using QuestionPro to understand your clients and other study subjects better. Make use of the possibilities of the enterprise-grade research suite right away!

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Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
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• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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Statistical Analysis in Research: Meaning, Methods and Types

Home » Videos » Statistical Analysis in Research: Meaning, Methods and Types

The scientific method is an empirical approach to acquiring new knowledge by making skeptical observations and analyses to develop a meaningful interpretation. It is the basis of research and the primary pillar of modern science. Researchers seek to understand the relationships between factors associated with the phenomena of interest. In some cases, research works with vast chunks of data, making it difficult to observe or manipulate each data point. As a result, statistical analysis in research becomes a means of evaluating relationships and interconnections between variables with tools and analytical techniques for working with large data. Since researchers use statistical power analysis to assess the probability of finding an effect in such an investigation, the method is relatively accurate. Hence, statistical analysis in research eases analytical methods by focusing on the quantifiable aspects of phenomena.

What is Statistical Analysis in Research? A Simplified Definition

Statistical analysis uses quantitative data to investigate patterns, relationships, and patterns to understand real-life and simulated phenomena. The approach is a key analytical tool in various fields, including academia, business, government, and science in general. This statistical analysis in research definition implies that the primary focus of the scientific method is quantitative research. Notably, the investigator targets the constructs developed from general concepts as the researchers can quantify their hypotheses and present their findings in simple statistics.

When a business needs to learn how to improve its product, they collect statistical data about the production line and customer satisfaction. Qualitative data is valuable and often identifies the most common themes in the stakeholders’ responses. On the other hand, the quantitative data creates a level of importance, comparing the themes based on their criticality to the affected persons. For instance, descriptive statistics highlight tendency, frequency, variation, and position information. While the mean shows the average number of respondents who value a certain aspect, the variance indicates the accuracy of the data. In any case, statistical analysis creates simplified concepts used to understand the phenomenon under investigation. It is also a key component in academia as the primary approach to data representation, especially in research projects, term papers and dissertations. 

Most Useful Statistical Analysis Methods in Research

Using statistical analysis methods in research is inevitable, especially in academic assignments, projects, and term papers. It’s always advisable to seek assistance from your professor or you can try research paper writing by CustomWritings before you start your academic project or write statistical analysis in research paper. Consulting an expert when developing a topic for your thesis or short mid-term assignment increases your chances of getting a better grade. Most importantly, it improves your understanding of research methods with insights on how to enhance the originality and quality of personalized essays. Professional writers can also help select the most suitable statistical analysis method for your thesis, influencing the choice of data and type of study.

Descriptive Statistics

Descriptive statistics is a statistical method summarizing quantitative figures to understand critical details about the sample and population. A description statistic is a figure that quantifies a specific aspect of the data. For instance, instead of analyzing the behavior of a thousand students, research can identify the most common actions among them. By doing this, the person utilizes statistical analysis in research, particularly descriptive statistics.

• Measures of central tendency . Central tendency measures are the mean, mode, and media or the averages denoting specific data points. They assess the centrality of the probability distribution, hence the name. These measures describe the data in relation to the center.
• Measures of frequency . These statistics document the number of times an event happens. They include frequency, count, ratios, rates, and proportions. Measures of frequency can also show how often a score occurs.
• Measures of dispersion/variation . These descriptive statistics assess the intervals between the data points. The objective is to view the spread or disparity between the specific inputs. Measures of variation include the standard deviation, variance, and range. They indicate how the spread may affect other statistics, such as the mean.
• Measures of position . Sometimes researchers can investigate relationships between scores. Measures of position, such as percentiles, quartiles, and ranks, demonstrate this association. They are often useful when comparing the data to normalized information.

Inferential Statistics

Inferential statistics is critical in statistical analysis in quantitative research. This approach uses statistical tests to draw conclusions about the population. Examples of inferential statistics include t-tests, F-tests, ANOVA, p-value, Mann-Whitney U test, and Wilcoxon W test. This

Common Statistical Analysis in Research Types

Although inferential and descriptive statistics can be classified as types of statistical analysis in research, they are mostly considered analytical methods. Types of research are distinguishable by the differences in the methodology employed in analyzing, assembling, classifying, manipulating, and interpreting data. The categories may also depend on the type of data used.

Predictive Analysis

Predictive research analyzes past and present data to assess trends and predict future events. An excellent example of predictive analysis is a market survey that seeks to understand customers’ spending habits to weigh the possibility of a repeat or future purchase. Such studies assess the likelihood of an action based on trends.

Prescriptive Analysis

On the other hand, a prescriptive analysis targets likely courses of action. It’s decision-making research designed to identify optimal solutions to a problem. Its primary objective is to test or assess alternative measures.

Causal Analysis

Causal research investigates the explanation behind the events. It explores the relationship between factors for causation. Thus, researchers use causal analyses to analyze root causes, possible problems, and unknown outcomes.

Mechanistic Analysis

This type of research investigates the mechanism of action. Instead of focusing only on the causes or possible outcomes, researchers may seek an understanding of the processes involved. In such cases, they use mechanistic analyses to document, observe, or learn the mechanisms involved.

Exploratory Data Analysis

Similarly, an exploratory study is extensive with a wider scope and minimal limitations. This type of research seeks insight into the topic of interest. An exploratory researcher does not try to generalize or predict relationships. Instead, they look for information about the subject before conducting an in-depth analysis.

The Importance of Statistical Analysis in Research

As a matter of fact, statistical analysis provides critical information for decision-making. Decision-makers require past trends and predictive assumptions to inform their actions. In most cases, the data is too complex or lacks meaningful inferences. Statistical tools for analyzing such details help save time and money, deriving only valuable information for assessment. An excellent statistical analysis in research example is a randomized control trial (RCT) for the Covid-19 vaccine. You can download a sample of such a document online to understand the significance such analyses have to the stakeholders. A vaccine RCT assesses the effectiveness, side effects, duration of protection, and other benefits. Hence, statistical analysis in research is a helpful tool for understanding data.

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters  

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles  

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles  

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo:  http://www.qsrinternational.com/products.aspx

Atlas.ti:  http://www.atlasti.com/webinars.html

R- RQDA package:  http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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Table of Contents

What is data analysis, what is the data analysis process, why is data analysis important, data analysis methods with examples, applications of data analysis, top data analysis techniques to analyze data, what is the importance of data analysis in research, future trends in data analysis, choose the right program, what is data analysis: a comprehensive guide.

Analysis involves breaking down a whole into its parts for detailed study. Data analysis is the practice of transforming raw data into actionable insights for informed decision-making. It involves collecting and examining data to answer questions, validate hypotheses, or refute theories.

In the contemporary business landscape, gaining a competitive edge is imperative, given the challenges such as rapidly evolving markets, economic unpredictability, fluctuating political environments, capricious consumer sentiments, and even global health crises. These challenges have reduced the room for error in business operations. For companies striving not only to survive but also to thrive in this demanding environment, the key lies in embracing the concept of data analysis . This involves strategically accumulating valuable, actionable information, which is leveraged to enhance decision-making processes.

If you're interested in forging a career in data analysis and wish to discover the top data analysis courses in 2024, we invite you to explore our informative video. It will provide insights into the opportunities to develop your expertise in this crucial field.

Data analysis inspects, cleans, transforms, and models data to extract insights and support decision-making. As a data analyst , your role involves dissecting vast datasets, unearthing hidden patterns, and translating numbers into actionable information.

The data analysis process is a structured sequence of steps that lead from raw data to actionable insights. Here are the answers to what is data analysis:

• Data Collection: Gather relevant data from various sources, ensuring data quality and integrity.
• Data Cleaning: Identify and rectify errors, missing values, and inconsistencies in the dataset. Clean data is crucial for accurate analysis.
• Exploratory Data Analysis (EDA): Conduct preliminary analysis to understand the data's characteristics, distributions, and relationships. Visualization techniques are often used here.
• Data Transformation: Prepare the data for analysis by encoding categorical variables, scaling features, and handling outliers, if necessary.
• Model Building: Depending on the objectives, apply appropriate data analysis methods, such as regression, clustering, or deep learning.
• Model Evaluation: Depending on the problem type, assess the models' performance using metrics like Mean Absolute Error, Root Mean Squared Error , or others.
• Interpretation and Visualization: Translate the model's results into actionable insights. Visualizations, tables, and summary statistics help in conveying findings effectively.
• Deployment: Implement the insights into real-world solutions or strategies, ensuring that the data-driven recommendations are implemented.

Data analysis plays a pivotal role in today's data-driven world. It helps organizations harness the power of data, enabling them to make decisions, optimize processes, and gain a competitive edge. By turning raw data into meaningful insights, data analysis empowers businesses to identify opportunities, mitigate risks, and enhance their overall performance.

1. Informed Decision-Making

Data analysis is the compass that guides decision-makers through a sea of information. It enables organizations to base their choices on concrete evidence rather than intuition or guesswork. In business, this means making decisions more likely to lead to success, whether choosing the right marketing strategy, optimizing supply chains, or launching new products. By analyzing data, decision-makers can assess various options' potential risks and rewards, leading to better choices.

2. Improved Understanding

Data analysis provides a deeper understanding of processes, behaviors, and trends. It allows organizations to gain insights into customer preferences, market dynamics, and operational efficiency .

3. Competitive Advantage

Organizations can identify opportunities and threats by analyzing market trends, consumer behavior , and competitor performance. They can pivot their strategies to respond effectively, staying one step ahead of the competition. This ability to adapt and innovate based on data insights can lead to a significant competitive advantage.

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4. Risk Mitigation

Data analysis is a valuable tool for risk assessment and management. Organizations can assess potential issues and take preventive measures by analyzing historical data. For instance, data analysis detects fraudulent activities in the finance industry by identifying unusual transaction patterns. This not only helps minimize financial losses but also safeguards the reputation and trust of customers.

5. Efficient Resource Allocation

Data analysis helps organizations optimize resource allocation. Whether it's allocating budgets, human resources, or manufacturing capacities, data-driven insights can ensure that resources are utilized efficiently. For example, data analysis can help hospitals allocate staff and resources to the areas with the highest patient demand, ensuring that patient care remains efficient and effective.

6. Continuous Improvement

Data analysis is a catalyst for continuous improvement. It allows organizations to monitor performance metrics, track progress, and identify areas for enhancement. This iterative process of analyzing data, implementing changes, and analyzing again leads to ongoing refinement and excellence in processes and products.

Descriptive Analysis

Descriptive analysis involves summarizing and organizing data to describe the current situation. It uses measures like mean, median, mode, and standard deviation to describe the main features of a data set.

Example: A company analyzes sales data to determine the monthly average sales over the past year. They calculate the mean sales figures and use charts to visualize the sales trends.

Diagnostic Analysis

Diagnostic analysis goes beyond descriptive statistics to understand why something happened. It looks at data to find the causes of events.

Example: After noticing a drop in sales, a retailer uses diagnostic analysis to investigate the reasons. They examine marketing efforts, economic conditions, and competitor actions to identify the cause.

Predictive Analysis

Predictive analysis uses historical data and statistical techniques to forecast future outcomes. It often involves machine learning algorithms.

Example: An insurance company uses predictive analysis to assess the risk of claims by analyzing historical data on customer demographics, driving history, and claim history.

Prescriptive Analysis

Prescriptive analysis recommends actions based on data analysis. It combines insights from descriptive, diagnostic, and predictive analyses to suggest decision options.

Example: An online retailer uses prescriptive analysis to optimize its inventory management . The system recommends the best products to stock based on demand forecasts and supplier lead times.

Quantitative Analysis

Quantitative analysis involves using mathematical and statistical techniques to analyze numerical data.

Example: A financial analyst uses quantitative analysis to evaluate a stock's performance by calculating various financial ratios and performing statistical tests.

Qualitative Research

Qualitative research focuses on understanding concepts, thoughts, or experiences through non-numerical data like interviews, observations, and texts.

Example: A researcher interviews customers to understand their feelings and experiences with a new product, analyzing the interview transcripts to identify common themes.

Time Series Analysis

Time series analysis involves analyzing data points collected or recorded at specific time intervals to identify trends , cycles, and seasonal variations.

Example: A climatologist studies temperature changes over several decades using time series analysis to identify patterns in climate change.

Regression Analysis

Regression analysis assesses the relationship between a dependent variable and one or more independent variables.

Example: An economist uses regression analysis to examine the impact of interest, inflation, and employment rates on economic growth.

Cluster Analysis

Cluster analysis groups data points into clusters based on their similarities.

Example: A marketing team uses cluster analysis to segment customers into distinct groups based on purchasing behavior, demographics, and interests for targeted marketing campaigns.

Sentiment Analysis

Sentiment analysis identifies and categorizes opinions expressed in the text to determine the sentiment behind it (positive, negative, or neutral).

Example: A social media manager uses sentiment analysis to gauge public reaction to a new product launch by analyzing tweets and comments.

Factor Analysis

Factor analysis reduces data dimensions by identifying underlying factors that explain the patterns observed in the data.

Example: A psychologist uses factor analysis to identify underlying personality traits from a large set of behavioral variables.

Statistics involves the collection, analysis, interpretation, and presentation of data.

