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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense.

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words.

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

Fraud: To ensure an actual human being records each response to the survey or the questionnaire
Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
Procedure: To ensure ethical standards were maintained while collecting the data sample
Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

Count, Percent, Frequency
It is used to denote home often a particular event occurs.
Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

Mean, Median, Mode
The method is widely used to demonstrate distribution by various points.
Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

Range, Variance, Standard deviation
Here the field equals high/low points.
Variance standard deviation = difference between the observed score and mean
It is used to identify the spread of scores by stating intervals.
Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

Percentile ranks, Quartile ranks
It relies on standardized scores helping researchers to identify the relationship between different scores.
It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie.

Here are two significant areas of inferential statistics.

Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables. Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.
Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

LEARN ABOUT: Average Order Value

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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data analysis types, methods, and techniques tree diagram

Data Analysis: Types, Methods & Techniques (a Complete List)

( Updated Version )

While the term sounds intimidating, “data analysis” is nothing more than making sense of information in a table. It consists of filtering, sorting, grouping, and manipulating data tables with basic algebra and statistics.

In fact, you don’t need experience to understand the basics. You have already worked with data extensively in your life, and “analysis” is nothing more than a fancy word for good sense and basic logic.

Over time, people have intuitively categorized the best logical practices for treating data. These categories are what we call today types , methods , and techniques .

This article provides a comprehensive list of types, methods, and techniques, and explains the difference between them.

For a practical intro to data analysis (including types, methods, & techniques), check out our Intro to Data Analysis eBook for free.

Descriptive, Diagnostic, Predictive, & Prescriptive Analysis

If you Google “types of data analysis,” the first few results will explore descriptive , diagnostic , predictive , and prescriptive analysis. Why? Because these names are easy to understand and are used a lot in “the real world.”

Descriptive analysis is an informational method, diagnostic analysis explains “why” a phenomenon occurs, predictive analysis seeks to forecast the result of an action, and prescriptive analysis identifies solutions to a specific problem.

That said, these are only four branches of a larger analytical tree.

Good data analysts know how to position these four types within other analytical methods and tactics, allowing them to leverage strengths and weaknesses in each to uproot the most valuable insights.

Let’s explore the full analytical tree to understand how to appropriately assess and apply these four traditional types.

Tree diagram of Data Analysis Types, Methods, and Techniques

Here’s a picture to visualize the structure and hierarchy of data analysis types, methods, and techniques.

If it’s too small you can view the picture in a new tab . Open it to follow along!

Note: basic descriptive statistics such as mean , median , and mode , as well as standard deviation , are not shown because most people are already familiar with them. In the diagram, they would fall under the “descriptive” analysis type.

Tree Diagram Explained

The highest-level classification of data analysis is quantitative vs qualitative . Quantitative implies numbers while qualitative implies information other than numbers.

Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis . Mathematical types then branch into descriptive , diagnostic , predictive , and prescriptive .

Methods falling under mathematical analysis include clustering , classification , forecasting , and optimization . Qualitative data analysis methods include content analysis , narrative analysis , discourse analysis , framework analysis , and/or grounded theory .

Moreover, mathematical techniques include regression , Nïave Bayes , Simple Exponential Smoothing , cohorts , factors , linear discriminants , and more, whereas techniques falling under the AI type include artificial neural networks , decision trees , evolutionary programming , and fuzzy logic . Techniques under qualitative analysis include text analysis , coding , idea pattern analysis , and word frequency .

It’s a lot to remember! Don’t worry, once you understand the relationship and motive behind all these terms, it’ll be like riding a bike.

We’ll move down the list from top to bottom and I encourage you to open the tree diagram above in a new tab so you can follow along .

But first, let’s just address the elephant in the room: what’s the difference between methods and techniques anyway?

Difference between methods and techniques

Though often used interchangeably, methods ands techniques are not the same. By definition, methods are the process by which techniques are applied, and techniques are the practical application of those methods.

For example, consider driving. Methods include staying in your lane, stopping at a red light, and parking in a spot. Techniques include turning the steering wheel, braking, and pushing the gas pedal.

Data sets: observations and fields

It’s important to understand the basic structure of data tables to comprehend the rest of the article. A data set consists of one far-left column containing observations, then a series of columns containing the fields (aka “traits” or “characteristics”) that describe each observations. For example, imagine we want a data table for fruit. It might look like this:

The fruit (observation)	(field1)	Avg. diameter (field 2)	Avg. time to eat (field 3)
Watermelon	20 lbs (9 kg)	16 inch (40 cm)	20 minutes
Apple	.33 lbs (.15 kg)	4 inch (8 cm)	5 minutes
Orange	.30 lbs (.14 kg)	4 inch (8 cm)	5 minutes

Now let’s turn to types, methods, and techniques. Each heading below consists of a description, relative importance, the nature of data it explores, and the motivation for using it.

Quantitative Analysis

It accounts for more than 50% of all data analysis and is by far the most widespread and well-known type of data analysis.
As you have seen, it holds descriptive, diagnostic, predictive, and prescriptive methods, which in turn hold some of the most important techniques available today, such as clustering and forecasting.
It can be broken down into mathematical and AI analysis.
Importance : Very high . Quantitative analysis is a must for anyone interesting in becoming or improving as a data analyst.
Nature of Data: data treated under quantitative analysis is, quite simply, quantitative. It encompasses all numeric data.
Motive: to extract insights. (Note: we’re at the top of the pyramid, this gets more insightful as we move down.)

Qualitative Analysis

It accounts for less than 30% of all data analysis and is common in social sciences .
It can refer to the simple recognition of qualitative elements, which is not analytic in any way, but most often refers to methods that assign numeric values to non-numeric data for analysis.
Because of this, some argue that it’s ultimately a quantitative type.
Importance: Medium. In general, knowing qualitative data analysis is not common or even necessary for corporate roles. However, for researchers working in social sciences, its importance is very high .
Nature of Data: data treated under qualitative analysis is non-numeric. However, as part of the analysis, analysts turn non-numeric data into numbers, at which point many argue it is no longer qualitative analysis.
Motive: to extract insights. (This will be more important as we move down the pyramid.)

Mathematical Analysis

Description: mathematical data analysis is a subtype of qualitative data analysis that designates methods and techniques based on statistics, algebra, and logical reasoning to extract insights. It stands in opposition to artificial intelligence analysis.
Importance: Very High. The most widespread methods and techniques fall under mathematical analysis. In fact, it’s so common that many people use “quantitative” and “mathematical” analysis interchangeably.
Nature of Data: numeric. By definition, all data under mathematical analysis are numbers.
Motive: to extract measurable insights that can be used to act upon.

Artificial Intelligence & Machine Learning Analysis

Description: artificial intelligence and machine learning analyses designate techniques based on the titular skills. They are not traditionally mathematical, but they are quantitative since they use numbers. Applications of AI & ML analysis techniques are developing, but they’re not yet mainstream enough to show promise across the field.
Importance: Medium . As of today (September 2020), you don’t need to be fluent in AI & ML data analysis to be a great analyst. BUT, if it’s a field that interests you, learn it. Many believe that in 10 year’s time its importance will be very high .
Nature of Data: numeric.
Motive: to create calculations that build on themselves in order and extract insights without direct input from a human.

Descriptive Analysis

Description: descriptive analysis is a subtype of mathematical data analysis that uses methods and techniques to provide information about the size, dispersion, groupings, and behavior of data sets. This may sounds complicated, but just think about mean, median, and mode: all three are types of descriptive analysis. They provide information about the data set. We’ll look at specific techniques below.
Importance: Very high. Descriptive analysis is among the most commonly used data analyses in both corporations and research today.
Nature of Data: the nature of data under descriptive statistics is sets. A set is simply a collection of numbers that behaves in predictable ways. Data reflects real life, and there are patterns everywhere to be found. Descriptive analysis describes those patterns.
Motive: the motive behind descriptive analysis is to understand how numbers in a set group together, how far apart they are from each other, and how often they occur. As with most statistical analysis, the more data points there are, the easier it is to describe the set.

Diagnostic Analysis

Description: diagnostic analysis answers the question “why did it happen?” It is an advanced type of mathematical data analysis that manipulates multiple techniques, but does not own any single one. Analysts engage in diagnostic analysis when they try to explain why.
Importance: Very high. Diagnostics are probably the most important type of data analysis for people who don’t do analysis because they’re valuable to anyone who’s curious. They’re most common in corporations, as managers often only want to know the “why.”
Nature of Data : data under diagnostic analysis are data sets. These sets in themselves are not enough under diagnostic analysis. Instead, the analyst must know what’s behind the numbers in order to explain “why.” That’s what makes diagnostics so challenging yet so valuable.
Motive: the motive behind diagnostics is to diagnose — to understand why.

Predictive Analysis

Description: predictive analysis uses past data to project future data. It’s very often one of the first kinds of analysis new researchers and corporate analysts use because it is intuitive. It is a subtype of the mathematical type of data analysis, and its three notable techniques are regression, moving average, and exponential smoothing.
Importance: Very high. Predictive analysis is critical for any data analyst working in a corporate environment. Companies always want to know what the future will hold — especially for their revenue.
Nature of Data: Because past and future imply time, predictive data always includes an element of time. Whether it’s minutes, hours, days, months, or years, we call this time series data . In fact, this data is so important that I’ll mention it twice so you don’t forget: predictive analysis uses time series data .
Motive: the motive for investigating time series data with predictive analysis is to predict the future in the most analytical way possible.

Prescriptive Analysis

Description: prescriptive analysis is a subtype of mathematical analysis that answers the question “what will happen if we do X?” It’s largely underestimated in the data analysis world because it requires diagnostic and descriptive analyses to be done before it even starts. More than simple predictive analysis, prescriptive analysis builds entire data models to show how a simple change could impact the ensemble.
Importance: High. Prescriptive analysis is most common under the finance function in many companies. Financial analysts use it to build a financial model of the financial statements that show how that data will change given alternative inputs.
Nature of Data: the nature of data in prescriptive analysis is data sets. These data sets contain patterns that respond differently to various inputs. Data that is useful for prescriptive analysis contains correlations between different variables. It’s through these correlations that we establish patterns and prescribe action on this basis. This analysis cannot be performed on data that exists in a vacuum — it must be viewed on the backdrop of the tangibles behind it.
Motive: the motive for prescriptive analysis is to establish, with an acceptable degree of certainty, what results we can expect given a certain action. As you might expect, this necessitates that the analyst or researcher be aware of the world behind the data, not just the data itself.