Example: A researcher uses statistics to analyze survey data, calculate the average responses, and test hypotheses about population behavior.

Content Analysis

Content analysis systematically examines text, images, or media to quantify and analyze the presence of certain words, themes, or concepts.

Example: A political scientist uses content analysis to study election speeches and identify common themes and rhetoric from candidates.

Monte Carlo Simulation

Monte Carlo simulation uses random sampling and statistical modeling to estimate mathematical functions and mimic the operation of complex systems.

Example: A financial analyst uses Monte Carlo simulation to assess a portfolio's risk by simulating various market scenarios and their impact on asset prices.

Cohort Analysis

Cohort analysis studies groups of people who share a common characteristic or experience within a defined time period to understand their behavior over time.

Example: An e-commerce company conducts cohort analysis to track the purchasing behavior of customers who signed up in the same month to identify retention rates and revenue trends.

Grounded Theory

Grounded theory involves generating theories based on systematically gathered and analyzed data through the research process.

Example: A sociologist uses grounded theory to develop a theory about social interactions in online communities by analyzing participant observations and interviews.

Text Analysis

Text analysis involves extracting meaningful information from text through techniques like natural language processing (NLP).

Example: A customer service team uses text analysis to automatically categorize and prioritize customer support emails based on the content of the messages.

Data Mining

Data mining involves exploring large datasets to discover patterns, associations, or trends that can provide actionable insights.

Example: A retail company uses data mining to identify purchasing patterns and recommend products to customers based on their previous purchases.

Decision-Making

Decision-making involves choosing the best course of action from available options based on data analysis and evaluation.

Example: A manager uses data-driven decision-making to allocate resources efficiently by analyzing performance metrics and cost-benefit analyses.

Neural Network

A neural network is a computational model inspired by the human brain used in machine learning to recognize patterns and make predictions.

Example: A tech company uses neural networks to develop a facial recognition system that accurately identifies individuals from images.

Data Cleansing

Data cleansing involves identifying and correcting inaccuracies and inconsistencies in data to improve its quality.

Example: A data analyst cleans a customer database by removing duplicates, correcting typos, and filling in missing values.

Narrative Analysis

Narrative analysis examines stories or accounts to understand how people make sense of events and experiences.

Example: A researcher uses narrative analysis to study patients' stories about their experiences with healthcare to identify common themes and insights into patient care.

Data Collection

Data collection is the process of gathering information from various sources to be used in analysis.

Example: A market researcher collects data through surveys, interviews, and observations to study consumer preferences.

Data Interpretation

Data interpretation involves making sense of data by analyzing and drawing conclusions from it.

Example: After analyzing sales data, a manager interprets the results to understand the effectiveness of a recent marketing campaign and plans future strategies based on these insights.

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Data analysis is a versatile and indispensable tool that finds applications across various industries and domains. Its ability to extract actionable insights from data has made it a fundamental component of decision-making and problem-solving. Let's explore some of the key applications of data analysis:

1. Business and Marketing

• Market Research: Data analysis helps businesses understand market trends, consumer preferences, and competitive landscapes. It aids in identifying opportunities for product development, pricing strategies, and market expansion.
• Sales Forecasting: Data analysis models can predict future sales based on historical data, seasonality, and external factors. This helps businesses optimize inventory management and resource allocation.

2. Healthcare and Life Sciences

• Disease Diagnosis: Data analysis is vital in medical diagnostics, from interpreting medical images (e.g., MRI, X-rays) to analyzing patient records. Machine learning models can assist in early disease detection.
• Drug Discovery: Pharmaceutical companies use data analysis to identify potential drug candidates, predict their efficacy, and optimize clinical trials.
• Genomics and Personalized Medicine: Genomic data analysis enables personalized treatment plans by identifying genetic markers that influence disease susceptibility and response to therapies.
• Risk Management: Financial institutions use data analysis to assess credit risk, detect fraudulent activities, and model market risks.
• Algorithmic Trading: Data analysis is integral to developing trading algorithms that analyze market data and execute trades automatically based on predefined strategies.
• Fraud Detection: Credit card companies and banks employ data analysis to identify unusual transaction patterns and detect fraudulent activities in real-time.

4. Manufacturing and Supply Chain

• Quality Control: Data analysis monitors and controls product quality on manufacturing lines. It helps detect defects and ensure consistency in production processes.
• Inventory Optimization: By analyzing demand patterns and supply chain data, businesses can optimize inventory levels, reduce carrying costs, and ensure timely deliveries.

5. Social Sciences and Academia

• Social Research: Researchers in social sciences analyze survey data, interviews, and textual data to study human behavior, attitudes, and trends. It helps in policy development and understanding societal issues.
• Academic Research: Data analysis is crucial to scientific physics, biology, and environmental science research. It assists in interpreting experimental results and drawing conclusions.

6. Internet and Technology

• Search Engines: Google uses complex data analysis algorithms to retrieve and rank search results based on user behavior and relevance.
• Recommendation Systems: Services like Netflix and Amazon leverage data analysis to recommend content and products to users based on their past preferences and behaviors.

7. Environmental Science

• Climate Modeling: Data analysis is essential in climate science. It analyzes temperature, precipitation, and other environmental data. It helps in understanding climate patterns and predicting future trends.
• Environmental Monitoring: Remote sensing data analysis monitors ecological changes, including deforestation, water quality, and air pollution.

1. Descriptive Statistics

Descriptive statistics provide a snapshot of a dataset's central tendencies and variability. These techniques help summarize and understand the data's basic characteristics.

2. Inferential Statistics

Inferential statistics involve making predictions or inferences based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis. These methods are crucial for drawing conclusions from data and assessing the significance of findings.

3. Regression Analysis

It explores the relationship between one or more independent variables and a dependent variable. It is widely used for prediction and understanding causal links. Linear, logistic, and multiple regression are common in various fields.

4. Clustering Analysis

It is an unsupervised learning method that groups similar data points. K-means clustering and hierarchical clustering are examples. This technique is used for customer segmentation, anomaly detection, and pattern recognition.

5. Classification Analysis

Classification analysis assigns data points to predefined categories or classes. It's often used in applications like spam email detection, image recognition, and sentiment analysis. Popular algorithms include decision trees, support vector machines, and neural networks.

6. Time Series Analysis

Time series analysis deals with data collected over time, making it suitable for forecasting and trend analysis. Techniques like moving averages, autoregressive integrated moving averages (ARIMA), and exponential smoothing are applied in fields like finance, economics, and weather forecasting.

7. Text Analysis (Natural Language Processing - NLP)

Text analysis techniques, part of NLP , enable extracting insights from textual data. These methods include sentiment analysis, topic modeling, and named entity recognition. Text analysis is widely used for analyzing customer reviews, social media content, and news articles.

8. Principal Component Analysis

It is a dimensionality reduction technique that simplifies complex datasets while retaining important information. It transforms correlated variables into a set of linearly uncorrelated variables, making it easier to analyze and visualize high-dimensional data.

9. Anomaly Detection

Anomaly detection identifies unusual patterns or outliers in data. It's critical in fraud detection, network security, and quality control. Techniques like statistical methods, clustering-based approaches, and machine learning algorithms are employed for anomaly detection.

10. Data Mining

Data mining involves the automated discovery of patterns, associations, and relationships within large datasets. Techniques like association rule mining, frequent pattern analysis, and decision tree mining extract valuable knowledge from data.

11. Machine Learning and Deep Learning

ML and deep learning algorithms are applied for predictive modeling, classification, and regression tasks. Techniques like random forests, support vector machines, and convolutional neural networks (CNNs) have revolutionized various industries, including healthcare, finance, and image recognition.

12. Geographic Information Systems (GIS) Analysis

GIS analysis combines geographical data with spatial analysis techniques to solve location-based problems. It's widely used in urban planning, environmental management, and disaster response.

• Uncovering Patterns and Trends: Data analysis allows researchers to identify patterns, trends, and relationships within the data. By examining these patterns, researchers can better understand the phenomena under investigation. For example, in epidemiological research, data analysis can reveal the trends and patterns of disease outbreaks, helping public health officials take proactive measures.
• Testing Hypotheses: Research often involves formulating hypotheses and testing them. Data analysis provides the means to evaluate hypotheses rigorously. Through statistical tests and inferential analysis, researchers can determine whether the observed patterns in the data are statistically significant or simply due to chance.
• Making Informed Conclusions: Data analysis helps researchers draw meaningful and evidence-based conclusions from their research findings. It provides a quantitative basis for making claims and recommendations. In academic research, these conclusions form the basis for scholarly publications and contribute to the body of knowledge in a particular field.
• Enhancing Data Quality: Data analysis includes data cleaning and validation processes that improve the quality and reliability of the dataset. Identifying and addressing errors, missing values, and outliers ensures that the research results accurately reflect the phenomena being studied.
• Supporting Decision-Making: In applied research, data analysis assists decision-makers in various sectors, such as business, government, and healthcare. Policy decisions, marketing strategies, and resource allocations are often based on research findings.
• Identifying Outliers and Anomalies: Outliers and anomalies in data can hold valuable information or indicate errors. Data analysis techniques can help identify these exceptional cases, whether medical diagnoses, financial fraud detection, or product quality control.
• Revealing Insights: Research data often contain hidden insights that are not immediately apparent. Data analysis techniques, such as clustering or text analysis, can uncover these insights. For example, social media data sentiment analysis can reveal public sentiment and trends on various topics in social sciences.
• Forecasting and Prediction: Data analysis allows for the development of predictive models. Researchers can use historical data to build models forecasting future trends or outcomes. This is valuable in fields like finance for stock price predictions, meteorology for weather forecasting, and epidemiology for disease spread projections.
• Optimizing Resources: Research often involves resource allocation. Data analysis helps researchers and organizations optimize resource use by identifying areas where improvements can be made, or costs can be reduced.
• Continuous Improvement: Data analysis supports the iterative nature of research. Researchers can analyze data, draw conclusions, and refine their hypotheses or research designs based on their findings. This cycle of analysis and refinement leads to continuous improvement in research methods and understanding.

Data analysis is an ever-evolving field driven by technological advancements. The future of data analysis promises exciting developments that will reshape how data is collected, processed, and utilized. Here are some of the key trends of data analysis:

1. Artificial Intelligence and Machine Learning Integration

Artificial intelligence (AI) and machine learning (ML) are expected to play a central role in data analysis. These technologies can automate complex data processing tasks, identify patterns at scale, and make highly accurate predictions. AI-driven analytics tools will become more accessible, enabling organizations to harness the power of ML without requiring extensive expertise.

2. Augmented Analytics

Augmented analytics combines AI and natural language processing (NLP) to assist data analysts in finding insights. These tools can automatically generate narratives, suggest visualizations, and highlight important trends within data. They enhance the speed and efficiency of data analysis, making it more accessible to a broader audience.

3. Data Privacy and Ethical Considerations

As data collection becomes more pervasive, privacy concerns and ethical considerations will gain prominence. Future data analysis trends will prioritize responsible data handling, transparency, and compliance with regulations like GDPR . Differential privacy techniques and data anonymization will be crucial in balancing data utility with privacy protection.

4. Real-time and Streaming Data Analysis

The demand for real-time insights will drive the adoption of real-time and streaming data analysis. Organizations will leverage technologies like Apache Kafka and Apache Flink to process and analyze data as it is generated. This trend is essential for fraud detection, IoT analytics, and monitoring systems.

5. Quantum Computing

It can potentially revolutionize data analysis by solving complex problems exponentially faster than classical computers. Although quantum computing is in its infancy, its impact on optimization, cryptography , and simulations will be significant once practical quantum computers become available.

6. Edge Analytics

With the proliferation of edge devices in the Internet of Things (IoT), data analysis is moving closer to the data source. Edge analytics allows for real-time processing and decision-making at the network's edge, reducing latency and bandwidth requirements.

7. Explainable AI (XAI)

Interpretable and explainable AI models will become crucial, especially in applications where trust and transparency are paramount. XAI techniques aim to make AI decisions more understandable and accountable, which is critical in healthcare and finance.

8. Data Democratization

The future of data analysis will see more democratization of data access and analysis tools. Non-technical users will have easier access to data and analytics through intuitive interfaces and self-service BI tools , reducing the reliance on data specialists.

9. Advanced Data Visualization

Data visualization tools will continue to evolve, offering more interactivity, 3D visualization, and augmented reality (AR) capabilities. Advanced visualizations will help users explore data in new and immersive ways.

10. Ethnographic Data Analysis

Ethnographic data analysis will gain importance as organizations seek to understand human behavior, cultural dynamics, and social trends. This qualitative data analysis approach and quantitative methods will provide a holistic understanding of complex issues.

11. Data Analytics Ethics and Bias Mitigation

Ethical considerations in data analysis will remain a key trend. Efforts to identify and mitigate bias in algorithms and models will become standard practice, ensuring fair and equitable outcomes.

Our Data Analytics courses have been meticulously crafted to equip you with the necessary skills and knowledge to thrive in this swiftly expanding industry. Our instructors will lead you through immersive, hands-on projects, real-world simulations, and illuminating case studies, ensuring you gain the practical expertise necessary for success. Through our courses, you will acquire the ability to dissect data, craft enlightening reports, and make data-driven choices that have the potential to steer businesses toward prosperity.

Having addressed the question of what is data analysis, if you're considering a career in data analytics, it's advisable to begin by researching the prerequisites for becoming a data analyst. You may also want to explore the Post Graduate Program in Data Analytics offered in collaboration with Purdue University. This program offers a practical learning experience through real-world case studies and projects aligned with industry needs. It provides comprehensive exposure to the essential technologies and skills currently employed in the field of data analytics.

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1. What is the difference between data analysis and data science? 

Data analysis primarily involves extracting meaningful insights from existing data using statistical techniques and visualization tools. Whereas, data science encompasses a broader spectrum, incorporating data analysis as a subset while involving machine learning, deep learning, and predictive modeling to build data-driven solutions and algorithms.

2. What are the common mistakes to avoid in data analysis?

Common mistakes to avoid in data analysis include neglecting data quality issues, failing to define clear objectives, overcomplicating visualizations, not considering algorithmic biases, and disregarding the importance of proper data preprocessing and cleaning. Additionally, avoiding making unwarranted assumptions and misinterpreting correlation as causation in your analysis is crucial.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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What is Market Research Analysis? Definition, Steps, Benefits, and Best Practices

By Nick Jain

Published on: September 8, 2023

Table of Contents

What is Market Research Analysis?

Market research analysis steps, market research analysis benefits, 15 market research analysis best practices.

Market research analysis is defined as the systematic process of collecting, processing, interpreting, and evaluating data related to a specific market, industry, or business environment. Its primary purpose is to gain insights into various aspects of the market, including consumer behavior, market trends, competitive landscape, and other relevant factors. Market research analysis aims to provide businesses with actionable information that can inform their decision-making processes and strategies.

Here are the key components and objectives of market research analysis:

• Data Collection: The process begins with gathering data from a variety of sources. This data can be classified into two main categories:

Primary Data: Data collected directly from original sources, such as surveys, interviews, focus groups , observations, and experiments.

Secondary Data: Existing data collected by third parties, such as market reports, government publications, industry publications, and academic studies.

• Data Processing: Once collected, the data is processed to ensure its accuracy and reliability. This step involves cleaning the data to remove errors or inconsistencies and structuring it in a way that is suitable for analysis. Data processing may also involve data coding, categorization, and transformation.
• Data Analysis: The heart of market research analysis involves examining and interpreting the data to extract meaningful insights. Various analytical techniques and statistical tools are used to identify patterns, relationships, trends, and correlations within the data. This analysis supports businesses in making knowledgeable decisions.
• Competitive Analysis: Assessing the competitive landscape is an essential aspect of market research analysis. This includes studying competitors’ strengths, weaknesses, strategies, market share, and customer perceptions. Understanding the competitive environment is crucial for shaping a company’s strategy and positioning in the market.
• Consumer Behavior Analysis: Understanding how consumers think, feel, and act is a central objective of market research analysis. It involves identifying consumer preferences, purchasing habits, motivations, and pain points. This information helps businesses tailor their products, services, and marketing efforts to meet customer needs effectively.
• Market Trends Identification: Market research analysis helps businesses stay updated on the latest market trends, industry developments, and emerging technologies. Recognizing these trends allows companies to adapt, innovate, and remain competitive in their respective markets.
• Strategic Decision-Making: Ultimately, the goal of market research analysis is to provide actionable insights that inform strategic decision-making. These decisions can relate to product development, pricing strategies, marketing campaigns, market entry or expansion, and more.
• Risk Mitigation: By understanding market dynamics and potential challenges, businesses can proactively identify and mitigate risks. This reduces the likelihood of unexpected setbacks and allows for more effective crisis management.

Market research analysis is a vital tool that helps businesses gather and interpret data to make informed decisions, mitigate risks, identify opportunities for growth, and stay competitive in their respective markets. It plays a pivotal role in shaping business strategies and ensuring that resources are allocated effectively to achieve business objectives.

Market research analysis involves a series of systematic steps to gather, process, and interpret data to gain insights into a specific market or industry. These steps are crucial for making informed business decisions and developing effective strategies. Here are the key steps in the market research analysis process:

Step 1: Define Research Objectives

Precisely outline the goals and objectives of your market research . What specific insights or data are you aiming to acquire? What are your research questions? Understanding your objectives is essential for guiding the entire process.

Step 2: Data Collection

Collect relevant data from various sources. This can include primary data (directly collected from surveys, interviews, focus groups , observations, etc.) and secondary data (existing data from reports, publications, databases, etc.). Make certain that your data-gathering approaches are in harmony with your research objectives.

Step 3: Data Processing and Cleaning

Clean and preprocess the collected data to ensure its accuracy and reliability. This step may involve removing duplicate records, correcting errors, and organizing the data for analysis.

Step 4: Data Analysis

Perform data analysis using appropriate techniques and tools. Common analytical methods include statistical analysis, regression analysis, trend analysis, customer segmentation, and sentiment analysis. The objective is to derive significant insights from the data.

Step 5: Competitive Analysis

Assess the competitive landscape by studying your competitors. Analyze their strengths, weaknesses, market share, strategies, and customer perceptions. Recognize potential opportunities and vulnerabilities within the competitive landscape.

Step 6: Consumer Behavior Analysis

Examine consumer behavior by analyzing data related to preferences, purchasing habits, motivations, and demographics. Gain insights into what drives consumer decisions and how they interact with your products or services.

Step 7: Market Trends Identification

Identify and analyze current market trends, industry developments, and emerging technologies. Stay up-to-date with changes in the market that could impact your business.

Step 8: Data Interpretation

Interpret the outcomes of your data analysis within the framework of your research goals. What do the findings mean for your business? Are there actionable insights that can inform your decisions?

Step 9: Report and Presentation

Create a comprehensive report or presentation that summarizes your research findings. Use clear visuals, charts, and graphs to convey the information effectively. Include recommendations and insights that can guide decision-making.

Step 10: Strategic Decision-Making

Use the insights gained from your market research analysis to make informed strategic decisions. These decisions can relate to product development, pricing strategies, marketing campaigns, market entry or expansion, and more.

Step 11: Implementation

Put your strategic decisions into action. Implement the changes and strategies based on your market research analysis. Continuously track progress and adapt your approach as necessary.

Step 12: Continuous Monitoring

Market research analysis is an ongoing process. Continuously monitor market conditions, consumer behavior, and competitive developments to stay adaptable and responsive to changes in the market.

By following these steps, businesses can harness the power of market research analysis to make informed decisions, gain a competitive edge, and drive growth and innovation in their respective industries.

Learn more: What is Research Design?

Market research analysis offers numerous benefits to businesses and organizations across various industries. These benefits are instrumental in making informed decisions, shaping strategies, and ultimately achieving business objectives. Here are some of the key advantages of conducting market research analysis:

• Informed Decision-Making: Market research analysis provides valuable insights and data-driven information that support informed decision-making. By understanding market dynamics, consumer behavior, and trends, businesses can make strategic choices that are more likely to lead to success.
• Risk Mitigation: Through market research , organizations can identify potential risks and challenges in advance. This proactive approach allows them to develop strategies for risk mitigation and crisis management, reducing the impact of unforeseen events.
• Market Understanding: Market research analysis helps companies gain a deeper understanding of their target audience, including demographics, preferences, and purchasing behavior. This knowledge is critical for tailoring products, services, and marketing efforts to meet customer needs effectively.
• Competitive Advantage: By analyzing the competitive landscape, businesses can identify their competitors’ strengths and weaknesses. This information enables them to develop strategies that capitalize on their strengths and exploit competitors’ weaknesses, leading to a competitive advantage.
• Product Development: Market research analysis guides product development by uncovering consumer preferences, pain points, and unmet needs. This ensures that companies create products that resonate with their target market, increasing the likelihood of success in the market.
• Effective Marketing Strategies: Understanding consumer behavior and preferences helps in crafting more effective marketing campaigns. Market research analysis can identify the most suitable marketing channels, messaging, and timing to reach and engage the target audience.
• Optimized Pricing Strategies: Businesses can determine the optimal pricing strategies for their products or services through market research analysis. This includes assessing price sensitivity, competitive pricing, and value perception among customers.
• Market Expansion and Diversification: Market research analysis can reveal new market opportunities and potential areas for diversification. Companies can use this information to expand their reach into new markets or introduce new product lines.
• Improved Customer Satisfaction: By aligning products and services with customer preferences, companies can enhance customer satisfaction and loyalty. Contented customers are increasingly inclined to become returning purchasers and enthusiastic brand supporters.
• Cost Efficiency: Market research analysis can help companies allocate resources more efficiently by focusing on strategies and initiatives that are most likely to yield positive results. This reduces wasteful spending on ineffective activities.
• Measurable Results: Market research provides a basis for measuring the success of strategies and initiatives. It allows companies to set benchmarks, track progress, and assess the return on investment (ROI) of various marketing and business efforts.
• Innovation and Adaptation: Market research analysis keeps businesses up-to-date with market trends and emerging technologies. This knowledge encourages innovation and the ability to adapt to changing market conditions.
• Enhanced Reputation: Companies that demonstrate a commitment to understanding their market and meeting customer needs often enjoy an enhanced reputation in the eyes of consumers, partners, and investors.

Market research analysis is a valuable tool that empowers businesses to make data-driven decisions, minimize risks, gain a competitive edge, and achieve sustainable growth. It is an investment that can yield substantial returns by helping organizations align their strategies and resources with market realities and customer expectations.

Learn more: What is Primary Market Research?

Effective market research analysis is crucial for businesses to make informed decisions and stay competitive in their respective industries. To ensure that your market research analysis yields valuable insights, consider these best practices:

1. Clearly Define Objectives

Begin by clearly defining the objectives of your market research analysis. What particular inquiries do you aim to address? What are your goals and desired outcomes? Having a well-defined purpose will guide your research efforts.

2. Use a Mix of Data Sources

Combine both primary and secondary data sources. Primary data is collected directly from your target audience, while secondary data comes from existing sources. Using a mix of data sources enhances the comprehensiveness of your analysis.

3. Ensure Data Quality

Data quality is paramount. Take steps to ensure the data you collect is accurate, relevant, and reliable. Verify the credibility of your sources and implement data-cleaning processes to remove errors and inconsistencies.

4. Segment Your Audience

Segment your target audience into distinct groups based on demographics, behaviors, or other relevant criteria. This allows for more tailored insights and strategies.

5. Use a Variety of Analysis Techniques

Employ a range of analysis techniques such as quantitative and qualitative methods . Quantitative analysis involves numerical data, while qualitative analysis explores insights from open-ended questions and interviews. This all-encompassing strategy offers a more complete perspective.

6. Stay Objective and Unbiased

Avoid bias in your research by maintaining objectivity. Be aware of any preconceived notions or assumptions that might influence your analysis. Use unbiased language and interpretation of results.

7. Thoroughly Understand Your Market

Before conducting research , gain a deep understanding of the market and industry you’re investigating. This background knowledge will help you ask the right questions and interpret findings effectively.

8. Invest in Technology and Tools

Utilize advanced tools and software for data analysis. These tools can streamline the process, handle large datasets, and provide more robust insights. Consider investing in data visualization tools to present findings effectively.

9. Continuous Learning and Adaptation

Keep yourself informed about the most current research methodologies and industry developments. Market conditions evolve, so it’s essential to adapt your research methods accordingly.

10. Ethical Considerations

Adhere to ethical standards in data collection and analysis. Respect privacy and confidentiality, obtain informed consent when necessary, and ensure compliance with data protection regulations.

11. Regularly Communicate Findings

Share research findings with relevant stakeholders within your organization. Effective communication ensures that insights are used to inform decision-making and strategy development.

12. Iterative Process

Market research analysis should be an iterative process. As you implement strategies based on your findings, continue to monitor and analyze the market to stay responsive to changes.

13. Benchmark and Measure Progress

Set benchmarks and key performance indicators (KPIs) to measure the success of your strategies. Regularly assess whether you are meeting your objectives and adjust your approach as needed.

14. Seek External Expertise

Consider consulting with external experts or hiring market research professionals when needed. Their expertise can enhance the quality and reliability of your analysis.

15. Document Your Process

Maintain thorough documentation of your research process , including data sources, methodologies, and assumptions. This documentation is valuable for transparency and future reference.

By following these best practices, businesses can conduct market research analysis that provides actionable insights, informs decision-making, and contributes to long-term success in a competitive market.

Learn more: What is Qualitative Market Research?

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Meta Analysis: definition, meaning and steps to conduct

Meta-analysis: This article explains the concept of meta-analysis in a practical way. The article begins with an introduction to this concept, followed by a definition and a general explanation. You will also find a practical example and tips for conducting a simple analysis yourself. Enjoy reading!

What is a meta-analysis?

Have you ever wondered how doctors and researchers often make the right decisions about complex (medical) treatments? A powerful tool they use is the so-called meta-analysis. With this approach, they combine the results of multiple scientific studies to get a clearer picture of the overall effectiveness of a treatment.