Clustering Method

Description: the clustering method groups data points together based on their relativeness closeness to further explore and treat them based on these groupings. There are two ways to group clusters: intuitively and statistically (or K-means).
Importance: Very high. Though most corporate roles group clusters intuitively based on management criteria, a solid understanding of how to group them mathematically is an excellent descriptive and diagnostic approach to allow for prescriptive analysis thereafter.
Nature of Data : the nature of data useful for clustering is sets with 1 or more data fields. While most people are used to looking at only two dimensions (x and y), clustering becomes more accurate the more fields there are.
Motive: the motive for clustering is to understand how data sets group and to explore them further based on those groups.
Here’s an example set:

Classification Method

Description: the classification method aims to separate and group data points based on common characteristics . This can be done intuitively or statistically.
Importance: High. While simple on the surface, classification can become quite complex. It’s very valuable in corporate and research environments, but can feel like its not worth the work. A good analyst can execute it quickly to deliver results.
Nature of Data: the nature of data useful for classification is data sets. As we will see, it can be used on qualitative data as well as quantitative. This method requires knowledge of the substance behind the data, not just the numbers themselves.
Motive: the motive for classification is group data not based on mathematical relationships (which would be clustering), but by predetermined outputs. This is why it’s less useful for diagnostic analysis, and more useful for prescriptive analysis.

Forecasting Method

Description: the forecasting method uses time past series data to forecast the future.
Importance: Very high. Forecasting falls under predictive analysis and is arguably the most common and most important method in the corporate world. It is less useful in research, which prefers to understand the known rather than speculate about the future.
Nature of Data: data useful for forecasting is time series data, which, as we’ve noted, always includes a variable of time.
Motive: the motive for the forecasting method is the same as that of prescriptive analysis: the confidently estimate future values.

Optimization Method

Description: the optimization method maximized or minimizes values in a set given a set of criteria. It is arguably most common in prescriptive analysis. In mathematical terms, it is maximizing or minimizing a function given certain constraints.
Importance: Very high. The idea of optimization applies to more analysis types than any other method. In fact, some argue that it is the fundamental driver behind data analysis. You would use it everywhere in research and in a corporation.
Nature of Data: the nature of optimizable data is a data set of at least two points.
Motive: the motive behind optimization is to achieve the best result possible given certain conditions.

Content Analysis Method

Description: content analysis is a method of qualitative analysis that quantifies textual data to track themes across a document. It’s most common in academic fields and in social sciences, where written content is the subject of inquiry.
Importance: High. In a corporate setting, content analysis as such is less common. If anything Nïave Bayes (a technique we’ll look at below) is the closest corporations come to text. However, it is of the utmost importance for researchers. If you’re a researcher, check out this article on content analysis .
Nature of Data: data useful for content analysis is textual data.
Motive: the motive behind content analysis is to understand themes expressed in a large text

Narrative Analysis Method

Description: narrative analysis is a method of qualitative analysis that quantifies stories to trace themes in them. It’s differs from content analysis because it focuses on stories rather than research documents, and the techniques used are slightly different from those in content analysis (very nuances and outside the scope of this article).
Importance: Low. Unless you are highly specialized in working with stories, narrative analysis rare.
Nature of Data: the nature of the data useful for the narrative analysis method is narrative text.
Motive: the motive for narrative analysis is to uncover hidden patterns in narrative text.

Discourse Analysis Method

Description: the discourse analysis method falls under qualitative analysis and uses thematic coding to trace patterns in real-life discourse. That said, real-life discourse is oral, so it must first be transcribed into text.
Importance: Low. Unless you are focused on understand real-world idea sharing in a research setting, this kind of analysis is less common than the others on this list.
Nature of Data: the nature of data useful in discourse analysis is first audio files, then transcriptions of those audio files.
Motive: the motive behind discourse analysis is to trace patterns of real-world discussions. (As a spooky sidenote, have you ever felt like your phone microphone was listening to you and making reading suggestions? If it was, the method was discourse analysis.)

Framework Analysis Method

Description: the framework analysis method falls under qualitative analysis and uses similar thematic coding techniques to content analysis. However, where content analysis aims to discover themes, framework analysis starts with a framework and only considers elements that fall in its purview.
Importance: Low. As with the other textual analysis methods, framework analysis is less common in corporate settings. Even in the world of research, only some use it. Strangely, it’s very common for legislative and political research.
Nature of Data: the nature of data useful for framework analysis is textual.
Motive: the motive behind framework analysis is to understand what themes and parts of a text match your search criteria.

Grounded Theory Method

Description: the grounded theory method falls under qualitative analysis and uses thematic coding to build theories around those themes.
Importance: Low. Like other qualitative analysis techniques, grounded theory is less common in the corporate world. Even among researchers, you would be hard pressed to find many using it. Though powerful, it’s simply too rare to spend time learning.
Nature of Data: the nature of data useful in the grounded theory method is textual.
Motive: the motive of grounded theory method is to establish a series of theories based on themes uncovered from a text.

Clustering Technique: K-Means

Description: k-means is a clustering technique in which data points are grouped in clusters that have the closest means. Though not considered AI or ML, it inherently requires the use of supervised learning to reevaluate clusters as data points are added. Clustering techniques can be used in diagnostic, descriptive, & prescriptive data analyses.
Importance: Very important. If you only take 3 things from this article, k-means clustering should be part of it. It is useful in any situation where n observations have multiple characteristics and we want to put them in groups.
Nature of Data: the nature of data is at least one characteristic per observation, but the more the merrier.
Motive: the motive for clustering techniques such as k-means is to group observations together and either understand or react to them.

Regression Technique

Description: simple and multivariable regressions use either one independent variable or combination of multiple independent variables to calculate a correlation to a single dependent variable using constants. Regressions are almost synonymous with correlation today.
Importance: Very high. Along with clustering, if you only take 3 things from this article, regression techniques should be part of it. They’re everywhere in corporate and research fields alike.
Nature of Data: the nature of data used is regressions is data sets with “n” number of observations and as many variables as are reasonable. It’s important, however, to distinguish between time series data and regression data. You cannot use regressions or time series data without accounting for time. The easier way is to use techniques under the forecasting method.
Motive: The motive behind regression techniques is to understand correlations between independent variable(s) and a dependent one.

Nïave Bayes Technique

Description: Nïave Bayes is a classification technique that uses simple probability to classify items based previous classifications. In plain English, the formula would be “the chance that thing with trait x belongs to class c depends on (=) the overall chance of trait x belonging to class c, multiplied by the overall chance of class c, divided by the overall chance of getting trait x.” As a formula, it’s P(c|x) = P(x|c) * P(c) / P(x).
Importance: High. Nïave Bayes is a very common, simplistic classification techniques because it’s effective with large data sets and it can be applied to any instant in which there is a class. Google, for example, might use it to group webpages into groups for certain search engine queries.
Nature of Data: the nature of data for Nïave Bayes is at least one class and at least two traits in a data set.
Motive: the motive behind Nïave Bayes is to classify observations based on previous data. It’s thus considered part of predictive analysis.

Cohorts Technique

Description: cohorts technique is a type of clustering method used in behavioral sciences to separate users by common traits. As with clustering, it can be done intuitively or mathematically, the latter of which would simply be k-means.
Importance: Very high. With regard to resembles k-means, the cohort technique is more of a high-level counterpart. In fact, most people are familiar with it as a part of Google Analytics. It’s most common in marketing departments in corporations, rather than in research.
Nature of Data: the nature of cohort data is data sets in which users are the observation and other fields are used as defining traits for each cohort.
Motive: the motive for cohort analysis techniques is to group similar users and analyze how you retain them and how the churn.

Factor Technique

Description: the factor analysis technique is a way of grouping many traits into a single factor to expedite analysis. For example, factors can be used as traits for Nïave Bayes classifications instead of more general fields.
Importance: High. While not commonly employed in corporations, factor analysis is hugely valuable. Good data analysts use it to simplify their projects and communicate them more clearly.
Nature of Data: the nature of data useful in factor analysis techniques is data sets with a large number of fields on its observations.
Motive: the motive for using factor analysis techniques is to reduce the number of fields in order to more quickly analyze and communicate findings.

Linear Discriminants Technique

Description: linear discriminant analysis techniques are similar to regressions in that they use one or more independent variable to determine a dependent variable; however, the linear discriminant technique falls under a classifier method since it uses traits as independent variables and class as a dependent variable. In this way, it becomes a classifying method AND a predictive method.
Importance: High. Though the analyst world speaks of and uses linear discriminants less commonly, it’s a highly valuable technique to keep in mind as you progress in data analysis.
Nature of Data: the nature of data useful for the linear discriminant technique is data sets with many fields.
Motive: the motive for using linear discriminants is to classify observations that would be otherwise too complex for simple techniques like Nïave Bayes.

Exponential Smoothing Technique

Description: exponential smoothing is a technique falling under the forecasting method that uses a smoothing factor on prior data in order to predict future values. It can be linear or adjusted for seasonality. The basic principle behind exponential smoothing is to use a percent weight (value between 0 and 1 called alpha) on more recent values in a series and a smaller percent weight on less recent values. The formula is f(x) = current period value * alpha + previous period value * 1-alpha.
Importance: High. Most analysts still use the moving average technique (covered next) for forecasting, though it is less efficient than exponential moving, because it’s easy to understand. However, good analysts will have exponential smoothing techniques in their pocket to increase the value of their forecasts.
Nature of Data: the nature of data useful for exponential smoothing is time series data . Time series data has time as part of its fields .
Motive: the motive for exponential smoothing is to forecast future values with a smoothing variable.

Moving Average Technique

Description: the moving average technique falls under the forecasting method and uses an average of recent values to predict future ones. For example, to predict rainfall in April, you would take the average of rainfall from January to March. It’s simple, yet highly effective.
Importance: Very high. While I’m personally not a huge fan of moving averages due to their simplistic nature and lack of consideration for seasonality, they’re the most common forecasting technique and therefore very important.
Nature of Data: the nature of data useful for moving averages is time series data .
Motive: the motive for moving averages is to predict future values is a simple, easy-to-communicate way.

Neural Networks Technique

Description: neural networks are a highly complex artificial intelligence technique that replicate a human’s neural analysis through a series of hyper-rapid computations and comparisons that evolve in real time. This technique is so complex that an analyst must use computer programs to perform it.
Importance: Medium. While the potential for neural networks is theoretically unlimited, it’s still little understood and therefore uncommon. You do not need to know it by any means in order to be a data analyst.
Nature of Data: the nature of data useful for neural networks is data sets of astronomical size, meaning with 100s of 1000s of fields and the same number of row at a minimum .
Motive: the motive for neural networks is to understand wildly complex phenomenon and data to thereafter act on it.

Decision Tree Technique

Description: the decision tree technique uses artificial intelligence algorithms to rapidly calculate possible decision pathways and their outcomes on a real-time basis. It’s so complex that computer programs are needed to perform it.
Importance: Medium. As with neural networks, decision trees with AI are too little understood and are therefore uncommon in corporate and research settings alike.
Nature of Data: the nature of data useful for the decision tree technique is hierarchical data sets that show multiple optional fields for each preceding field.
Motive: the motive for decision tree techniques is to compute the optimal choices to make in order to achieve a desired result.

Evolutionary Programming Technique

Description: the evolutionary programming technique uses a series of neural networks, sees how well each one fits a desired outcome, and selects only the best to test and retest. It’s called evolutionary because is resembles the process of natural selection by weeding out weaker options.
Importance: Medium. As with the other AI techniques, evolutionary programming just isn’t well-understood enough to be usable in many cases. It’s complexity also makes it hard to explain in corporate settings and difficult to defend in research settings.
Nature of Data: the nature of data in evolutionary programming is data sets of neural networks, or data sets of data sets.
Motive: the motive for using evolutionary programming is similar to decision trees: understanding the best possible option from complex data.
Video example :

Fuzzy Logic Technique

Description: fuzzy logic is a type of computing based on “approximate truths” rather than simple truths such as “true” and “false.” It is essentially two tiers of classification. For example, to say whether “Apples are good,” you need to first classify that “Good is x, y, z.” Only then can you say apples are good. Another way to see it helping a computer see truth like humans do: “definitely true, probably true, maybe true, probably false, definitely false.”
Importance: Medium. Like the other AI techniques, fuzzy logic is uncommon in both research and corporate settings, which means it’s less important in today’s world.
Nature of Data: the nature of fuzzy logic data is huge data tables that include other huge data tables with a hierarchy including multiple subfields for each preceding field.
Motive: the motive of fuzzy logic to replicate human truth valuations in a computer is to model human decisions based on past data. The obvious possible application is marketing.

Text Analysis Technique

Description: text analysis techniques fall under the qualitative data analysis type and use text to extract insights.
Importance: Medium. Text analysis techniques, like all the qualitative analysis type, are most valuable for researchers.
Nature of Data: the nature of data useful in text analysis is words.
Motive: the motive for text analysis is to trace themes in a text across sets of very long documents, such as books.

Coding Technique

Description: the coding technique is used in textual analysis to turn ideas into uniform phrases and analyze the number of times and the ways in which those ideas appear. For this reason, some consider it a quantitative technique as well. You can learn more about coding and the other qualitative techniques here .
Importance: Very high. If you’re a researcher working in social sciences, coding is THE analysis techniques, and for good reason. It’s a great way to add rigor to analysis. That said, it’s less common in corporate settings.
Nature of Data: the nature of data useful for coding is long text documents.
Motive: the motive for coding is to make tracing ideas on paper more than an exercise of the mind by quantifying it and understanding is through descriptive methods.

Idea Pattern Technique

Description: the idea pattern analysis technique fits into coding as the second step of the process. Once themes and ideas are coded, simple descriptive analysis tests may be run. Some people even cluster the ideas!
Importance: Very high. If you’re a researcher, idea pattern analysis is as important as the coding itself.
Nature of Data: the nature of data useful for idea pattern analysis is already coded themes.
Motive: the motive for the idea pattern technique is to trace ideas in otherwise unmanageably-large documents.

Word Frequency Technique

Description: word frequency is a qualitative technique that stands in opposition to coding and uses an inductive approach to locate specific words in a document in order to understand its relevance. Word frequency is essentially the descriptive analysis of qualitative data because it uses stats like mean, median, and mode to gather insights.
Importance: High. As with the other qualitative approaches, word frequency is very important in social science research, but less so in corporate settings.
Nature of Data: the nature of data useful for word frequency is long, informative documents.
Motive: the motive for word frequency is to locate target words to determine the relevance of a document in question.

Types of data analysis in research

Types of data analysis in research methodology include every item discussed in this article. As a list, they are:

Quantitative
Qualitative
Mathematical
Machine Learning and AI
Descriptive
Prescriptive
Classification
Forecasting
Optimization
Grounded theory
Artificial Neural Networks
Decision Trees
Evolutionary Programming
Fuzzy Logic
Text analysis
Idea Pattern Analysis
Word Frequency Analysis
Nïave Bayes
Exponential smoothing
Moving average
Linear discriminant

Types of data analysis in qualitative research

As a list, the types of data analysis in qualitative research are the following methods:

Types of data analysis in quantitative research

As a list, the types of data analysis in quantitative research are:

Data analysis methods

As a list, data analysis methods are:

Content (qualitative)
Narrative (qualitative)
Discourse (qualitative)
Framework (qualitative)
Grounded theory (qualitative)

Quantitative data analysis methods

As a list, quantitative data analysis methods are:

Tabular View of Data Analysis Types, Methods, and Techniques

Types (Numeric or Non-numeric)	Quantitative
	Qualitative
Types tier 2 (Traditional Numeric or New Numeric)	Mathematical
	Artificial Intelligence (AI)
Types tier 3 (Informative Nature)	Descriptive
	Diagnostic
	Predictive
	Prescriptive
Methods	Clustering
	Classification
	Forecasting
	Optimization

	Narrative analysis
	Discourse analysis
	Framework analysis
	Grounded theory
Techniques	Clustering (doubles as technique)
	Regression (linear and multivariable)
	Nïave Bayes
	Cohorts
	Factors
	Linear Discriminants
	Exponential smoothing
	Moving average
	Neural networks
	Decision trees
	Evolutionary programming
	Fuzzy logic
	Text analysis
	Coding
	Idea pattern analysis
	Word frequency

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

Types of Data Analysis

Descriptive analysis
Diagnostic analysis
Exploratory analysis
Inferential analysis
Predictive analysis
Causal analysis
Mechanistic analysis
Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including energy, healthcare and marketing, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in decision-making , providing a better, faster and more effective system that minimizes risks and reduces human biases .

That said, there are different kinds of data analysis with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book R for Data Science :

Hypothesis Generation: This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
Hypothesis Confirmation: This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data . Here’s what you need to know:

Descriptive analysis is the very first analysis performed in the data analysis process.
It generates simple summaries of samples and measurements.
It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

2. Diagnostic Analysis

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen.
Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.
Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies .

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic.

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August.

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection .

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
The accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the central limit theorem .

Inferential Analysis Example

A psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven to nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

The accuracy of the predictions depends on the input variables.
Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 United States election is a popular topic and many prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. This way, researchers can examine how a change in one variable affects another. Here’s what you need to know:

To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
Causal analysis is applied in randomized studies focused on identifying causation.
Causal analysis is the gold standard in data analysis and scientific studies where the cause of a phenomenon is to be extracted and singled out, like separating wheat from chaff.
Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables . In some ways, it is a predictive analysis, but it’s modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science. Here’s what you need to know:

It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention.

Mechanistic Analysis Example

Say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

8. Prescriptive Analysis

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know:

Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses.
Companies need advanced technology and plenty of resources to conduct prescriptive analysis. Artificial intelligence systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed.

More on Data Explaining the Empirical Rule for Normal Distribution

When to Use the Different Types of Data Analysis

Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies.
Exploratory data analysis helps you discover correlations and relationships between variables in your data.
Inferential analysis is for generalizing the larger population with a smaller sample size of data.
Predictive analysis helps you make predictions about the future with data.
Causal analysis emphasizes finding the cause of a correlation between variables.
Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes.

A few important tips to remember about data analysis include:

Correlation doesn’t imply causation.
EDA helps discover new connections and form hypotheses.
Accuracy of inference depends on the sampling scheme.
A good prediction depends on the right input variables.
A simple linear model with enough data usually does the trick.
Using a variable to predict another doesn’t denote causal relationships.
Good data is hard to find, and to produce it requires expensive research.
Results from studies are done in aggregate and are average effects and might not apply to everyone.

Frequently Asked Questions

What is an example of data analysis.

A marketing team reviews a company’s web traffic over the past 12 months. To understand why sales rise and fall during certain months, the team breaks down the data to look at shoe type, seasonal patterns and sales events. Based on this in-depth analysis, the team can determine variables that influenced web traffic and make adjustments as needed.

How do you know which data analysis method to use?

Selecting a data analysis method depends on the goals of the analysis and the complexity of the task, among other factors. It’s best to assess the circumstances and consider the pros and cons of each type of data analysis before moving forward with a particular method.

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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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Types of data analysis

The means by which you analyse your data are largely determined by the nature of your research question , the approach and paradigm within which your research operates, the methods used, and consequently the type of data elicited. In turn, the language and terms you use in both conducting and reporting your data analysis should reflect these.

The list below includes some of the more commonly used means of qualitative data analysis in educational research – although this is by no means exhaustive. It is also important to point out that each of the terms given below generally encompass a range of possible methods or options and there can be overlap between them. In all cases, further reading is essential to ensure that the process of data analysis is valid, transparent and appropriately systematic, and we have provided below (as well as in our further resources and tools and resources for qualitative data analysis sections) some recommendations for this.

If your research is likely to involve quantitative analysis, we recommend the books listed below.

Types of qualitative data analysis

Thematic analysis
Coding and/or content analysis
Concept map analysis
Discourse or narrative analysis
Grouded theory
Phenomenological analysis or interpretative phenomenological analysis (IPA)

For qualitative approaches

Savin-Baden, M. & Howell Major, C. (2013) Data analysis. In Qualitative Research: The essential guide to theory and practice . (Abingdon, Routledge, pp. 434-450).

For quantitative approaches

Bors, D. (2018) Data analysis for the social sciences (Sage, London).

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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.

What is Data Analysis? (Types, Methods, and Tools)

Couchbase Product Marketing December 17, 2023

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains.

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process.

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior.

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs.

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

What is Big Data Analytics?
Operational Analytics
JSON Analytics + Real-Time Insights
Database vs. Data Warehouse: Differences, Use Cases, Examples
Couchbase Capella Columnar Product Blog
Posted in: Analytics , Application Design , Best Practices and Tutorials
Tagged in: data analytics , data visualization , time series

Posted by Couchbase Product Marketing

Research Data Management

Data Management Plans
Data Collection
Data Organization
Data Analysis and Visualization
Data Security and Backup
Data Preservation and Sharing
The Carpentries: Workshops & Training
Love Data Week 2024
Additional Resources

Data Curation Specialist

Data Analysis

What is Data Analysis?

Types of Data Analysis

Data analysis methods.

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main features of a dataset. It focuses on organizing and presenting the data in a meaningful way, often using measures such as mean, median, mode, and standard deviation. It provides an overview of the data and helps identify patterns or trends.

Inferential Analysis

Inferential analysis aims to make inferences or predictions about a larger population based on sample data. It involves applying statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. It helps generalize findings from a sample to a larger population.

Exploratory Data Analysis (EDA)

EDA focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations, summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps generate hypotheses for further analysis.

Diagnostic Analysis

Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of Variance), or correlation analysis are commonly used in diagnostic analysis.

Predictive Analysis

Predictive analysis involves using historical data to make predictions or forecasts about future outcomes. It utilizes statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.

Prescriptive Analysis

Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize outcomes. It helps in decision-making and resource allocation.