Definition and meaning

But what exactly is meta-analysis? It’s a research process that systematically brings together the findings of individual studies and uses statistical methods to calculate an overall or ‘absolute’ effect.

It’s not just about merging data from smaller studies to increase sample size. Analysts also use systematic methods to account for differences in research approaches, treatment outcomes, and sample sizes.

For example, they also test the sensitivity and validity of their results for their own research protocols and statistical analyses.

Admittedly, that sounds difficult. It can also be described as putting puzzle pieces together to see the bigger picture. According to experts, scientists are often confronted with valuable but sometimes contradictory results in individual studies.

Meta-analyses play an important role in putting these puzzle pieces together and combining the findings of multiple studies to provide a more complete understanding.

Due to the combination of several scientific studies, it is considered the most comprehensive form of scientific research. This creates more confidence in the conclusions drawn, as a larger body of research is considered.

A practical example

Imagine this: there are several studies examining the same medical treatment, and each study reports slightly different results due to some degree of error.

Meta-analysis helps the researcher by combining these results to get closer to the truth.

By using statistical approaches, an estimated mean can be derived that reflects the common effect observed in the studies.

Steps in conducting a meta-analysis

Meta-analyses are usually preceded by a systematic review, as this helps identify and assess all relevant facts. It is an extremely precise and complex process, which is almost exclusively performed in a scientific research setting.

The general steps are as follows:

• Formulating the research question , for example by using the PICO model
• Searching for literature
• Selection of studies based on certain criteria
• Selection of specific studies on a well-defined topic
• Deciding whether to include unpublished studies to avoid publication bias
• Determining which dependent variables or summary measures are allowed
• Selection of the right model, for example a fixed-effect or random-effect meta-analysis
• Investigating sources of heterogeneity between studies, for example by meta-regression or by subgroup analysis
• Following formal guidelines for conducting and reporting the analysis as described in the Cochrane Handbook
• Use of Reporting Guidelines

By following these steps, meta-analyses can be performed to obtain reliable summaries and conclusions from a wide range of research data.

Meta-analyses have very valuable advantages.

First, it provides an estimate of the unknown effect size, which helps us understand how effective a treatment really is.

It also allows us to compare and contrast results from different studies. It helps identify patterns between the findings, uncover sources of disagreement, and uncover interesting connections that may emerge when multiple studies are analyzed together.

However, like any research method, meta-analysis also has its limitations. A concern is possible bias in individual studies due to questionable research practices or publication bias.

If such biases are present, the overall treatment effect calculated via this type of analysis may not reflect the true efficacy of a treatment.

Another challenge lies in dealing with heterogeneous studies.

Each study can have its own unique characteristics and produce different results. When we average these differences in a meta-analysis, the result may not accurately represent a specific group studied.

It’s like averaging the weight of apples and oranges – the result may not accurately represent both the apples and the oranges.

This means that researchers must make careful choices during the analysis process, such as how to search for studies, which studies to select based on specific criteria, how to handle incomplete data, analyze the data, and take publication bias into account.

Despite these challenges, meta-analysis remains a valuable tool in evidence-based research.

It is often an essential part of systematic reviews, where multiple studies are extensively analyzed. By combining evidence from different sources, it provides a more comprehensive insight into the effectiveness of medical treatments, for example.

Meta-analysis in psychology

Meta-analysis plays an important role in various fields, including psychology. It provides value primarily through its ability to bring together results from different studies.

Imagine there are many little puzzle pieces of information scattered across different studies. Meta-analysis helps us put all those pieces together and get a complete picture.

It helps psychologists discover patterns and trends and draw more reliable conclusions about certain topics, such as the effectiveness of a treatment or the relationship between certain factors.

Now it’s your turn

What do you think? Do you recognize the explanation of meta-analysis? Have you ever heard of this research method? Have you ever performed this analysis yourself? What do you think are the benefits its use? How would you explain its importance to someone who has no experience with research methods? What tips or comments can you share with us?

Share your experience and knowledge in the comments box below.

More information

• Guzzo, R. A., Jackson, S. E., & Katzell, R. A. (1987). Meta-analysis. Research in organizational behavior, 9(1), 407-442.
• Becker, B. J. (2000). Multivariate meta-analysis. Handbook of applied multivariate statistics and mathematical modeling, 499-525.
• Haidich, A. B. (2010). Meta-analysis in medical research. Hippokratia, 14(Suppl 1), 29.
• Field, A. P., & Gillett, R. (2010). How to do a meta‐analysis. British Journal of Mathematical and Statistical Psychology, 63(3), 665-694.

How to cite this article: Janse, B. (2024). Meta Analysis . Retrieved [insert date] from Toolshero: https://www.toolshero.com/research/meta-analysis/

Original publication date: 06/27/2024 | Last update: 06/27/2024

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

Table of Contents

Narrative Analysis

Definition:

Narrative analysis is a qualitative research methodology that involves examining and interpreting the stories or narratives people tell in order to gain insights into the meanings, experiences, and perspectives that underlie them. Narrative analysis can be applied to various forms of communication, including written texts, oral interviews, and visual media.

In narrative analysis, researchers typically examine the structure, content, and context of the narratives they are studying, paying close attention to the language, themes, and symbols used by the storytellers. They may also look for patterns or recurring motifs within the narratives, and consider the cultural and social contexts in which they are situated.

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

This type of narrative analysis involves examining the content of a narrative in order to identify themes, motifs, and other patterns. Researchers may use coding schemes to identify specific themes or categories within the text, and then analyze how they are related to each other and to the overall narrative. Content analysis can be used to study various forms of communication, including written texts, oral interviews, and visual media.

Structural Analysis

This type of narrative analysis focuses on the formal structure of a narrative, including its plot, character development, and use of literary devices. Researchers may analyze the narrative arc, the relationship between the protagonist and antagonist, or the use of symbolism and metaphor. Structural analysis can be useful for understanding how a narrative is constructed and how it affects the reader or audience.

Discourse Analysis

This type of narrative analysis focuses on the language and discourse used in a narrative, including the social and cultural context in which it is situated. Researchers may analyze the use of specific words or phrases, the tone and style of the narrative, or the ways in which social and cultural norms are reflected in the narrative. Discourse analysis can be useful for understanding how narratives are influenced by larger social and cultural structures.

Phenomenological Analysis

This type of narrative analysis focuses on the subjective experience of the narrator, and how they interpret and make sense of their experiences. Researchers may analyze the language used to describe experiences, the emotions expressed in the narrative, or the ways in which the narrator constructs meaning from their experiences. Phenomenological analysis can be useful for understanding how people make sense of their own lives and experiences.

Critical Analysis

This type of narrative analysis involves examining the political, social, and ideological implications of a narrative, and questioning its underlying assumptions and values. Researchers may analyze the ways in which a narrative reflects or reinforces dominant power structures, or how it challenges or subverts those structures. Critical analysis can be useful for understanding the role that narratives play in shaping social and cultural norms.

Autoethnography

This type of narrative analysis involves using personal narratives to explore cultural experiences and identity formation. Researchers may use their own personal narratives to explore issues such as race, gender, or sexuality, and to understand how larger social and cultural structures shape individual experiences. Autoethnography can be useful for understanding how individuals negotiate and navigate complex cultural identities.

Thematic Analysis

This method involves identifying themes or patterns that emerge from the data, and then interpreting these themes in relation to the research question. Researchers may use a deductive approach, where they start with a pre-existing theoretical framework, or an inductive approach, where themes are generated from the data itself.

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

• Identify the research question: Narrative analysis begins with identifying the research question or topic of interest. Researchers may want to explore a particular social or cultural phenomenon, or gain a deeper understanding of a particular individual’s experience.
• Collect the narratives: Researchers then collect the narratives or stories that they will analyze. This can involve collecting written texts, conducting interviews, or analyzing visual media.
• Transcribe and code the narratives: Once the narratives have been collected, they are transcribed into a written format, and then coded in order to identify themes, motifs, or other patterns. Researchers may use a coding scheme that has been developed specifically for the study, or they may use an existing coding scheme.
• Analyze the narratives: Researchers then analyze the narratives, focusing on the themes, motifs, and other patterns that have emerged from the coding process. They may also analyze the formal structure of the narratives, the language used, and the social and cultural context in which they are situated.
• Interpret the findings: Finally, researchers interpret the findings of the narrative analysis, and draw conclusions about the meanings, experiences, and perspectives that underlie the narratives. They may use the findings to develop theories, make recommendations, or inform further research.

Applications of Narrative Analysis

Narrative analysis is a versatile qualitative research method that has applications across a wide range of fields, including psychology, sociology, anthropology, literature, and history. Here are some examples of how narrative analysis can be used:

• Understanding individuals’ experiences: Narrative analysis can be used to gain a deeper understanding of individuals’ experiences, including their thoughts, feelings, and perspectives. For example, psychologists might use narrative analysis to explore the stories that individuals tell about their experiences with mental illness.
• Exploring cultural and social phenomena: Narrative analysis can also be used to explore cultural and social phenomena, such as gender, race, and identity. Sociologists might use narrative analysis to examine how individuals understand and experience their gender identity.
• Analyzing historical events: Narrative analysis can be used to analyze historical events, including those that have been recorded in literary texts or personal accounts. Historians might use narrative analysis to explore the stories of survivors of historical traumas, such as war or genocide.
• Examining media representations: Narrative analysis can be used to examine media representations of social and cultural phenomena, such as news stories, films, or television shows. Communication scholars might use narrative analysis to examine how news media represent different social groups.
• Developing interventions: Narrative analysis can be used to develop interventions to address social and cultural problems. For example, social workers might use narrative analysis to understand the experiences of individuals who have experienced domestic violence, and then use that knowledge to develop more effective interventions.

Examples of Narrative Analysis

Here are some examples of how narrative analysis has been used in research:

• Personal narratives of illness: Researchers have used narrative analysis to examine the personal narratives of individuals living with chronic illness, to understand how they make sense of their experiences and construct their identities.
• Oral histories: Historians have used narrative analysis to analyze oral histories to gain insights into individuals’ experiences of historical events and social movements.
• Children’s stories: Researchers have used narrative analysis to analyze children’s stories to understand how they understand and make sense of the world around them.
• Personal diaries : Researchers have used narrative analysis to examine personal diaries to gain insights into individuals’ experiences of significant life events, such as the loss of a loved one or the transition to adulthood.
• Memoirs : Researchers have used narrative analysis to analyze memoirs to understand how individuals construct their life stories and make sense of their experiences.
• Life histories : Researchers have used narrative analysis to examine life histories to gain insights into individuals’ experiences of migration, displacement, or social exclusion.

Purpose of Narrative Analysis

The purpose of narrative analysis is to gain a deeper understanding of the stories that individuals tell about their experiences, identities, and beliefs. By analyzing the structure, content, and context of these stories, researchers can uncover patterns and themes that shed light on the ways in which individuals make sense of their lives and the world around them.

The primary purpose of narrative analysis is to explore the meanings that individuals attach to their experiences. This involves examining the different elements of a story, such as the plot, characters, setting, and themes, to identify the underlying values, beliefs, and attitudes that shape the story. By analyzing these elements, researchers can gain insights into the ways in which individuals construct their identities, understand their relationships with others, and make sense of the world.

Narrative analysis can also be used to identify patterns and themes across multiple stories. This involves comparing and contrasting the stories of different individuals or groups to identify commonalities and differences. By analyzing these patterns and themes, researchers can gain insights into broader cultural and social phenomena, such as gender, race, and identity.

In addition, narrative analysis can be used to develop interventions that address social and cultural problems. By understanding the stories that individuals tell about their experiences, researchers can develop interventions that are tailored to the unique needs of different individuals and groups.

Overall, the purpose of narrative analysis is to provide a rich, nuanced understanding of the ways in which individuals construct meaning and make sense of their lives. By analyzing the stories that individuals tell, researchers can gain insights into the complex and multifaceted nature of human experience.

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

• Studying life stories: Narrative analysis can be useful in understanding how individuals construct their life stories, including the events, characters, and themes that are important to them.
• Analyzing cultural narratives: Narrative analysis can be used to analyze cultural narratives, such as myths, legends, and folktales, to understand their meanings and functions.
• Exploring organizational narratives: Narrative analysis can be helpful in examining the stories that organizations tell about themselves, their histories, and their values, to understand how they shape the culture and practices of the organization.
• Investigating media narratives: Narrative analysis can be used to analyze media narratives, such as news stories, films, and TV shows, to understand how they construct meaning and influence public perceptions.
• Examining policy narratives: Narrative analysis can be helpful in examining policy narratives, such as political speeches and policy documents, to understand how they construct ideas and justify policy decisions.

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

• Focus on stories and narratives: Narrative analysis is concerned with analyzing the stories and narratives that people tell, whether they are oral or written, to understand how they shape and reflect individuals’ experiences and identities.
• Emphasis on context: Narrative analysis seeks to understand the context in which the narratives are produced and the social and cultural factors that shape them.
• Interpretive approach: Narrative analysis is an interpretive approach that seeks to identify patterns and themes in the stories and narratives and to understand the meaning that individuals and communities attach to them.
• Iterative process: Narrative analysis involves an iterative process of analysis, in which the researcher continually refines their understanding of the narratives as they examine more data.
• Attention to language and form : Narrative analysis pays close attention to the language and form of the narratives, including the use of metaphor, imagery, and narrative structure, to understand the meaning that individuals and communities attach to them.
• Reflexivity : Narrative analysis requires the researcher to reflect on their own assumptions and biases and to consider how their own positionality may shape their interpretation of the narratives.
• Qualitative approach: Narrative analysis is typically a qualitative research method that involves in-depth analysis of a small number of cases rather than large-scale quantitative studies.