Qualitative Data Analysis

The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:

Content Analysis, for analyzing behavioral and verbal data.
Narrative Analysis, for working with data culled from interviews, diaries, and surveys.
Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.

Quantitative Data Analysis

Also known as statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:

Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body.

Data Visualization

What is data visualization

Scatter Plot

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Data visualization is commonly used for idea generation, and illustration, so as to help teams and individuals convey data more effectively to colleagues and decision-makers. Frequently used types of data visualizations are listed in the following tabs.

A bar chart is one of the most commonly used forms to present quantitative data. It is simple to create and to understand. It is best used when comparing data from different categories. A bar chart is simple: We usually have a few values – ordered as categories on the x or y axis. Then we have the values expressed as bars (horizontal) or columns (vertical). The extent of the bars is the value.

Bar Chart of Race & Ethnicity in New York (2015)

Datawheel, CC0, via Wikimedia Commons

A pie chart is used to display the proportions of a whole. These charts are useful for percentages. When making a pie chart, please note:

All portions should add up to a total of 100%.
Sizes of the portions should represent their value.
Not too many variables

A line chart is a type of chart used to show information that changes over time. We plot line charts using several points connected by straight lines. The line chart comprises two axes known as the 'x' axis and 'y' axis. The horizontal axis is known as the x-axis.

A scatter plot is a type of plot or diagram to display values for typically two variables for a set of data.

Scatter plots show whether there is a relationship between two variables. The trend line shows the central tendency of the data.

Scatter Plots | A Complete Guide to Scatter Plots

Yi, M. (2019). A complete guide to scatter plots. Retrieved February , 25 , 2021.

A box plot or boxplot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes indicating variability outside the upper and lower quartiles.

Michelson experiment (1881)

Data Analysis and Visualization Resources and Tools

Google Charts

The Research Data Services department assists with:

Open-access tools in analyzing and visualizing data.
Identify what types of visualization best fit your needs
Provide tips on creating your visualization
Offer guidance on interpreting data analysis results

Other Resources on Campus

Department of Sociology Statistics Tutoring Lab
The Data Analytics and Research Methodology (DARM) service in the Division of Research also provides faculty with up to six hours of free consulting and may access additional hours through contract consulting: see more information here .
T he Collaborative Learning Center (CLC) : please check the math & statistics tutoring schedule here .
Data Visualization : Schwabish, J. (2021). Better data visualizations: A guide for scholars, researchers, and wonks . Columbia University Press. https://www.jstor.org/stable/10.7312/schw19310
Data Analysis : Ross, S. M. (2017). Introductory statistics . Academic Press. The course textbook is available for download free of charge from OpenStax at https://openstax.org/details/introductory-statistics .
Introduction to Python Programming - OpenStax

What is R? What is RStudio?

R is more of a programming language than just a statistics program. it is “a language for data analysis and graphics.” You can use R to create, import, and scrape data from the web; clean and reshape it; visualize it; run statistical analysis and modeling operations on it; text and data mine it; and much more.

RStudio is a user interface for working with R. It is called an Integrated Development Environment (IDE): a piece of software that provides tools to make programming easier. RStudio acts as a sort of wrapper around the R language.

Install R and RStudio

R and RStudio are two separate pieces of software:

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis
RStudio is an integrated development environment (IDE) that makes using R easier. In this course we use RStudio to interact with R.
Download R from the CRAN website .
Run the .exe file that was just downloaded
Go to the RStudio download page
Under Installers select RStudio x.yy.zzz - Windows Vista/7/8/10 (where x, y, and z represent version numbers)
Double click the file to install it
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.
Select the .pkg file for the latest R version
Double click on the downloaded file to install R
It is also a good idea to install XQuartz (needed by some packages)
Under Installers select RStudio x.yy.zzz - Mac OS X 10.6+ (64-bit) (where x, y, and z represent version numbers)
Double click the file to install RStudio

Open Access e-book: Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations . " O'Reilly Media, Inc.".

Python is a high-level , general-purpose programming language . Its design philosophy emphasizes code readability with the use of significant indentation . Python is dynamically typed and garbage-collected . It supports multiple programming paradigms , including structured (particularly procedural ), object-oriented and functional programming . It is often described as a "batteries included" language due to its comprehensive standard library .

- from WIKIPedia

Introduction to Python Programming - OpenStax

NVivo is a software program used for qualitative and mixed-methods research . Specifically, it is used for the analysis of unstructured text, audio, video, and image data, including (but not limited to) interviews, focus groups, surveys, social media, and journal articles

With Tableau, users can upload data from spreadsheets, cloud-based data management software, and online databases and merge them to identify trends, filter databases, and forecast outcomes. Users also can drag and drop information to transform data and instantly create charts and visualizations. Start your free trial of Tableau here .

Microsoft’s Power BI software provides business intelligence and data analytics tools to clean and transform data, merge data from different sources, and perform grouping, clustering, and forecasting to find patterns in the data. Start Power BI for free here .

Google’s free Charts software creates customizable charts, maps, and diagrams from imported datasets.

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Research Guide: Data analysis and reporting findings

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends.

Data Analysis Checklist

Cleaning data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

* Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

Online Resources

Qualitative analysis of interview data: A step-by-step guide
Qualitative Data Analysis - Coding & Developing Themes

Recommended Quantitative Data Analysis books

Recommended Qualitative Data Analysis books

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Last Updated: Jul 16, 2024 8:49 AM
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Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

Inspecting : Initial examination of data to understand its structure, quality, and completeness.
Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

Calculate the mean, median, and mode of academic scores for both groups.
Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

Description: An open-source programming language specifically designed for statistical computing and data visualization.
Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

Description: A data mining software application used for building predictive models and conducting advanced analytics.
Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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The 7 Most Useful Data Analysis Methods and Techniques

Data analytics is the process of analyzing raw data to draw out meaningful insights. These insights are then used to determine the best course of action.

When is the best time to roll out that marketing campaign? Is the current team structure as effective as it could be? Which customer segments are most likely to purchase your new product?

Ultimately, data analytics is a crucial driver of any successful business strategy. But how do data analysts actually turn raw data into something useful? There are a range of methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.

You can get a hands-on introduction to data analytics in this free short course .

In this post, we’ll explore some of the most useful data analysis techniques. By the end, you’ll have a much clearer idea of how you can transform meaningless data into business intelligence. We’ll cover:

What is data analysis and why is it important?
What is the difference between qualitative and quantitative data?
Regression analysis
Monte Carlo simulation
Factor analysis
Cohort analysis
Cluster analysis
Time series analysis
Sentiment analysis
The data analysis process
The best tools for data analysis
Key takeaways

The first six methods listed are used for quantitative data , while the last technique applies to qualitative data. We briefly explain the difference between quantitative and qualitative data in section two, but if you want to skip straight to a particular analysis technique, just use the clickable menu.

1. What is data analysis and why is it important?

Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.

Why is data analysis important? Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.

These data will appear as different structures, including—but not limited to—the following:

The concept of big data —data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data as the three Vs: volume, velocity, and variety.

Volume: As mentioned earlier, organizations are collecting data constantly. In the not-too-distant past it would have been a real issue to store, but nowadays storage is cheap and takes up little space.
Velocity: Received data needs to be handled in a timely manner. With the growth of the Internet of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
Variety: The data being collected and stored by organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, audio, and so on. We’ll cover structured and unstructured data a little further on.

This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.

Real-time data

This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.

Machine data

This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.

Quantitative and qualitative data

Quantitative data—otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns. Qualitative data—otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos and more. We’ll discuss this further in the next section.

2. What is the difference between quantitative and qualitative data?

How you analyze your data depends on the type of data you’re dealing with— quantitative or qualitative . So what’s the difference?

Quantitative data is anything measurable , comprising specific quantities and numbers. Some examples of quantitative data include sales figures, email click-through rates, number of website visitors, and percentage revenue increase. Quantitative data analysis techniques focus on the statistical, mathematical, or numerical analysis of (usually large) datasets. This includes the manipulation of statistical data using computational techniques and algorithms. Quantitative analysis techniques are often used to explain certain phenomena or to make predictions.

Qualitative data cannot be measured objectively , and is therefore open to more subjective interpretation. Some examples of qualitative data include comments left in response to a survey question, things people have said during interviews, tweets and other social media posts, and the text included in product reviews. With qualitative data analysis, the focus is on making sense of unstructured data (such as written text, or transcripts of spoken conversations). Often, qualitative analysis will organize the data into themes—a process which, fortunately, can be automated.

Data analysts work with both quantitative and qualitative data , so it’s important to be familiar with a variety of analysis methods. Let’s take a look at some of the most useful techniques now.

3. Data analysis techniques

Now we’re familiar with some of the different types of data, let’s focus on the topic at hand: different methods for analyzing data.

a. Regression analysis

Regression analysis is used to estimate the relationship between a set of variables. When conducting any type of regression analysis , you’re looking to see if there’s a correlation between a dependent variable (that’s the variable or outcome you want to measure or predict) and any number of independent variables (factors which may have an impact on the dependent variable). The aim of regression analysis is to estimate how one or more variables might impact the dependent variable, in order to identify trends and patterns. This is especially useful for making predictions and forecasting future trends.

Let’s imagine you work for an ecommerce company and you want to examine the relationship between: (a) how much money is spent on social media marketing, and (b) sales revenue. In this case, sales revenue is your dependent variable—it’s the factor you’re most interested in predicting and boosting. Social media spend is your independent variable; you want to determine whether or not it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same. Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward. However: It’s important to note that, on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they don’t tell you anything about cause and effect. So, while a positive correlation between social media spend and sales revenue may suggest that one impacts the other, it’s impossible to draw definitive conclusions based on this analysis alone.

There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For example, your dependent variable might be continuous (i.e. something that can be measured on a continuous scale, such as sales revenue in USD), in which case you’d use a different type of regression analysis than if your dependent variable was categorical in nature (i.e. comprising values that can be categorised into a number of distinct groups based on a certain characteristic, such as customer location by continent). You can learn more about different types of dependent variables and how to choose the right regression analysis in this guide .

Regression analysis in action: Investigating the relationship between clothing brand Benetton’s advertising expenditure and sales

b. Monte Carlo simulation

When making decisions or taking certain actions, there are a range of different possible outcomes. If you take the bus, you might get stuck in traffic. If you walk, you might get caught in the rain or bump into your chatty neighbor, potentially delaying your journey. In everyday life, we tend to briefly weigh up the pros and cons before deciding which action to take; however, when the stakes are high, it’s essential to calculate, as thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a computerized technique used to generate models of possible outcomes and their probability distributions. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will be realized. The Monte Carlo method is used by data analysts to conduct advanced risk analysis, allowing them to better forecast what might happen in the future and make decisions accordingly.

So how does Monte Carlo simulation work, and what can it tell us? To run a Monte Carlo simulation, you’ll start with a mathematical model of your data—such as a spreadsheet. Within your spreadsheet, you’ll have one or several outputs that you’re interested in; profit, for example, or number of sales. You’ll also have a number of inputs; these are variables that may impact your output variable. If you’re looking at profit, relevant inputs might include the number of sales, total marketing spend, and employee salaries. If you knew the exact, definitive values of all your input variables, you’d quite easily be able to calculate what profit you’d be left with at the end. However, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if you make 100,000 sales and hire five new employees on a salary of $50,000 each? What is the likelihood of this outcome? What will your profit be if you only make 12,000 sales and hire five new employees? And so on. It does this by replacing all uncertain values with functions which generate random samples from distributions determined by you, and then running a series of calculations and recalculations to produce models of all the possible outcomes and their probability distributions. The Monte Carlo method is one of the most popular techniques for calculating the effect of unpredictable variables on a specific output variable, making it ideal for risk analysis.

Monte Carlo simulation in action: A case study using Monte Carlo simulation for risk analysis

c. Factor analysis

Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors. It works on the basis that multiple separate, observable variables correlate with each other because they are all associated with an underlying construct. This is useful not only because it condenses large datasets into smaller, more manageable samples, but also because it helps to uncover hidden patterns. This allows you to explore concepts that cannot be easily measured or observed—such as wealth, happiness, fitness, or, for a more business-relevant example, customer loyalty and satisfaction.

Let’s imagine you want to get to know your customers better, so you send out a rather long survey comprising one hundred questions. Some of the questions relate to how they feel about your company and product; for example, “Would you recommend us to a friend?” and “How would you rate the overall customer experience?” Other questions ask things like “What is your yearly household income?” and “How much are you willing to spend on skincare each month?”

Once your survey has been sent out and completed by lots of customers, you end up with a large dataset that essentially tells you one hundred different things about each customer (assuming each customer gives one hundred responses). Instead of looking at each of these responses (or variables) individually, you can use factor analysis to group them into factors that belong together—in other words, to relate them to a single underlying construct. In this example, factor analysis works by finding survey items that are strongly correlated. This is known as covariance . So, if there’s a strong positive correlation between household income and how much they’re willing to spend on skincare each month (i.e. as one increases, so does the other), these items may be grouped together. Together with other variables (survey responses), you may find that they can be reduced to a single factor such as “consumer purchasing power”. Likewise, if a customer experience rating of 10/10 correlates strongly with “yes” responses regarding how likely they are to recommend your product to a friend, these items may be reduced to a single factor such as “customer satisfaction”.

In the end, you have a smaller number of factors rather than hundreds of individual variables. These factors are then taken forward for further analysis, allowing you to learn more about your customers (or any other area you’re interested in exploring).

Factor analysis in action: Using factor analysis to explore customer behavior patterns in Tehran

d. Cohort analysis

Cohort analysis is a data analytics technique that groups users based on a shared characteristic , such as the date they signed up for a service or the product they purchased. Once users are grouped into cohorts, analysts can track their behavior over time to identify trends and patterns.

So what does this mean and why is it useful? Let’s break down the above definition further. A cohort is a group of people who share a common characteristic (or action) during a given time period. Students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in the month of December may also be considered a cohort.

With cohort analysis, you’re dividing your customers or users into groups and looking at how these groups behave over time. So, rather than looking at a single, isolated snapshot of all your customers at a given moment in time (with each customer at a different point in their journey), you’re examining your customers’ behavior in the context of the customer lifecycle. As a result, you can start to identify patterns of behavior at various points in the customer journey—say, from their first ever visit to your website, through to email newsletter sign-up, to their first purchase, and so on. As such, cohort analysis is dynamic, allowing you to uncover valuable insights about the customer lifecycle.

This is useful because it allows companies to tailor their service to specific customer segments (or cohorts). Let’s imagine you run a 50% discount campaign in order to attract potential new customers to your website. Once you’ve attracted a group of new customers (a cohort), you’ll want to track whether they actually buy anything and, if they do, whether or not (and how frequently) they make a repeat purchase. With these insights, you’ll start to gain a much better understanding of when this particular cohort might benefit from another discount offer or retargeting ads on social media, for example. Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more targeted, personalized experience. You can learn more about how to run cohort analysis using Google Analytics .

Cohort analysis in action: How Ticketmaster used cohort analysis to boost revenue

e. Cluster analysis

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups (or clusters) that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other, and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

There are many real-world applications of cluster analysis. In marketing, cluster analysis is commonly used to group a large customer base into distinct segments, allowing for a more targeted approach to advertising and communication. Insurance firms might use cluster analysis to investigate why certain locations are associated with a high number of insurance claims. Another common application is in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and thus try to mitigate the risk with protective measures).

It’s important to note that, while cluster analysis may reveal structures within your data, it won’t explain why those structures exist. With that in mind, cluster analysis is a useful starting point for understanding your data and informing further analysis. Clustering algorithms are also used in machine learning—you can learn more about clustering in machine learning in our guide .

Cluster analysis in action: Using cluster analysis for customer segmentation—a telecoms case study example

f. Time series analysis

Time series analysis is a statistical technique used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time (for example, weekly sales figures or monthly email sign-ups). By looking at time-related trends, analysts are able to forecast how the variable of interest may fluctuate in the future.

When conducting time series analysis, the main patterns you’ll be looking out for in your data are:

Trends: Stable, linear increases or decreases over an extended time period.
Seasonality: Predictable fluctuations in the data due to seasonal factors over a short period of time. For example, you might see a peak in swimwear sales in summer around the same time every year.
Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but rather, may occur as a result of economic or industry-related conditions.

As you can imagine, the ability to make informed predictions about the future has immense value for business. Time series analysis and forecasting is used across a variety of industries, most commonly for stock market analysis, economic forecasting, and sales forecasting. There are different types of time series models depending on the data you’re using and the outcomes you want to predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. For an in-depth look at time series analysis, refer to our guide .

Time series analysis in action: Developing a time series model to predict jute yarn demand in Bangladesh

g. Sentiment analysis

When you think of data, your mind probably automatically goes to numbers and spreadsheets.

Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people (especially customers) write and say about you. So how do you go about analyzing textual data?

One highly useful qualitative technique is sentiment analysis , a technique which belongs to the broader category of text analysis —the (usually automated) process of sorting and understanding textual data.

With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service.

There are several different types of sentiment analysis models, each with a slightly different focus. The three main types include:

Fine-grained sentiment analysis

If you want to focus on opinion polarity (i.e. positive, neutral, or negative) in depth, fine-grained sentiment analysis will allow you to do so.

For example, if you wanted to interpret star ratings given by customers, you might use fine-grained sentiment analysis to categorize the various ratings along a scale ranging from very positive to very negative.

Emotion detection

This model often uses complex machine learning algorithms to pick out various emotions from your textual data.

You might use an emotion detection model to identify words associated with happiness, anger, frustration, and excitement, giving you insight into how your customers feel when writing about you or your product on, say, a product review site.

Aspect-based sentiment analysis

This type of analysis allows you to identify what specific aspects the emotions or opinions relate to, such as a certain product feature or a new ad campaign.

If a customer writes that they “find the new Instagram advert so annoying”, your model should detect not only a negative sentiment, but also the object towards which it’s directed.

In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) algorithms and systems which are trained to associate certain inputs (for example, certain words) with certain outputs.

For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time!

Sentiment analysis in action: 5 Real-world sentiment analysis case studies

4. The data analysis process

In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process —but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question

The first step for any data analyst will be to define the objective of the analysis, sometimes called a ‘problem statement’. Essentially, you’re asking a question with regards to a business problem you’re trying to solve. Once you’ve defined this, you’ll then need to determine which data sources will help you answer this question.

Collecting the data

Now that you’ve defined your objective, the next step will be to set up a strategy for collecting and aggregating the appropriate data. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these data fit into first-party, second-party, or third-party data?

Learn more: Quantitative vs. Qualitative Data: What’s the Difference?

Cleaning the data

Unfortunately, your collected data isn’t automatically ready for analysis—you’ll have to clean it first. As a data analyst, this phase of the process will take up the most time. During the data cleaning process, you will likely be:

Removing major errors, duplicates, and outliers
Removing unwanted data points
Structuring the data—that is, fixing typos, layout issues, etc.
Filling in major gaps in data

Analyzing the data

Now that we’ve finished cleaning the data, it’s time to analyze it! Many analysis methods have already been described in this article, and it’s up to you to decide which one will best suit the assigned objective. It may fall under one of the following categories:

Descriptive analysis , which identifies what has already happened
Diagnostic analysis , which focuses on understanding why something has happened
Predictive analysis , which identifies future trends based on historical data
Prescriptive analysis , which allows you to make recommendations for the future

Visualizing and sharing your findings

We’re almost at the end of the road! Analyses have been made, insights have been gleaned—all that remains to be done is to share this information with others. This is usually done with a data visualization tool, such as Google Charts, or Tableau.

Learn more: 13 of the Most Common Types of Data Visualization

To sum up the process, Will’s explained it all excellently in the following video:

5. The best tools for data analysis

As you can imagine, every phase of the data analysis process requires the data analyst to have a variety of tools under their belt that assist in gaining valuable insights from data. We cover these tools in greater detail in this article , but, in summary, here’s our best-of-the-best list, with links to each product:

The top 9 tools for data analysts

Microsoft Excel
Jupyter Notebook
Apache Spark
Microsoft Power BI

6. Key takeaways and further reading

As you can see, there are many different data analysis techniques at your disposal. In order to turn your raw data into actionable insights, it’s important to consider what kind of data you have (is it qualitative or quantitative?) as well as the kinds of insights that will be useful within the given context. In this post, we’ve introduced seven of the most useful data analysis techniques—but there are many more out there to be discovered!

So what now? If you haven’t already, we recommend reading the case studies for each analysis technique discussed in this post (you’ll find a link at the end of each section). For a more hands-on introduction to the kinds of methods and techniques that data analysts use, try out this free introductory data analytics short course. In the meantime, you might also want to read the following:

The Best Online Data Analytics Courses for 2024
What Is Time Series Data and How Is It Analyzed?
What is Spatial Analysis?

Home » Research Data – Types Methods and Examples

Research Data – Types Methods and Examples

Table of Contents

Research Data

Research data refers to any information or evidence gathered through systematic investigation or experimentation to support or refute a hypothesis or answer a research question.

It includes both primary and secondary data, and can be in various formats such as numerical, textual, audiovisual, or visual. Research data plays a critical role in scientific inquiry and is often subject to rigorous analysis, interpretation, and dissemination to advance knowledge and inform decision-making.

Types of Research Data

There are generally four types of research data:

Quantitative Data

This type of data involves the collection and analysis of numerical data. It is often gathered through surveys, experiments, or other types of structured data collection methods. Quantitative data can be analyzed using statistical techniques to identify patterns or relationships in the data.