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

• Rich and detailed data : Narrative analysis provides rich and detailed data that allows for a deep understanding of individuals’ experiences, emotions, and identities.
• Humanizing approach: Narrative analysis allows individuals to tell their own stories and express their own perspectives, which can help to humanize research and give voice to marginalized communities.
• Holistic understanding: Narrative analysis allows researchers to understand individuals’ experiences in their entirety, including the social, cultural, and historical contexts in which they occur.
• Flexibility : Narrative analysis is a flexible research method that can be applied to a wide range of contexts and research questions.
• Interpretive insights: Narrative analysis provides interpretive insights into the meanings that individuals attach to their experiences and the ways in which they construct their identities.
• Appropriate for sensitive topics: Narrative analysis can be particularly useful in researching sensitive topics, such as trauma or mental health, as it allows individuals to express their experiences in their own words and on their own terms.
• Can lead to policy implications: Narrative analysis can provide insights that can inform policy decisions and interventions, particularly in areas such as health, education, and social policy.

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

• Subjectivity : Narrative analysis relies on the interpretation of researchers, which can be influenced by their own biases and assumptions.
• Limited generalizability: Narrative analysis typically involves in-depth analysis of a small number of cases, which limits its generalizability to broader populations.
• Ethical considerations: The process of eliciting and analyzing narratives can raise ethical concerns, particularly when sensitive topics such as trauma or abuse are involved.
• Limited control over data collection: Narrative analysis often relies on data that is already available, such as interviews, oral histories, or written texts, which can limit the control that researchers have over the quality and completeness of the data.
• Time-consuming: Narrative analysis can be a time-consuming research method, particularly when analyzing large amounts of data.
• Interpretation challenges: Narrative analysis requires researchers to make complex interpretations of data, which can be challenging and time-consuming.
• Limited statistical analysis: Narrative analysis is typically a qualitative research method that does not lend itself well to statistical analysis.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 26 June 2024

Health care workers’ self-perceived meaning of residential care work

• Sui Yu Yau 1 ,
• Yin King Linda Lee 1 ,
• Siu Yin Becky Li 1 ,
• Sin Ping Susan Law 1 ,
• Sze Ki Veronica Lai 1 &
• Shixin Huang 2  

BMC Health Services Research volume  24 , Article number:  766 ( 2024 ) Cite this article

137 Accesses

Metrics details

Attracting and supporting a sustainable long-term care (LTC) workforce has been a persistent social policy challenge across the globe. To better attract and retain a sustainable LTC workforce, it is necessary to adopt a unified concept of worker well-being. Meaning of work is an important psychological resource that buffers the negative impacts of adverse working conditions on workers’ motivation, satisfaction, and turnover intention. The aim of this study was to explore the positive meaning of care work with older people and its implications for health care workers’ job satisfaction and motivation to work in the LTC sector.

This study adopted a qualitative descriptive design that pays particular attention to health care workers; such as nurses, personal care workers; as active agents of the meaning making and reframing of care work in LTC communities in a East Asia city. In-depth semi-structured interviews were conducted with thirty health care workers in LTC communities in Hong Kong. Thematic analysis was employed for data analysis.

The research findings indicate that while health care workers perform demanding care work and experience external constraints, they actively construct positive meanings of care work with older people as a helping career that enables them to facilitate the comfortable aging of older people, build affectional relationships, achieve professional identity, and gain job security.

Conclusions

This qualitative study explores how health care workers negotiate the positive meaning of older people care work and the implications of meaningful work for workers’ job satisfaction and motivation to work in the LTC sector. The importance of a culturally sensitive perspective in researching and developing social policy intervention are suggested.

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Introduction

Recruiting and retaining health care workers (HCWs) in the long-term care (LTC) sector is a persistent worldwide social policy challenge [ 1 ]. Across the globe, population aging will create significantly higher demands for LTC services for older people. These demands include residential care services, especially among older people with complex care needs due to age-related disabilities and chronic diseases [ 2 ]. Comprised mainly of nurses and personal care workers, HCWs in LTC communities perform a variety of tasks that are essential to maintain the functional ability of older people, including helping with activities of daily living (ADL) (such as bathing, toileting and eating), instrumental activities of daily living (IADL) (such as taking medication), monitoring and coordinating care, and communicating with older people and their families [ 3 ]. Despite the growing demand and significance of LTC services, health care work in LTC communities is often devalued as “dirty work” and characterized by low wages, precarious working conditions, limited career development opportunities, understaffing, and work overload [ 4 ].

In the context of LTC communities, while the research to date has extensively evaluated the demanding working conditions that lead to negative well-being outcomes for HCWs [ 1 ], relatively little is known about the positive meaning that HCWs experience in, and attribute to, their care work in LTC communities [ 5 ]. Further exploration of how HCWs engage in meaningful work is helpful to the development of strategies that improve worker well-being and other work outcomes in LTC communities, especially job satisfaction and worker retention. In addition, cultural and social contexts exert a heavy influence on the meaning of care [ 6 ]. Most of the current literature on older people care work has been produced and addressed in Anglo-American contexts; there are limited evaluations of the meanings and experiences of older people care work from the perspectives of HCWs in East Asia, a region that is characterized by a large, rapidly aging population and unique socio-cultural meanings of older people care. A culturally sensitive understanding of what contributes to meaningful work in the LTC setting is thus needed to attract and support the LTC workforce beyond the Western contexts. Thus, this qualitative study aims to examine how HCWs in LTC communities construct positive meanings of older people care and also the implications of meaningful work for their job satisfaction and intention to stay in the LTC sector in Hong Kong, in the People’s Republic of China. This study is produced as part of a larger research project examining the social construction of stigma attached to older people care work in Hong Kong’s LTC communities [ 7 ] and pays particular attention to HCWs’ meaning construction in relation to the policy, organizational, and socio-cultural contexts to inform LTC workforce development policy.

Constructing meaning of work in LTC communities

Meaning of work (MOW) is an important psychological resource that buffers the negative impacts of adverse working conditions on workers’ motivation, satisfaction, and turnover intention [ 8 , 9 ]. Across different occupational contexts, organizational scholars have consistently found that MOW is a significant aspect of workers’ subjective well-being and is associated with positive worker and organizational outcomes, including higher work engagement, organizational commitment, worker retention, and productivity [ 10 ]. MOW refers to “employees’ understandings of what they do at work as well as the significance of what they do” [ 11 ]. It captures how employees make sense of their experiences at work, as well as the role of work in the context of life [ 12 ]. MOW consists of three primary facets: positive meaning in work, meaning making through work, and greater good motivation [ 13 ]. Meaning in work concerns individuals’ subjective interpretations of experiences and interactions at work in terms of the values, attitudes, and beliefs that they see as intrinsic to the nature of their work and working relationships [ 10 ]. Meaning making through work involves the idea that work could serve as a critical avenue for meaning making in life, such as facilitating personal growth, deepening self-understanding, and attaining personal and professional identity [ 14 ]. Lastly, greater good motivation implies the perception that one’s work has positive impacts on the greater good, ranging from generating positive contributions to others to responding to the meaning of work [ 15 ].

Although MOW is experienced by individual employees as feelings and cognitions, a sociological perspective of MOW suggests that the meaning individuals ascribe to their work is constructed within an array of socially influenced worldviews regarding the value of their work activities [ 16 ]. Individuals’ meaning making of their jobs, roles, and selves at work is a dynamic process that is influenced by the social and interpersonal valuation and devaluation of their work [ 11 ]. Work in the LTC sector is often socially constructed as “dirty work” that is physically, socially, and morally tainted [ 17 , 18 ]. The social discourses on “dirty work” are further reinforced by the emotionally and physically demanding nature of care work, as well as the poor job quality in the LTC sector [ 19 ]. Work in LTC communities is typically characterized by poor compensation, heavy workloads, precarious part-time employment, limited career development prospects, limited training and supervision, and low occupational status compared to other healthcare fields [ 20 ].

Given these external constraints, it is not surprising that HCWs in LTC communities feel disempowered to make positive sense of their care work [ 21 ], which in turn negatively influences their job satisfaction and intention to work in the LTC sector [ 17 ]. Despite the social devaluation and demanding nature of older people care work, HCWs in LTC communities could actively engage in negotiating the meaning of their work and construct positive career identities to overcome the taint of dirty work, a research theme that to date remains underdeveloped [ 22 ]. These positive meanings might include forming caring relationships with older people [ 5 ].

Residential care services and LTC workforce in Hong Kong

Health care workers in LTC communities negotiate the meaning of care work within particular social policy, organizational, and socio-cultural contexts [ 7 ]. Given the drastically increasing demand for residential care among older people, the chronic workforce crisis in the LTC sector, and the transforming socio-cultural meaning of care for older people [ 4 ], it has never been timelier to explore the meaning of work in Hong Kong’s LTC communities.

Hong Kong is an economically advanced metropolis located in the Southern part of China. With increasing life expectancy, Hong Kong’s aging population is projected to increase from 1.12 million (or 15% of total population) in 2015 to 1.51 million (or 30.6% of total population) in 2043, significantly higher than the OECD (Organisation for Economic Co-operation and Development) average percentage (25% in 2043) [ 23 ]. As a result, the demand for LTC services, including residential care services, will also increase drastically. The limited residential spaces, the transformation of family structure, and the imbalanced public investment in community and residential care have turned the number of older people who require residential care in Hong Kong into one of the highest among developed economies [ 24 ].

Hong Kong adopts a hybrid model in the financing and provision of its residential care services. In 2022, there were about 76,200 older people require residential care in Hong Kong, among which 46% (or a total number of 35,040) were subsidized by the government and 54% (or a total number of 41,160) were non-subsidized [ 25 ]. While residential care services in general are provided by non-governmental organizations (NGOs) (31%) and the private sector (69%), the majority of subsidized residential services are provided by NGOs, although the government also purchases subsidized places and services from private facilities [ 26 ]. Like many developed economies, Hong Kong has experienced an acute shortage of HCWs in LTC communities [ 26 ]. Even though the Hong Kong government has initiated many measures over the past few years to tackle the issues of the care workforce crisis, such as increasing salaries, launching different schemes to train young people and encouraging migrant workers to join the LTC communities, 20% of HCW positions in LTC communities remain vacant [ 27 ].

HCWs’ well-being is indeed connected to workforce attraction and retention. Despite the Hong Kong government initiating various ongoing measures to increase the number of workforce in LTC sector, there will be a shortfall of 4,500 HCWs in the next three year [ 28 ]. To better attract and retain a sustainable LTC workforce, it is necessary to adopt a unified concept of worker well-being that not only addresses the structural factors, such as economic and physical working conditions, but also the subjective factors that attract and motivate workers to join and remain in the LTC sector, including promoting meaningful, valued work [ 29 , 30 ]. Caring for older people entails unique socio-cultural meanings in Hong Kong and East Asian societies. Although sociodemographic changes have transformed the patterns of social care for older people, most noticeably exemplified by the rising demand for residential care, such cultural norms still exert significant influences on the meaning of care work [ 31 ]. The aim of this study was to explore the positive meaning of care work with older people and its implications for health care workers’ job satisfaction and motivation to work in the LTC sector.

This study adopted a qualitative descriptive design that focuses on HCWs as active agents of the meaning making and reframing of care work in LTC communities. The use of qualitative descriptive design is common in health care research because of its simplicity and flexibility in diverse healthcare environment. Qualitative research is appropriate to explore experiences and perceptions on subjective nature of a phenomenon. It is especially suitable for nursing and healthcare studies that interested in individual’s experience [ 32 ]. Thus, this design is particularly relevant to this study which aimed to explore the positive meaning of care work with older people and its implications for health care workers’ job satisfaction and motivation to work in the LTC sector.

In the context of Hong Kong, HCWs in LTC communities include personal care workers (PCWs) who take care of residents’ ADL and IADL, health workers (HWs, largely equivalent to “certified nursing assistants” in the United States) who monitor the work of PCWs and are responsible for the delivery of basic nursing care, and enrolled nurses (ENs) and registered nurses (RNs) who provide nursing care and oversee the work of PCWs and HWs.

Recruitment sample

Purposive sampling was used to recruit HCWs from LTC communities as research participants. To meet the inclusion criteria, participants had to (1) be serving in the role of a PCW, HW, EN, or RN; (2) have at least 6 months of experience working in an LTC community; and (3) be providing frontline services to older people. The exclusion criteria were as follows: (1) LTC workers who had only a managerial role and did not provide frontline care; (2) LTC workers working in other roles (e.g., social workers, occupational therapists, physical therapists). In the process of participant recruitment, the maximum variation sampling method was used to ensure the heterogeneity of participants in terms of participants’ characteristics. The use of maximum variation sampling method aimed to recruit information-rich participants and to capture the widest range of possible perspectives [ 33 ]. Thus, in order to ensure maximum variation, this study recruited participants based on a variety of nature such as gender, age, role and rank, years of work experience, and types of LTC communities worked for including publicly subsidized and private communities.