Qualitative Data

This type of data is non-numerical and often involves the collection and analysis of words, images, or sounds. It is often gathered through methods such as interviews, focus groups, or observation. Qualitative data can be analyzed using techniques such as content analysis, thematic analysis, or discourse analysis.

Primary Data

This type of data is collected by the researcher directly from the source. It can include data gathered through surveys, experiments, interviews, or observation. Primary data is often used to answer specific research questions or to test hypotheses.

Secondary Data

This type of data is collected by someone other than the researcher. It can include data from sources such as government reports, academic journals, or industry publications. Secondary data is often used to supplement or support primary data or to provide context for a research project.

Research Data Formates

There are several formats in which research data can be collected and stored. Some common formats include:

Text : This format includes any type of written data, such as interview transcripts, survey responses, or open-ended questionnaire answers.
Numeric : This format includes any data that can be expressed as numerical values, such as measurements or counts.
Audio : This format includes any recorded data in an audio form, such as interviews or focus group discussions.
Video : This format includes any recorded data in a video form, such as observations of behavior or experimental procedures.
Images : This format includes any visual data, such as photographs, drawings, or scans of documents.
Mixed media: This format includes any combination of the above formats, such as a survey response that includes both text and numeric data, or an observation study that includes both video and audio recordings.
Sensor Data: This format includes data collected from various sensors or devices, such as GPS, accelerometers, or heart rate monitors.
Social Media Data: This format includes data collected from social media platforms, such as tweets, posts, or comments.
Geographic Information System (GIS) Data: This format includes data with a spatial component, such as maps or satellite imagery.
Machine-Readable Data : This format includes data that can be read and processed by machines, such as data in XML or JSON format.
Metadata: This format includes data that describes other data, such as information about the source, format, or content of a dataset.

Data Collection Methods

Some common research data collection methods include:

Surveys : Surveys involve asking participants to answer a series of questions about a particular topic. Surveys can be conducted online, over the phone, or in person.
Interviews : Interviews involve asking participants a series of open-ended questions in order to gather detailed information about their experiences or perspectives. Interviews can be conducted in person, over the phone, or via video conferencing.
Focus groups: Focus groups involve bringing together a small group of participants to discuss a particular topic or issue in depth. The group is typically led by a moderator who asks questions and encourages discussion among the participants.
Observations : Observations involve watching and recording behaviors or events as they naturally occur. Observations can be conducted in person or through the use of video or audio recordings.
Experiments : Experiments involve manipulating one or more variables in order to measure the effect on an outcome of interest. Experiments can be conducted in a laboratory or in the field.
Case studies: Case studies involve conducting an in-depth analysis of a particular individual, group, or organization. Case studies typically involve gathering data from multiple sources, including interviews, observations, and document analysis.
Secondary data analysis: Secondary data analysis involves analyzing existing data that was collected for another purpose. Examples of secondary data sources include government records, academic research studies, and market research reports.

Analysis Methods

Some common research data analysis methods include:

Descriptive statistics: Descriptive statistics involve summarizing and describing the main features of a dataset, such as the mean, median, and standard deviation. Descriptive statistics are often used to provide an initial overview of the data.
Inferential statistics: Inferential statistics involve using statistical techniques to draw conclusions about a population based on a sample of data. Inferential statistics are often used to test hypotheses and determine the statistical significance of relationships between variables.
Content analysis : Content analysis involves analyzing the content of text, audio, or video data to identify patterns, themes, or other meaningful features. Content analysis is often used in qualitative research to analyze open-ended survey responses, interviews, or other types of text data.
Discourse analysis: Discourse analysis involves analyzing the language used in text, audio, or video data to understand how meaning is constructed and communicated. Discourse analysis is often used in qualitative research to analyze interviews, focus group discussions, or other types of text data.
Grounded theory : Grounded theory involves developing a theory or model based on an analysis of qualitative data. Grounded theory is often used in exploratory research to generate new insights and hypotheses.
Network analysis: Network analysis involves analyzing the relationships between entities, such as individuals or organizations, in a network. Network analysis is often used in social network analysis to understand the structure and dynamics of social networks.
Structural equation modeling: Structural equation modeling involves using statistical techniques to test complex models that include multiple variables and relationships. Structural equation modeling is often used in social science research to test theories about the relationships between variables.

Purpose of Research Data

Research data serves several important purposes, including:

Supporting scientific discoveries : Research data provides the basis for scientific discoveries and innovations. Researchers use data to test hypotheses, develop new theories, and advance scientific knowledge in their field.
Validating research findings: Research data provides the evidence necessary to validate research findings. By analyzing and interpreting data, researchers can determine the statistical significance of relationships between variables and draw conclusions about the research question.
Informing policy decisions: Research data can be used to inform policy decisions by providing evidence about the effectiveness of different policies or interventions. Policymakers can use data to make informed decisions about how to allocate resources and address social or economic challenges.
Promoting transparency and accountability: Research data promotes transparency and accountability by allowing other researchers to verify and replicate research findings. Data sharing also promotes transparency by allowing others to examine the methods used to collect and analyze data.
Supporting education and training: Research data can be used to support education and training by providing examples of research methods, data analysis techniques, and research findings. Students and researchers can use data to learn new research skills and to develop their own research projects.

Applications of Research Data

Research data has numerous applications across various fields, including social sciences, natural sciences, engineering, and health sciences. The applications of research data can be broadly classified into the following categories:

Academic research: Research data is widely used in academic research to test hypotheses, develop new theories, and advance scientific knowledge. Researchers use data to explore complex relationships between variables, identify patterns, and make predictions.
Business and industry: Research data is used in business and industry to make informed decisions about product development, marketing, and customer engagement. Data analysis techniques such as market research, customer analytics, and financial analysis are widely used to gain insights and inform strategic decision-making.
Healthcare: Research data is used in healthcare to improve patient outcomes, develop new treatments, and identify health risks. Researchers use data to analyze health trends, track disease outbreaks, and develop evidence-based treatment protocols.
Education : Research data is used in education to improve teaching and learning outcomes. Data analysis techniques such as assessments, surveys, and evaluations are used to measure student progress, evaluate program effectiveness, and inform policy decisions.
Government and public policy: Research data is used in government and public policy to inform decision-making and policy development. Data analysis techniques such as demographic analysis, cost-benefit analysis, and impact evaluation are widely used to evaluate policy effectiveness, identify social or economic challenges, and develop evidence-based policy solutions.
Environmental management: Research data is used in environmental management to monitor environmental conditions, track changes, and identify emerging threats. Data analysis techniques such as spatial analysis, remote sensing, and modeling are used to map environmental features, monitor ecosystem health, and inform policy decisions.

Advantages of Research Data

Research data has numerous advantages, including:

Empirical evidence: Research data provides empirical evidence that can be used to support or refute theories, test hypotheses, and inform decision-making. This evidence-based approach helps to ensure that decisions are based on objective, measurable data rather than subjective opinions or assumptions.
Accuracy and reliability : Research data is typically collected using rigorous scientific methods and protocols, which helps to ensure its accuracy and reliability. Data can be validated and verified using statistical methods, which further enhances its credibility.
Replicability: Research data can be replicated and validated by other researchers, which helps to promote transparency and accountability in research. By making data available for others to analyze and interpret, researchers can ensure that their findings are robust and reliable.
Insights and discoveries : Research data can provide insights into complex relationships between variables, identify patterns and trends, and reveal new discoveries. These insights can lead to the development of new theories, treatments, and interventions that can improve outcomes in various fields.
Informed decision-making: Research data can inform decision-making in a range of fields, including healthcare, business, education, and public policy. Data analysis techniques can be used to identify trends, evaluate the effectiveness of interventions, and inform policy decisions.
Efficiency and cost-effectiveness: Research data can help to improve efficiency and cost-effectiveness by identifying areas where resources can be directed most effectively. By using data to identify the most promising approaches or interventions, researchers can optimize the use of resources and improve outcomes.

Limitations of Research Data

Research data has several limitations that researchers should be aware of, including:

Bias and subjectivity: Research data can be influenced by biases and subjectivity, which can affect the accuracy and reliability of the data. Researchers must take steps to minimize bias and subjectivity in data collection and analysis.
Incomplete data : Research data can be incomplete or missing, which can affect the validity of the findings. Researchers must ensure that data is complete and representative to ensure that their findings are reliable.
Limited scope: Research data may be limited in scope, which can limit the generalizability of the findings. Researchers must carefully consider the scope of their research and ensure that their findings are applicable to the broader population.
Data quality: Research data can be affected by issues such as measurement error, data entry errors, and missing data, which can affect the quality of the data. Researchers must ensure that data is collected and analyzed using rigorous methods to minimize these issues.
Ethical concerns: Research data can raise ethical concerns, particularly when it involves human subjects. Researchers must ensure that their research complies with ethical standards and protects the rights and privacy of human subjects.
Data security: Research data must be protected to prevent unauthorized access or use. Researchers must ensure that data is stored and transmitted securely to protect the confidentiality and integrity of the data.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Quantitative Data – Types, Methods and Examples

Qualitative Data – Types, Methods and Examples

Information in Research – Types and Examples

Secondary Data – Types, Methods and Examples

Primary Data – Types, Methods and Examples

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful.

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

qualitative data analysis vs quantitative data analysis

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

Content analysis
Narrative analysis
Discourse analysis
Thematic analysis
Grounded theory (GT)
Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

Thematic analysis takes bodies of data and groups them according to similarities (themes), which help us make sense of the content.

QDA Method #5: Grounded theory (GT)

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name).

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

Grounded theory is used to create a new theory (or theories) by using the data at hand, as opposed to existing theories and frameworks.

QDA Method #6: Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

IPA can help you understand the personal experiences of a person or group concerning a major life event, an experience or a situation.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant.

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

No single analysis method is perfect, so it can often make sense to adopt more than one method (this is called triangulation).

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
Then we looked at narrative analysis , which is about analysing how stories are told.
Next up was discourse analysis – which is about analysing conversations and interactions.
Then we moved on to thematic analysis – which is about identifying themes and patterns.
From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

87 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

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Thanks madam . It is very important .

thank you very good

Great presentation

This has been very well explained in simple language . It is useful even for a new researcher.

Great to hear that. Good luck with your qualitative data analysis, Pramod!

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

Thank you so much.

very informative sequential presentation

Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

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i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

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Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

The session was very helpful and insightful. Thank you

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As a professional academic writer, this has been so informative and educative. Keep up the good work Grad Coach you are unmatched with quality content for sure.

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Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

This is Amazing and well explained, thanks

great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

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Very helpful.

very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

This was helpful. Thank you

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

The tutorial is useful. I benefited a lot.

This is an eye opener for me and very informative, I have used some of your guidance notes on my Thesis, I wonder if you can assist with your 1. name of your book, year of publication, topic etc., this is for citing in my Bibliography,

I certainly hope to hear from you

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Types of Data Analysis Techniques

Data analysis techniques have significantly evolved, providing a comprehensive toolkit for understanding, interpreting, and predicting data patterns. These methods are crucial in extracting actionable insights from data, enabling organizations to make informed decisions.

Data Analysis Techniques

This article will cover major Types of Data Analysis Techniques , each tailored to specific goals and data types.

Descriptive Data Analysis

Qualitative data analysis, predictive data analysis, diagnostic data analysis, regression analysis, cohort analysis, factor analysis, time series analysis, cluster analysis.

Descriptive analysis is considered the beginning point for the analytic journey and often strives to answer questions related to what happened. This technique follows ordering factors, manipulating and interpreting varied data from diverse sources, and then turning it into valuable insights.

In addition, conducting this analysis is imperative as it allows individuals to showcase insights in a streamlined method. This technique does not allow you to estimate future outcomes – such as identifying specific reasoning for a particular factor. It will keep your data streamlined and simplify to conduct a thorough evaluation for further circumstances.

Examples of Descriptive Data Analysis :

Sales Performance: A retail company might use descriptive statistics to understand the average sales volume per store or to find which products are the best sellers.
Customer Satisfaction Surveys: Analyzing survey data to find the most common responses or average scores.

Qualitative data analysis techniques cannot be measured directly, and hence, this technique is utilized when an organization needs to make decisions based on subjective interpretation. For instance, qualitative data can involve evaluating customer feedback, the impact of survey questions, the effectiveness of social media posts, analyzing specific changes or features of a product, and more.

The focus of this technique should be identifying meaningful insights or answers from unstructured data such as transcripts, vocal feedback, and more. Additionally, qualitative analysis aids in organizing data into themes or categories, which can be further automated. Quantitative data analysis refers to measurable information, which includes specific numbers and quantities. For instance, sales figures, email campaigns based on click-through rates, website visitors, employee performance percentage, or percentage for revenue generated, and more.

Examples of Qualitative Data Analysis:

Market Analysis: A business might analyze why a product’s sales spiked in a particular quarter by looking at marketing activities, price changes, and market trends.
Medical Diagnosis: Clinicians use diagnostic analysis to understand the cause of symptoms based on lab results and patient data.

Predictive data analysis enables us to look into the future by answering questions— what will happen? Individuals need to utilize the results of descriptive data analysis, exploratory and diagnostic analysis techniques, and combine machine learning and artificial intelligence . Using this method, you can get an overview of future trends and identify potential issues and loopholes in your dataset.

In addition, you can discover and develop initiatives to enhance varied operation processes and your competitive edge with insightful data. With easy-to-understand insights, businesses can tap into trends, common patterns, or reasons for a specific event, making initiatives or decisions for further strategies easier.

Examples of Predictive Data Analysis:

Credit Scoring: Financial institutions use predictive models to assess a customer’s likelihood of defaulting on a loan.
Weather Forecasting: Meteorologists use predictive models to forecast weather conditions based on historical weather data.

When you know why something happened, it is easy to identify the “How” for that specific aspect. For instance, with diagnostic analysis , you can identify why your sales results are declining and eventually explore the exact factors that led to this loss.

In addition, this technique offers actionable answers to your specific questions. It is also the most commonly preferred method in research for varied domains.

Example of Diagnostic Data Analysis:

Inventory Analysis: Checking if lower sales correlate with stock outs or overstock situations.
Promotion Effectiveness: Analyzing the impact of different promotional campaigns to see which failed to attract customers.

This method utilizes historical data to understand the impact on the dependent variable’s value when one or more independent variables tend to change or remain the same. In addition, determining each variable’s relationship and past development or initiative enables you to predict potential outcomes in the future. And the technique gives you the right path to make informed decisions effectively.

Let’s assume you conducted a Regression Analysis for your sales report in 2022, and the results represented variables like customer services, sales channels, marketing campaigns, and more that affected the overall results. Then, you can conduct another regression analysis to check if the variables changed over time or if new variables are impacting your sales result in 2023. By following this method, your sales can increase with improved product quality or services

Example of Regression Analysis:

Market Trend Assessment: Evaluating how changes in the economic environment (e.g., interest rates) affect property prices.
Predictive Pricing: Using historical data to predict future price trends based on current market dynamics.

Cohort analysis includes historical data to analyze and compare specific segments in user behavior and groups a few aspects with other similar elements. This technique can provide an idea of your customer’s and target audience’s evolving needs.

In addition, you can utilize Cohort analysis to determine a marketing campaign’s impact on certain audience groups. For instance, you can implement the features of the Cohort analysis technique to evaluate two types of email campaigns—commonly termed A/B Testing over time—and understand which variation turned out to be responsive and impactful in terms of performance.

Example of Cohort Analysis:

Customer Retention: Measuring how long newly acquired customers continue to make purchases compared to those not enrolled in the loyalty program.
Program Impact: Determining if and how the loyalty program influences buying patterns and average spend per purchase.

Factor data analysis defines the variations with observed related variables based on lower unobserved variables termed factors. In short, it helps in extracting independent variables, which is considered ideal for optimizing specific segments.

For instance, if you have a product and collect customer feedback for varied purposes, this analysis technique aids in focusing on specific factors like current trends, layout, product performance, potential errors, and more. The factors can vary depending on what you want to monitor and focus on. Lastly, factor analysis simplifies summarizing related factors in similar groups.

Example of Factor Analysis :

Service Improvement: Identifying key factors such as wait time, staff behavior, and treatment outcome that impact patient satisfaction.
Resource Allocation: Using these insights to improve areas that significantly affect patient satisfaction.

A time series analysis technique checks data points over a certain timeframe. You can utilize this method to monitor data within a certain time frame on a loop; however, this technique isn’t ideal for collecting data only in a specific time interval.

Sounds confusing? This technique is ideal for determining whether the variable changed amid the evaluation interval, how each variable is dependent, and how the result was achieved for a specific aspect. Additionally, you can rely on time series analysis to determine market trends and patterns over time. You can also use this method to forecast future events based on certain data insights.

Example of Time Series Analysis :

Demand Forecasting: Estimating sales volume for the next season based on historical sales data during similar periods.
Resource Planning: Adjusting production schedules and inventory levels to meet anticipated demand.

Cluster analysis describes data and identifies common patterns. It is often used when data needs more evident labels or insights or has ambiguous categories. This process includes recognizing similar observations and grouping those aspects to create clusters, which means assigning names and categorizing groups.

In addition, this technique aids in identifying similarities and disparities in databases and presenting them in a visually organized method to seamlessly compare factors. Box plot visualization is mainly preferred to showcase data clusters.

Example of Cluster Analysis:

Market Segmentation: Dividing customers into groups that exhibit similar behaviors and preferences for more targeted marketing.
Campaign Customization: Designing unique marketing strategies for each cluster to maximize engagement and conversions.

Each method offers unique benefits and is suited to different types of data challenges. Understanding and applying the right data analysis techniques can significantly impact an organization’s strategy and decision-making processes, leading to more targeted, efficient, and effective outcomes.

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Published on 20.8.2024 in Vol 8 (2024)

This is a member publication of Florida State University

Effect of the Implementation of a Multiple-Behavior Self-Monitoring Intervention on Dietary Intake in Type 2 Diabetes: Secondary Data Analysis

Authors of this article:

Original Paper

Jisook Ko 1 , PhD ;
Jing Wang 2 , MPH, PhD ;
Ngozi Mbue 3 , PhD ;
Susan Schembre 4 , PhD ;
Stanley Cron 5 , MSPH

1 UT Health San Antonio, San Antonio, TX, United States

2 Florida State University, Tallahassee, FL, United States

3 Texas Women's University, Fort Sam Houston, TX, United States

4 Georgetown University, Washington, DC, United States

5 The University of Texas Health Science Center at Houston, Houston, TX, United States

Corresponding Author:

Jing Wang, MPH, PhD

Florida State University

98 Varsity Way

Tallahassee, FL, FL32306

United States

Phone: 1 850 644 3299

Email: [email protected]

Background: An electronic diary embedded in a mobile device to monitor lifestyle can be as effective as traditional methods. However, the efficacy of self-monitoring multiple behaviors for dietary intake has not been well studied in people with diabetes.

Objective: This study aimed to compare the effect of using technology-assisted self-monitoring versus paper diaries on changes in dietary intake.

Methods: This is a secondary analysis of data collected from 39 people with type 2 diabetes as part of a 3-month pilot clinical trial. Changes in energy intake and the contribution of total fat intake and total carbohydrate intake to total calories (%) from baseline to after intervention (3 months) were evaluated.

Results: In total, 26 (67%) of the 39 participants preferred mobile diaries over paper diaries. Participants in the mobile diary group showed slightly higher self-monitoring adherence. Linear mixed modeling results indicated a significant overall decrease in total energy intake ( P =.005), dietary fat intake ( P =.01), and carbohydrate intake ( P =.08) from baseline to 3 months. No significant group differences were detected ( P >.05).

Conclusions: The implementation of a 3-month, multiple-behavior, self-monitoring intervention in Diabetes Self-Management Education programs has resulted in successful reduction in dietary intake (energy, fat, and carbohydrate), whichever self-monitoring method is chosen by participants according to their preferences. Long-term studies are needed to confirm our findings on dietary intake and examine other behavioral and disease outcomes that require monitoring.

Introduction

Self-monitoring is an essential strategy used in behavioral lifestyle interventions for type 2 diabetes mellitus (T2DM) and obesity [ 1 , 2 ]. Diet, physical activity, weight, and blood glucose self-monitoring are effective in managing diabetes and preventing diabetes-related complications [ 3 , 4 ]. Targeting multiple behaviors rather than only single behaviors, self-monitoring allows the skills and knowledge learned for one behavior to be transferred to other behaviors [ 5 ], resulting in improvements across multiple behaviors [ 6 ]. In particular, individuals who would like to lose weight might be interested in sustaining diet and physical activity self-monitoring. Self-monitoring significantly mediates adherence to behavioral lifestyle interventions, including dietary behavior [ 7 ]. Traditionally, self-monitoring was performed using paper-based diaries. This paper-based method posed several challenges, including being time- and labor-intensive and requiring extensive numeracy and literacy skills, thus decreasing adherence to food logging over time and leading to low compliance [ 8 ]. Although more expensive than paper diaries, smartphones and mobile health devices became widely available, and self-monitoring using mobile devices became an appealing strategy supporting successful T2DM self-management [ 9 , 10 ]. This approach offers several advantages in enhancing the overall management of T2DM including enabling real-time monitoring, data accessibility to their self-monitoring data, behavioral change support, and patient engagement. In addition, self-monitoring using mobile devices reduces the burden and time effort of patients for recording daily activities related to T2DM self-management [ 9 - 11 ].