Six LTC communities were approached by the researchers. The managerial staff of each LTC community was invited to refer potential participants to the researchers after briefed for the purpose of the study, as well as the inclusion and exclusion criteria of the sample. The researcher (S. Huang) liaised with the managerial staff to schedule the logistics. Participants were fully informed of the purpose and procedures of the study. Informed consents were obtained before data collection commenced. Pseudonyms were used in the study in order to protect participants’ identities.

Data were collected between February 2021 and December 2021. Thirty participants were recruited in the study. The average age of the participants was 37 years old, and their mean years of tenure in the care sector were 7 years. Reflecting the gender ratio of the overall population of the care workforce, 5 participants were male and 25 were female. Thirteen of the participants worked as nurses (five RNs and eight ENs), eight worked as HWs, and nine were PCWs. Sixteen participants had attained a post-secondary education and 13 had earned secondary education, with only one participant having received primary or below education (see Table 1 for demographic data of the participants) [see Additional file 1].

Data collection

Semi-structured in-depth interviews were conducted. Interviewers were trained in qualitative study methods and came from a variety of healthcare research backgrounds of nursing and social work. Interviews were conducted in private meeting rooms in LTC communities. Interview sessions lasted from 30 to 80 min (mean = 55 min). Cantonese was adopted in the interviews. An interview guide was developed for this study [see Additional file 2]. Each interview began with general questions revolving around the nature of the participant’s work and daily work routines, followed by exploratory questions that unraveled the meanings the participant made from her/his work. With the written informed consent of participants, all interviews were audio-recorded and transcribed verbatim.

Data analysis

Thematic analysis [ 34 ] was used to analyze the interview data. Adopting an inductive approach to analysis, this study followed the six-phase approach to thematic analysis that includes (1) data familiarization, (2) coding, (3) initial theme generation, (4) theme development and review, (5) refining, defining and naming themes, and (6) writing up [ 35 ]. Two experienced qualitative researchers (V. Lai and S. Huang) coded each interview transcript independently. Transcripts were coded with the facilitation of the qualitative research data analysis software NVivo 12. All the authors met regularly to review interview transcripts, compare coding, and generate initial analytical themes together. Disagreements regarding coding were raised and discussed in team meetings until agreements were reached. Two authors then finalized the processes by developing, reviewing, refining, defining, and naming themes.

The trustworthiness and rigor of the study was ensured by credibility, dependability, confirmability and transferability [ 36 ]. In order to enhance the credibility, two researchers read the transcripts and conduct coding independently for comparison. They discussed the emergent themes and codes until a consensus was researched. Dependability was achieved by using an audit trail that detailed the description of the research process to reduce bias. Peer debriefing with an expertise was used for confirmability. Transferability of findings was attained by describing the participant characteristics and the methodology of the study transparently and comprehensively in order to allow readers understood the strengths and limitations of the study.

Engaging in care for others can be highly rewarding work as reflected from the participants. Five themes identified from the data that articulated the positive meaning that HCWs ascribed to their work in LTC communities, including (1) “My work makes their lives more comfortable”: Helping older people to age comfortably; (2) “Everyday our affections increase”: Building meaningful relationships; (3) “These are all skills”: Forming a professional identity of older people care; (4) “I want to find a job that ensures I will never be unemployed”: Ensuring job security; and (5) “They are extra work”: Barriers to attaining the positive meaning of work.

“My work makes their lives more comfortable”: Helping older people to age comfortably

When making meaning of their work, the HCWs most frequently evoked the notion of helping older people to “age comfortably” in LTC communities. The idea of comfortable aging, as suggested by HCWs in this study, referred to both physical well-being (i.e., having desirable health outcomes and being free of pain) and psychosocial well-being. The physical and psychosocial well-being entailed the traditional socio-cultural values in Chinese society.

The HCWs suggested that their care activities supported older people’s comfortable aging by maintaining and even improving their physical health. The HCWs in LTC communities engaged in a variety of caregiving tasks in their everyday work. The daily work routine of the HWs, ENs, and RNs revolved around addressing the health needs of older residents through clinical and medical activities such as wound dressing, medication administration, peritoneal dialysis, tube feeding, etc. The care activities of the PCWs included personal care such as assisting with bathing, dressing, eating, toileting, transferring, grooming, etc., depending on the frailty level of the older residents. The HCWs suggested that they found their work meaningful because their care activities were helpful to older residents achieving desirable health outcomes.

I feel happy because my work makes their lives more comfortable. For example, a resident’s wound was quite severe and was at stage one or stage two before intervention. Then, we had multiple interventions and dressed the wound one shift after another until it finally healed. I gained a sense of fulfillment in the process. This process made me feel that our care was effective. (EN2)

As demonstrated by a participant, in the process of helping older people maintain their physical health, HCWs gain a strong sense of self efficacy and job satisfaction. Even though the HCWs pointed out that their care did not always lead to full recovery as many older people in LTC communities are physically frail and experiencing health deterioration, they deemed their work to be meaningful because it helped older people maintain the highest level of physical comfort possible.

Not everyone recovers. Some are not in a good condition, but at least my care helps to ensure they are not too bad. Even though they cannot recover fully, their wounds might get smaller or not deteriorate any more. They don’t feel so much pain… They can feel more comfortable. (HW3)

In addition, the HCWs suggested that their everyday care conveys companionship and psychological support to older people in LTC communities, which is also essential to their comfortable aging.

Actually, the meaning of taking care of them is about being part of their last journey of life. In other words, I can create a happy and comfortable later life for them before they pass away. There is someone who can talk with them and provide good care to them. For me, that is what nursing care is about. (EN4)

The idea of facilitating comfortable aging espoused by HCWs has socio-cultural relevance in Chinese society, where providing care to older people to enable their comfortable aging is seen as a moral virtue. Several HCWs, including those in younger ages, framed their care as rewarding and meaningful work as they believed that taking good care of older people would “accumulate good karma” for themselves and their family.

I quite like taking care of older people. It is like some sort of traditional thought… I think taking care of older people is accumulating good karma. I believe that this is beneficial to my family and myself. (EN1) I think it is accumulating good karma. When taking care of older people, I am thinking that if I take good care of them now, I will be treated well by others when I get old and need care from others in the future. I do my work with this mindset. Therefore, I do not see my work as hard or dirty. (HW4)

“Every day our affections increase”: Building meaningful relationships

The second theme that the HCWs ascribed to their work concerned the valuable long-term relationships they built with older residents in their daily work, through which they found joy and personal growth.

HCWs, especially the nurses, constantly drew comparisons between LTC communities and other health care settings, such as hospitals, when discussing the meaning of their work. They suggested that working in a LTC facility allowed them to form long-term, genuine bonds with the older people they cared for, something they argued was rarely possible elsewhere. According to a participant, residential homes allow “the cultivation of human relationships and affection that is absent in hospitals” (RN3). A participant further elaborated:

I like talking with people. Working in a hospital is like fighting a war. I had no time to know the backgrounds of my patients. I couldn’t even remember their names when they were discharged from the hospital. Then, I will never see them again… However, LTC communities are very different. The conditions of the older people we serve are more stable. I have more time to get along with them. (EN6)

The cultivation of relationship involves human interaction and emotional exchange as reflected from the participants. The HCWs believed that they were the ones who provided “close, personal care” to the residents. In the process of performing everyday care activities, they had frequent interactions and developed close relationships with older people. Many participants suggested that being able to communicate and interact with older people was the most enjoyable part of their work. Despite the challenges of caregiving work, participants found their relationships with older residents “joyful”, “satisfying”, and “rewarding”.

When I perform my work and provide care to them, I gain joy and fun out of it. I feel happy to interact with people. [The happiness] is very personal. It might be chatting with a resident and receiving an unexpected response. Some residents with dementia are very funny. They always come up with something unexpected and make me feel happy. (HW4) The sense of satisfaction comes from my interactions with older people. Every day, our affections increase. They treat me like their granddaughter. I think acknowledgement from the boss does not matter a lot; I feel the biggest sense of satisfaction by getting the acknowledgement of the older people. They personally experience how well I deliver care. (EN5)

Moreover, some HCWs reported that their relationships with older residents were “reciprocal”, not only because they constantly received appreciation from the residents but also, more importantly, because they were able to learn “old wisdom” and achieve personal growth from the lived experiences of the older residents.

It is not only about providing a service to them; sometimes when I talk with them, they offer me their perspectives, from which I can learn something. This is more like a reciprocal relationship…Sometimes, the older people have old wisdom and special perspectives. (RN4) I think I learn a lot from the older people because I meet a lot of people here and learn about their lived experiences from our conversations. They like sharing with me and I can reflect upon myself… (HW5)

“These are all skills”: Forming a professional identity of older people care

HCWs proposed that older people care is highly skillful and professional, requiring communication, coordination, and chronic illness care skills. Being able to form a professional identity as a HCW for the older people thus constituted a salient MOW for the participants.

Participants in this study reported that they constantly experienced devaluation of their work by their family, friends, and health care professional allies, who regarded care work in LTC communities as “dirty, less skilled, and unprofessional”.

People imagine that this work is about changing diapers and dealing with shit and piss… My aunt used to say to me that she’d rather beg than work in a residential home. People are not willing to join this sector because they think older people care is dirty work and cannot accept dealing with human excreta. (PCW4) They think that we work here because our nursing skills are not competent enough to work as hospital nurses. But when they hear that I am working in an LTC community, they doubt that my work is different from that in hospitals. They doubt that we work here because our nursing skills are not competent enough to work as hospital nurses. (EN6)

Contrary to the negative evaluations of their work, the HCWs evoked positive meanings of care work in LTC communities. One participant described that care in LTC communities and care in hospitals were “both part of the continuum of care that tackles the different health needs of older people, ranging from acute disease to long-term chronic illness” (RN5). More importantly, their care work in LTC communities allowed them to reimagine the nature of health care from delivering physical care tasks to providing holistic care that included psychological support, health education, human communication, resource coordination, and organizational management.

It is wrong to assume that nurses working with older people are not professional. Instead, we are differently professional in our specialties. For hospital nurses, their professional expertise lies in emergency treatment. But working in LTC is professional in terms of mastering the daily operation of a facility, governmental ordinances, and communication with family members. (RN2)

While the HCWs framed their work as valuable and professional, the HCWs described how performing personal care for older residents, such as positioning, lifting, transferring, feeding, and bathing, requires specialized knowledge, training, and experience.

Everything, every machine here requires specialized knowledge and training to handle. It is not that straightforward and simple. So, working as a PCW is not only about changing diapers. We need to grasp health and medical knowledge to monitor older peoples’ vital signs. We must also monitor whether the older people have bruises or wounds. We must be very careful to know whether the older people are doing ok. These are all skills. (PCW2)

Participants indicated that there were many other aspects that distinguished them as “professional” that further produced meanings and values in their personal life. One participant, a HW, indicated that working in LTC communities enabled her to work with interdisciplinary professionals such as doctors, nurses, nutritionists, social workers, physical therapists, and occupational therapists and thus allowed her to gain health knowledge. Many HCWs mentioned that the older people care knowledge and skills they learned from work could be useful in their personal life, particularly in terms of taking care of their older parents and grandparents at home.

“I want to find a job that ensures I will never be unemployed”: Ensuring job security

HCWs, especially PCW and HW working in government-subsidized facilities, perceived that the LTC sector offers relatively promising job opportunities and security, a stable income, and a career development pathway. These instrumental values made the LTC sector attractive for the participants.

Across the globe, the LTC sector has long been suffering from the challenge of workforce shortage. For participants in this study, however, this challenge was perceived as a positive opportunity that added value to their jobs. Many proposed that they found older people care as meaningful work because with the trend of population aging, there would always be increasing workforce demands in the job market which could provide them with promising job opportunities and security. Some HCWs also mentioned that the job offered them income stability, which they deemed as valuable compared to other work in the service industry.

The availability of job stability and opportunities in older people care work was particularly salient for participants during the COVID-19 pandemic, when the unemployment rate was high due to economic recession. Several participants described that they joined the LTC sector during the COVID-19 pandemic for the stability it offered. For example, a participant described, “I was working in the hotel industry…Then I lost my job and couldn’t find a new one. I wanted to find a job that will ensure I will never be unemployed.” (PCW5).

In addition, participants suggested that they found their work meaningful because of the relatively promising career development opportunities. The LTC sector in Hong Kong provides HCWs with a career pathway and ladder to pursue career development. Although promotion and degree admission opportunities are highly competitive, some participants saw the career ladder that moves up from PCW, HW, and EN to RN as a promising pathway for them to gain better income and work benefits.

“They are extra work”: Barriers to attaining meaning of work

Despite the HCWs ascribing a variety of positive meanings to their work, they admitted that it was not always possible to attain these meanings in their everyday work. They identified several barriers to attaining MOW, including the lack of organizational support for relational care, heavy workloads and workforce shortages, as well as emotional burnout.

As described above, HCWs found that the relational components of their work, particularly the helping relationships and affectional interactions with older residents, made the work highly meaningful. However, participants reported that although the LTC sector had long placed emphasis on person-centered care, they received little organizational support to develop meaningful relationships in their everyday work. Given that their daily work routines and timetables were predominantly organized around the delivery of physical caregiving tasks, many HCWs described an important and meaningful part of care work – relationship building and psychological support – as “extra” work that received little organizational recognition.

Of course, a lot of my work with the residents is extra work. I prefer to deliver holistic care that goes beyond physical care. Physical care tasks are those that appear on the timetable. But for the other parts, I must address them for the residents at other times by myself. (RN1)

Moreover, the heavy workloads and the chronic lack of workers in LTC communities impose further strains on HCWs in fulfilling their daily work routines, making it even more difficult for them to provide relational care. Despite these organizational constraints, the HCWs reported that they creatively made time and space in and between their work routines to build relationships and address older residents’ psychosocial well-being needs.

When I distribute medications, I usually have casual chats with the residents by greeting them and asking how their sleep and meals went. Just chatting. But it depends on the situation. When accidents happen, I would be too busy to handle this. (HW2) Sometimes I am very busy and do not have time to interact with the residents at all… I usually use meal times when I am more or less available. Residents are usually sitting and waiting for meals before we distribute them. I will use the ten minutes or so to chat with them. (HW1)

Relationship building and affectional interaction can be satisfying and exhausting simultaneously. The HCWs described the high emotional demands from older people and their family members they had to bear in their everyday work, which frequently put them in a situation of emotional burnout which can detract from building meaning. In addition, some HCWs reported that it took a lot of emotional labor (i.e. to manage feeling as to fulfill job requirement) to care for older residents with difficult behaviors or personalities, especially those with declining mental health and dementia. They said that they constantly experienced distrust, blaming, and rejection from older residents when they performed caregiving tasks such as feeding, which added a considerable amount of strain to their work. Similarly, the HCWs had to deal with constant distrust and misunderstanding from residents’ family members, which caused some of them frustration and stress.

This is work that cannot get understanding from everyone. Some [family members of the residents] would not notice my efforts to care for the residents. However, if I make a minor mistake, they will blame me. Human beings make mistakes and are not perfect. I am also sincerely concerned for the older people, but they don’t understand and blame me for my mistake. (PCW7)

This study examines HCWs’ engagement in meaningful work in LTC communities in the context of an economically developed Chinese society in Hong Kong. It is found that HCWs deemed their work to be a meaningful helping career that facilitated comfortable aging for older people and connoted positive socio-cultural values. They further attributed their MOW to the valuable relationships developed in their daily work and to the positive professional identity and relatively promising job security in their work, although the attainment of positive MOW was hindered by a number of barriers. In this discussion, we describe how these findings can support social policy initiatives to attract, retain, and support the LTC workforce.

To date, research and social policy interventions on LTC workforce development have largely focused on structural factors that influence the retention of HCWs and their job satisfaction [ 37 ]. Studies informed by this line of inquiry have identified the importance of working conditions, especially pay and compensation, workload and staffing level, teamwork, and supervision, in shaping work-related outcomes [ 29 , 38 , 39 ]. Even though the positive organizational scholarship has long argued the beneficial impacts of positive psychological states, including perceptions of meaningful work, on workforce functioning and productivity [ 40 ], relatively little attention has been paid to positive working experiences in the LTC sector. Our study moves a step forward from the current literature by shedding light on the subjective meaning making of work as an important, yet often overlooked, aspect of direct care work in LTC communities. While the structural factors of working conditions are pivotal to the job quality in LTC communities, MOW can serve as a psychological resource that engenders positive emotions and motivates HCWs to engage in direct care work in LTC communities. The findings of this study thus provide nuanced evidence about promoting meaningful work as a promising intervention for LTC workforce development. This could be done by addressing structural factors such as promoting job security, improving time and resource constrains, enhancing organizational support in LTC communities. Also, this could be done by supporting relationship building and better integrating psychosocial care into older people care work and exploring socio-cultural resources that contribute to positive meaning making of older people care work. In addition, as an extension of this qualitative study, quantitative research that examines the impacts of MOW on workers’ turnover intention and job satisfaction, as well as MOW as a mediating mechanism in explaining the impacts of working conditions on worker outcomes in the LTC sector, will be an important area for future exploration.

The findings of this study also imply that the meaning construction of older people care should be further understood and supported in the broader contexts, including the LTC policy, organizational support, and the socio-cultural meaning of older people care. As indicated by our research findings, the professional identity and job security in the LTC sector are important parts of HCWs’ MOW. While research to date has stressed the lack of job security and professional status in the LTC sector [ 41 ], our study has provided somewhat contradictory findings. Participants in the present study has relatively positive perceptions about career prospects in the LTC sector, proposing that the growing demand for LTC in the face of population aging entails job opportunities and job security, both of which make a career in LTC attractive. The nurses highlighted that their work was different to but equally as professional, skilled, and challenging as acute hospital care. Some indicated that their nursing care experiences in LTC communities allowed them to develop specialties in chronic disease management to maintain the wellness and quality of life of older people. This positive perception of LTC work is partly shaped by the preliminary, yet far from finished, social policy attempts to professionalize the LTC workforce in the local context. In Hong Kong, LTC policy has laid out the foundation of a relatively promising career development pathway in the nursing profession for HCWs in the LTC sector, most noticeably through the establishment of the Vocational Qualifications Pathway (VQP) for the LTC service industry and professional training programs [ 42 ]. Our findings thus call for research and social policy interventions to address the professionalization of the LTC sector and enable HCWs to gain public recognition, rewarding pay, job security, and career development.

Additionally, the findings of this study add to the existing studies on working conditions in LTC communities by highlighting the lack of organizational support for relational care as an organizational barrier to attaining meaningful work. Our study echoes existing research findings that HCWs deem affectional interactions and long-term relationships with older people as meaningful and valuable [ 29 ]. Yet HCWs’ yearning for meaningful relationships with older people is constantly constrained by the organizational structures of LTC communities, particularly the traditional institutional model of care centered around measurable and functional caregiving tasks [ 43 , 44 ]. The culture change movement that calls for humanizing care practice by transforming the institutional form of care in LTC communities to person-centered and relational care [ 45 , 46 ]. This culture change movement is thus particularly relevant to promoting the meaningful work of HCWs. Facilitating positive, meaningful working experiences for the LTC workforce would require changes in the organizational cultures of LTC communities to enable flexible caregiving routines, professional training opportunities that address relationship and rapport building, and a humanizing working environment.

Lastly, the meaning of older people care is constructed under an array of socio-cultural values. Even though increasing scholarly attention is being paid to revealing a culturally sensitive approach to older people care [ 47 ], very few studies have examined the socio-cultural meanings and values attached to older people care work from HCWs’ perspectives in the international contexts. As illustrated in this study, the notion of facilitating comfortable aging was seen as “accumulating good karma” and contained socio-cultural meaning towards older people care within the Chinese society. While engaging in older people care work is socially constructed as a “dirty work” [ 17 ], it could entail cultural salience and be regarded as a rewarding career in a society that values the life experience and moral authority of older people. This finding thus reveals the importance of a culturally sensitive perspective in researching and developing social policy interventions for LTC workforce development, including promoting a culturally resonant positive image of work in the LTC sector. This policy implication is not only resonant to other Asian societies, but also to the international contexts as Asian migrant workers represent a considerable proportion of the LTC workforce in developed countries such as Australia, US, UK and other European countries [ 48 , 49 ].

Limitations

Although this study adopted the maximum variation sampling method to increase the variety of HCWs’ perspectives and experiences, its use of purposive sampling is limited in representativeness. Additionally, this research intended to explore the MOW for all types of HCWs (eg, EN, RN, HW, PCW). However, these HCWs have quite different working experiences and work meaning because of different job quality and professional status. As non-nurses are particularly vulnerable to the deprivation of subjective well-being in work because of the poor job quality of their work [ 5 ], future studies would benefit from examining the subjective meaning making of work among this specific group of workers.

This qualitative study explores how HCWs negotiate the positive meaning of older people care work and the implications of meaningful work for workers’ job satisfaction and motivation to work in the LTC sector in Hong Kong’s LTC communities. While HCWs perform physically and emotionally demanding care work, they actively construct a subjective meaning of older people care as a helping career that enables them to facilitate comfortable aging of older people, build affectionate relationships, achieve professional identity, and gain job security. Their construction of meaningful work is further discussed in an array of social policy, organizational, and socio-cultural factors that all entail future research and social policy implications of LTC workforce development.

Availability of data and materials

The datasets generated and analysed during this study are not publicly available to protect the participant' confidentiality. However, they are available from the corresponding author upon reasonable request.

Abbreviations

Activities of daily living

Enrolled nurses

Health care workers

Health workers

Instrumental activities of daily living

Long-term care

Meaning of works

Personal care workers

Registered nurses

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Acknowledgements

We should like to thank the health care workers for participating in the study and the superintendents of the residential care homes for the older people to recruit the participants.

The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/M12/20).

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Y.S.Y., L.Y.K.L., L.S.Y.B., L.S.P.S. and L.S.K.V. conceived the ideas for the research. H.S., L.S.Y.B., L.S.P.S. and L.S.K.V. collected the data. Y.S.Y. and H.S. analysed the data. H.S. led the writing with the help of Y.S.Y. All authors critically revised the manuscript for important intellectual content. All authors have approved the final version of the article.

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Yau, S.Y., Lee, Y.K.L., Li, S.Y.B. et al. Health care workers’ self-perceived meaning of residential care work. BMC Health Serv Res 24 , 766 (2024). https://doi.org/10.1186/s12913-024-11218-2

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Textual Analysis | Guide, 3 Approaches & Examples

Published on November 8, 2019 by Jack Caulfield . Revised on June 22, 2023.

Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text – from its literal meaning to the subtext, symbolism, assumptions, and values it reveals.

The methods used to conduct textual analysis depend on the field and the aims of the research. It often aims to connect the text to a broader social, political, cultural, or artistic context. Relatedly, it’s good to be careful of confirmation bias when conducting these sorts of analyses, grounding your observations in clear and plausible ways.

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What is a text, textual analysis in cultural and media studies, textual analysis in the social sciences, textual analysis in literary studies, other interesting articles.

The term “text” is broader than it seems. A text can be a piece of writing, such as a book, an email, or a transcribed conversation. But in this context, a text can also be any object whose meaning and significance you want to interpret in depth: a film, an image, an artifact, even a place.

The methods you use to analyze a text will vary according to the type of object and the purpose of your analysis:

• Analysis of a short story might focus on the imagery, narrative perspective and structure of the text.
• To analyze a film, not only the dialogue but also the cinematography and use of sound could be relevant to the analysis.
• A building might be analyzed in terms of its architectural features and how it is navigated by visitors.
• You could analyze the rules of a game and what kind of behaviour they are designed to encourage in players.

While textual analysis is most commonly applied to written language, bear in mind how broad the term “text” is and how varied the methods involved can be.

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In the fields of cultural studies and media studies, textual analysis is a key component of research. Researchers in these fields take media and cultural objects – for example, music videos, social media content, billboard advertising – and treat them as texts to be analyzed.

Usually working within a particular theoretical framework (for example, using postcolonial theory, media theory, or semiotics), researchers seek to connect elements of their texts with issues in contemporary politics and culture. They might analyze many different aspects of the text:

• Word choice
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• Relationship with other texts

Textual analysis in this context is usually creative and qualitative in its approach. Researchers seek to illuminate something about the underlying politics or social context of the cultural object they’re investigating.

In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys , as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations.

Textual analysis in the social sciences sometimes takes a more quantitative approach , where the features of texts are measured numerically. For example, a researcher might investigate how often certain words are repeated in social media posts, or which colors appear most prominently in advertisements for products targeted at different demographics.

Some common methods of analyzing texts in the social sciences include content analysis , thematic analysis , and discourse analysis .

Textual analysis is the most important method in literary studies. Almost all work in this field involves in-depth analysis of texts – in this context, usually novels, poems, stories or plays.

Because it deals with literary writing, this type of textual analysis places greater emphasis on the deliberately constructed elements of a text: for example, rhyme and meter in a poem, or narrative perspective in a novel. Researchers aim to understand and explain how these elements contribute to the text’s meaning.

However, literary analysis doesn’t just involve discovering the author’s intended meaning. It often also explores potentially unintended connections between different texts, asks what a text reveals about the context in which it was written, or seeks to analyze a classic text in a new and unexpected way.

Some well-known examples of literary analysis show the variety of approaches that can be taken:

• Eve Kosofky Sedgwick’s book Between Men analyzes Victorian literature in light of more contemporary perspectives on gender and sexuality.
• Roland Barthes’ S/Z provides an in-depth structural analysis of a short story by Balzac.
• Harold Bloom’s The Anxiety of Influence applies his own “influence theory” to an analysis of various classic poets.

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Third-party and independent candidates for president often fall short of early polling numbers

The 2024 presidential campaign stands out as the first presumptive rematch between major-party candidates since 1956. It’s also the first time an ex-president has run to reclaim the White House in more than a century.

Another uncommon feature is the presence of several high-profile alternative candidates, including Democratic-scion-turned-independent Robert F. Kennedy Jr., independent Cornel West and three-time Green Party nominee Jill Stein.

Kennedy, an environmental lawyer and anti-vaccine activist , is currently polling in the mid-single digits nationally. He appears to draw support both from people who might otherwise back President Joe Biden and former President Donald Trump, complicating both men’s campaign calculations. (Bear in mind that accurately gauging support for third-party candidates can be tricky .)

But U.S. political history tells us that third-party and independent candidates usually finish a lot lower than where they start.

We examined preelection polls in six presidential contests that featured significant third-party or independent candidates, then reviewed those candidates’ actual shares of the popular vote in the general election.

Not only did support for third-party and independent candidates tend to decline over the course of their campaigns, but their vote shares often came in lower than polls suggested they might.

Here’s an election-by-election look at underperformance by third-party and independent candidates.

Given the unusual dynamics of the 2024 presidential election – including the presence of several potentially significant third-party and independent candidates – Pew Research Center examined how such candidates fared in past elections.

We focused on the six elections over the past 60 years in which the major-party share of the nationwide popular vote was less than 98%. In each of those elections, an independent or third-party candidate won at least 2% of the vote.

For each of those candidates, we obtained support-level data via iPoll , an online archive of historical survey data maintained by Cornell University’s Roper Center for Public Opinion Research. For 1980 and subsequent elections, we limited our analysis to surveys of registered voters. No such surveys were available for the 1968 election, so in that case we used surveys of the national adult population.

Over the decades, survey modes shifted from predominantly face-to-face interviews to landline telephone interviews, and then to landline-plus-cellphone interviews. By 2016, online surveys were making their first appearances, but most polls were still conducted via phone. To avoid any distortions caused by such different survey modes , we used only surveys conducted by the same mode within a given year. This meant that we only used face-to-face surveys in 1968, and only phone surveys in all other years we analyzed.

We also looked at the wording of each individual question to make sure each survey was asking essentially the same thing in similar ways. In particular, we wanted to ensure that candidates were referred to by name and identified by party (or as “independent” when appropriate).

Once we had assembled a list of comparable questions, we plotted support for third-party and independent candidates on a timeline. The final point on each chart represents the candidate’s share of the total nationwide popular vote. For 1968 through 2000, we used figures from America Votes , a long-running compilation of election data. For the 2016 election, we compiled official returns from all 50 states and the District of Columbia.

With two exceptions, all support figures in this analysis include those who said they would vote for or leaned toward the candidate in question. The exceptions are John Anderson in 1980 (because no surveys with “leaner” questions met our inclusion criteria) and Ross Perot in 1992, during the interim period in which he wasn’t actively campaigning (because surveys did not typically ask “leaner” questions about him during this period).

1968: George Wallace

Fresh off his first term as Alabama’s segregationist governor, George Wallace – running a “law and order”-themed campaign under the American Independent Party banner – saw his support rise in polls over the spring and summer leading up to the 1968 election. In April, around 10% of adults nationally said they supported or leaned toward Wallace. By September, that had doubled to 20%. Wallace appeared within reach of his goal: dividing the field enough to throw the election to the House of Representatives , where he could try to bargain his electoral votes for “concessions” on desegregation, voting rights and other issues.

That fall, Republican Richard Nixon’s campaign began warning conservatives that voting for Wallace would only help Democrat Hubert Humphrey. Meanwhile, Democratic-aligned unions worked to pull their members – whom Wallace had targeted – back into Humphrey’s fold. Wallace’s running mate, retired Air Force Gen. Curtis LeMay , also made headlines at his introductory press conference after saying he’d consider using nuclear weapons in Vietnam.

Wallace’s support in the polls began to slide, reaching the mid-teens in the weeks before Election Day. He ended up with 13.5% of the popular vote and 46 electoral votes – not enough to keep Nixon from winning the White House.

1980: John Anderson

Rep. John Anderson of Illinois was trailing badly in the Republican presidential primaries when, in April 1980, he dropped out and said he would run as an independent instead. Anderson’s candidacy generated considerable public interest: Around 20% of registered voters said they would support him, and he continued to poll around that level throughout the spring.

But Anderson’s nascent campaign had to spend much time and energy that spring and summer simply getting his name on state ballots. Anderson faded from view during that summer’s Democratic and Republican conventions. Incumbent President Jimmy Carter, the Democrat, refused to share a debate stage with him in the fall – though Republican nominee Ronald Reagan did debate Anderson one-on-one.

By October, Anderson’s support in polls had dwindled to the 9%-10% range. In the end, he won 6.6% of the national popular vote.

1992: Ross Perot

Money and visibility weren’t issues for Ross Perot, the billionaire businessman from Texas who mounted a stop-and-go independent campaign against Republican President George H.W. Bush and his Democratic challenger, Arkansas Gov. Bill Clinton.

Perot’s effort, driven initially by volunteers and appearances on Larry King Live , quickly gained momentum. In March, as Perot’s backers began gathering the hundreds of thousands of petition signatures he would need to get on state ballots, Perot was regularly receiving support from 20% or more of registered voters in polls. By May, about a third of registered voters were telling pollsters they’d vote for or were leaning toward Perot. In a few surveys, he led both Bush and Clinton.

Amid sharpening attacks from Republicans and Democrats , though, Perot’s numbers began falling. In mid-July, when his support was below 20% in most polls, Perot abruptly quit the race .

Although Perot was no longer actively campaigning, his name remained on two dozen state ballots, and some never-say-die supporters continued working to gain him ballot access in additional states. Pollsters continued to ask voters about Perot throughout the summer and fall – especially as speculation grew that he might jump back into the race. While Perot’s support declined steadily during this interim period, in late September around 10% of voters still said they preferred him to Bush or Clinton.

Perot reentered the campaign in early October, and within a few weeks his support had climbed back up to around 20%, including leaners. It began to slip again as Election Day neared, falling to around 15%. In the end, Perot won 18.9% of the popular vote – the best showing by a non-major-party candidate since Theodore Roosevelt 80 years earlier .

1996: Ross Perot

Perot wouldn’t come close to that in his second campaign. At the start of the year, when it was still unclear whether he would seek the nomination of the Reform Party (which he had founded the year before), his support among registered voters typically was in the mid-teens.

But Perot’s support declined during the campaign, eventually settling at around 5%-7%, including leaners. His poll numbers did pick up a bit in the run-up to Election Day, when he received 8.4% of the popular vote. Among the minor candidates Perot beat out for third place: consumer advocate Ralph Nader, who took 0.7% representing the Green Party.

2000: Ralph Nader and Pat Buchanan

Nader had a considerably higher profile four years later, when he was again the Green Party’s nominee. Polls taken during that close, contentious campaign regularly found that around 5% of registered voters said they supported or leaned toward Nader.

That was enough to concern Democrats that Nader threatened Vice President Al Gore’s chances of defeating Republican Texas Gov. George W. Bush. (Whether he in fact did so is still hotly debated among political scientists , journalists and other observers .)

In the end, Nader won only 2.7% of the national popular vote. But in several closely divided states – including Florida and New Hampshire, both of which Bush carried – Nader’s share was enough to potentially swing the outcome.

Another third-party candidate in 2000 received a fair amount of public and media attention: Pat Buchanan, the conservative commentator who had captured the nomination of Perot’s Reform Party. Buchanan polled as high as 4% in the spring, but by fall was mostly in the 1%-2% range. He ended up with less than 0.5% of the popular vote, but did well enough in five states to theoretically (or perhaps not so theoretically ) affect the outcome.

2016: Gary Johnson and Jill Stein

Widespread dissatisfaction with Republican Trump and his Democratic opponent, Hillary Clinton, may have caused more voters than usual to look beyond the major parties. Two candidates in particular received considerable attention: former New Mexico Gov. Gary Johnson – the Libertarian Party nominee – and physician and activist Jill Stein of the Green Party. (Both Johnson and Stein had also run in 2012, though with less impact.)

Johnson polled fairly strongly into the fall, with 8%-12% of registered voters routinely saying that they would vote for him or were leaning toward him. But Johnson’s poll numbers began trending downward, and by Election Day his support level was hovering around 5%-6%. Johnson ended up receiving 3.3% of the vote – the 52-year-old Libertarian Party’s best showing in a presidential election to date.

For her part, Stein often received support from 5%-7% of registered voters in polls taken during the spring and summer of 2016. But her support also eroded as the campaign went on, and she eventually received just over 1% of the popular vote – still the party’s best result since Nader in 2000.

• Election 2024
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• U.S. Elections & Voters
• Voters & Voting

Drew DeSilver is a senior writer at Pew Research Center .

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Safe and sustainable by design

What the framework is, how to get involved, test the framework, download documents. 

Give us feedback on the framework

The second feedback collection is open from 15 May until 30 August 2024 .

If you are a user of the framework, please provide your feedback.

Provide feedback

Support for the user

To help users apply the SSbD framework in practice:

• The JRC has published a Methodological Guidance that provides practical suggestions on the most commonly encountered issues when applying the framework
• the Partnership for the Assessment of Risks from Chemicals (PARC) has  developed a toolbox that provides an overview of existing tools for each step of the framework

The Commission Recommendation in a nutshell

The 'safe and sustainable by design' (SSbD framework) is a voluntary approach to guide the innovation process for chemicals and materials, announced on 8 December 2022 in a Commission Recommendation .

• steer the innovation process towards the green and sustainable industrial transition
• substitute or minimise the production and use of substances of concern, in line with, and beyond existing and upcoming regulatory obligations
• minimise the impact on health, climate and the environment during sourcing, production, use and end-of-life of chemicals, materials and products

The framework is composed of a (re-)design phase and an assessment phase that are applied iteratively as data becomes available.

The (re-)design phase consists of the application of guiding principles to steer the development process. The goal, the scope and the system boundaries – which will frame the assessment of the chemical or material – are defined in this phase.

The assessment phase comprises of 4 steps: hazard, workers exposure during production, exposure during use and life-cycle assessment. The assessment can be carried out either on newly developed chemicals and/or materials, or on existing chemicals and/or materials to improve their safety and sustainability performance during production, use and/or end-of-life.

Sign up to the SSbD stakeholder community

A European assessment framework. This Commission recommendation promotes research and innovation for safer and more sustainable chemicals and materials.

Test the framework

We are encouraging the engagement of relevant and willing stakeholders to support the progress of SSbD and adapt their innovation processes. The EU has started to implement SSbD under the Horizon Europe framework programme, but intends to continuously improve the methods, tools and data availability for ‘safe and sustainable by design’ chemicals and materials, as well as to refine the framework and make it applicable to a wide variety of substances.

The testing phase will allow us to establish a joint scientific reference base for safety and sustainability assessments that are necessary for innovation processes. It will also support the development of a fifth step on socioeconomic assessment. The engagement of the stakeholder community, and in particular the industry, is therefore crucial.

Who should participate?

The Recommendation is addressed to EU countries, industry, research and technology organisations (RTOs) and academia with each stakeholder group giving feedback on different actions.  

Expected actions by EU countries

• promote the framework in national research and innovation programmes
• increase the availability of findable, accessible, interoperable, reusable (FAIR) data for safe and sustainable by design assessment
• support the improvement of assessment methods, models and tools
• support the development of educational curricula on skills related to safety and sustainability of chemicals and materials

Expected actions by industry, academia and RTOs

• use the framework when developing chemicals and materials
• make available FAIR data for safe and sustainable by design assessment
• support the development of professional training and educational curricula on skills related to safety and sustainability of chemicals and materials

What is in there for me?

You can have your say by being part of the development of a common understanding of what safe and sustainable chemicals and materials are and how to assess them.

You will benefit from regulatory preparedness by applying 'safe and sustainable by design' in your innovation process and bring SSbD to practice by promoting the framework as a common baseline and ensure that other initiatives build on it.

You can support the design and assessment of digital tools assessing safety and sustainability early in the innovation process and increase transparency of SSbD strategies to support sustainable finance and consumer awareness.

• May - June 2023 Feedback collection
• Winter 2023 Workshop on collected feedback
• Spring 2024 Guidance report v1
• May - August 2024 Feedback collection
• Autumn 2024 Workshop on collected feedback
• Winter 2024 Guidance report v2
• 2025 Revision of framework
• 4 th SSbD Stakeholder workshop: Day 1 morning / Day 1 afternoon / Day 2
• 1st SSbD bootcamp: Day 1 / Day 2 / Day 3
• Webinar on the adoption of the SSbD Recommendation
• 3 rd SSbD Stakeholder workshop: Day 1 / Day 2

• Training and workshops
• Tuesday 22 October 2024, 13:30 - Friday 25 October 2024, 14:30 (CEST)
• Thessaloniki, Greece

• Wednesday 6 December 2023, 09:00 - Thursday 7 December 2023, 17:30 (CET)
• Brussels, Belgium

• Wednesday 25 October 2023, 14:30 - Friday 27 October 2023, 14:30 (CEST)
• Ispra, Italy

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