Most clinical trials designed to test the effect of a lifestyle intervention for T2DM randomly assigned the use of either a mobile diary or a paper-based method of self-monitoring; however, they rarely address patient preferences for how paper-based versus mobile diary methods influence outcomes of lifestyle intervention in T2DM. A recent study by our group found that an electronic diary embedded in a mobile device to monitor blood glucose can be as effective as the traditional method and is more likely to be used by participants than paper diaries [ 7 ]. However, no study has examined the efficacy of self-monitoring multiple behaviors on dietary behaviors.

Thus, we conducted a pilot study to test the feasibility of implementing a multiple-behavior self-monitoring intervention as an adjunct to a diabetes education program and tested the use of electronic or paper diaries to facilitate the patient self-monitoring process. The behavioral intervention used in this study was guided by the self-regulation theory, emphasizing the role of self-monitoring preceding self-awareness and self-regulation, and the social learning theory. This study aimed to examine the impact of the 3-month, multiple-behavior, self-monitoring intervention on patient dietary behaviors and whether using electronic diaries to facilitate self-monitoring would be more effective than paper diaries.

This is a secondary analysis of data collected as part of a 3-month intervention implementation study. Study participants were offered the opportunity to self-monitor multiple health behaviors using either a mobile app for their smartphone or tablet (mobile diary) or a paper diary. This study aimed to (1) assess the preference for using a mobile diary over a paper diary and (2) compare the efficacy of using mobile versus paper diaries on changes in dietary intake. The primary outcomes of this study were changes in energy intake and the contribution of total fat intake and total carbohydrate intake to total calories (%) from baseline to after intervention (3 months). Secondary outcomes included changes in the consumption of sugars, dietary fiber, protein, and the total number of fruits and vegetables.

Sample, Setting, and Recruitment

We posted flyers with information about the parent study at accredited Diabetes Self-Management Education programs at 3 partnering hospital systems or diabetes clinics in Houston, Texas. We invited those attending the Diabetes Self-Management Education group classes or individual visits to join the study before or after their sessions. Participants had the opportunity to speak with research staff about the study at the class site or could leave their contact information with the diabetes educators so research staff could contact them later.

Inclusion criteria for this study entailed (1) the ability to speak, read, and write English; (2) aged between 21 and 74 years; (3) having T2DM; (4) owning a smartphone compatible with the Jawbone UP24 fitness tracker (Jawbone Company); and (5) currently using a glucometer to monitor blood glucose levels. The exclusion criterion was current treatment for any severe psychiatric illness. We enrolled a total of 45 participants.

Study Procedures

All participants who expressed an interest in participation completed an initial screening form. The screening form contained questions about study eligibility criteria and mobile phone use. Specifically, we asked participants whether they had a mobile phone, the make or model of the phone, and the type of phone plan (ie, data and SMS text messaging) that would support them in using the phone with the Jawbone app appropriately. We asked participants who met eligibility requirements to provide informed consent and provided questionnaire survey books to complete at home. Research personnel then scheduled a second meeting with the participant to collect the survey book, complete weekday and weekend dietary recalls over the phone or during the meeting, and deliver a single face-to-face intervention session.

Intervention

Upon enrollment into the study, trained study staff provided participants with instructions on how to self-monitor multiple health behaviors for the duration of the parent intervention study. We modified the self-monitoring protocols from the Group Lifestyle Balance program and the Look AHEAD trial. Specifically, we provided participants with a digital scale to monitor their body weight daily, a pedometer to track the number of steps taken per day, measuring cups and a food scale to weigh their food and estimate portion size, and a behavioral lifestyle intervention guide with tips on how to improve their diets and increase their physical activity. Additionally, our trained interventionists educated participants on the contributions of diet and physical activity to energy balance as it relates to weight loss. With the interventionist, participants set weight loss goals ranging from 0.5 pounds per week to 2 pounds per week and specific dietary and physical activity goals. From these goals, a calorie allowance was determined based on the body weight, and the interventionist counseled the study participants on the impact of calorie, dietary fat, and carbohydrate intake on weight changes and blood glucose levels. Following the face-to-face session, research personnel followed up with each participant at 1 and 6 weeks to answer any participant questions regarding the instructions. The research team made themselves available to discuss questions and concerns on an ongoing basis throughout the 3-month study.

Depending on participant preference and smartphone ownership, we instructed participants to monitor their diet, physical activity, weight, and blood glucose daily. Participants had the option to self-monitor with a smartphone or a paper diary. We instructed smartphone owners who preferred to use their smartphone for self-monitoring to download 2 free smartphone apps: Lose It! (FitNow, Inc), with functions for logging diet, physical activity, and weight, and the Glucose Buddy (SkyHealth LLC), with functions for logging blood glucose. We selected apps that were free of charge and available on iOS and Android platforms. During the intervention session, participants received training on self-monitoring their dietary and physical activity habits and their weight and blood glucose using these 2 apps. Both apps track the foods by searching for foods in a food database, scanning the barcode on food labels, or snapping a picture (only Lose it!). Participants could constantly monitor their calorie intake through these apps.

We provided participants who owned a smartphone but preferred to self-monitor with a paper diary with the supplies to log their dietary and physical activity behaviors, weight, and blood glucose levels by hand. The study team designed the paper diaries to record the daily and weekly diet summary (eating time, portion size, calorie, fat gram, and carbohydrate content), physical activity (time, type, and duration of physical activity), weight, and blood glucose values. We provided participants using the paper diaries with an updated Calorie King book to estimate calorie and macronutrient content.

Study Measures

Participant characteristics.

At baseline, we administered a sociodemographic questionnaire that captured participants’ age, gender, race, marital status, education, employment status, and years of having diabetes.

Dietary Intake

We assessed dietary intake at baseline and 3 months after intervention with the Automated Self-Administered 24-hour Dietary Recall-2014 (ASA24), which is freely available for use by researchers through the National Cancer Institute [ 12 ]. The ASA24 is based on a modified version of the interviewer-administered Automated Multiple Pass Method 24-hour Recall developed by the US Department of Agriculture [ 12 - 14 ]. The ASA24 was found reliable as the standard interviewer-administered dietary recall in collecting consumed foods with lower attrition rates [ 14 , 15 ]. Analytic output can be requested from the Researcher Website. Data dictionaries and samples of the output files are available for download from the Version 1 Researcher Website and the ASA24 Portal. We used this approach in the previous 2 studies. Trained research personnel (phone or face-to-face) interviewed participants at each study time point (baseline and after intervention) and 1 weekday and 1 weekend 24-hour recall. Research personnel entered the data directly into the ASA24 portal during the interview. We averaged the 2 days of recall data to represent the usual dietary intake at baseline and postintervention. We calculated the total energy intake (kcal) and the contribution of dietary fat, protein, and carbohydrates to total energy intake (%). We estimated the energy-adjusted intakes of sugar (g/1000 kcal) and dietary fiber (g/1000 kcal) and the total number of servings of fruit and vegetables.

Sample Size

Using the effect size on weight loss from a self-monitoring focused intervention in obesity [ 13 ] and assuming 80% retention, we estimated that at least 40 participants (n=20 per group) were needed to detect a between-group (mobile vs paper diary) effect size of d =1.0 with 80% power.

Statistical Analysis

We performed descriptive statistics on nutrition intakes for the mobile diary group and paper diary group on data collected at baseline and after intervention (3 months). We used repeated measures analysis with linear mixed models to compare the changes in nutrient intakes over time between the groups. Total energy intake, dietary fat, sugars, and the total number of fruits were nonnormally distributed and, thus, transformed to a natural log scale to better approximate a normal distribution. Statistical analyses were performed using SAS for Windows (version 9.4; SAS Institute).

Ethical Considerations

The institutional review board approved the study at The University of Texas Health Science Center in Houston and partnering hospitals (application: HSC-SN-14-1027). Participants had to read and sign a consent form confirming that they were 18 years of age or older and that they consented to participate. All personal information was to be anonymized for data processing; hence, a participant identification number was required.

Sample Demographics

We enrolled 45 participants from 3 partnering hospital systems or clinics in the 3-month study. At 3 months, only 39 participants completed the ASA24. Based on smartphone ownership and preferences, about 67% (n=26) were in the mobile diary group and 13 (33%) were in the paper diary group. Four participants were lost to follow-up between the baseline visit and the intervention session, and 2 were asked to withdraw from the study (1 for a medical reason and 1 for an unknown reason).

All study participants were 21 years or older (range 28-77 years), had T2DM, and used a glucometer to monitor their blood glucose levels. Table 1 presents the sociodemographic information and diabetes history.

Characteristic			Total (n=39)		Mobile diary (n=26)		Paper diary (n=13)	Chi-square or test ( =38)
Sex (female), n (%)			29 (74)		16 (62)		13 (100)	6.72
									4.49
	Asian or Pacific Islander	4 (10)		1 (4)		3 (23)
	Black or African American	13 (33)		8 (31)		5 (39)
	White	15 (39)		13 (50)		2 (15)
	Mixed race	7 (18)		4 (15)		3 (23)

	Mean (SD)	58.2 (11.4)		57.6 (12.2)		59.4 (9.8)		0.45
	Adults ≥60 years, n (%)	22 (57)		11 (42)		11 (85)		0.03
									1.30
	Currently married	17 (44)		13 (50)		4 (31)
	Divorced or widowed or never married	22 (56)		13 (50)		9 (69)
Years of formal education, mean (SD)			15.8 (3.4)		16.1 (3.4)		15.3 (3.3)	0.70
Employed full time, n (%)			23 (61)		15 (58)		8 (67)	0.28
Years living with diabetes, mean (SD)			11.3 (7.5)		11.9 (8.0)		9.8 (6.2)	0.68

a P <.05.

b Chi-square test.

Adherence to Self-Monitoring by the Number of Diaries Completed

Of the 39 participants (n=26, 67% for the mobile diary group and n=13, 33% for the paper diary group) who received the intervention, 68% (n=26) completed 2 dietary recalls (1 weekday and 1 weekend) at baseline and 3 months ( Table 2 ). Although there were no significant differences in adherence to self-monitoring by the diary type (mobile diary vs paper diary), the total number of completed diaries was greater in the mobile diary group (mean 1.6, SD 1.0) compared to the paper diary group (mean 1.2, SD 0.6).

Variable			Total (n=39)		Mobile diary (n=26)		Paper diary (n=13)	Chi-square or test ( =38)
									5.92
	2 weekdays and 2 weekends (baseline and 3 months)	26 (68)		15 (60)		11 (85)
	1 weekday and 1 weekend (baseline only)	8 (21)		7 (28)		1 (8)
	1 weekday and 1 weekend (3 months only)	1 (3)		0 (0)		1 (8)
	2 weekdays and 1 weekend (mixed)	3 (8)		3 (12)		0 (0)
Number of completed diaries, mean (SD)			1.5 (0.9)		1.6 (1.0)		1.2 (0.6)	1.36

a Chi-square test.

Nutrition Intake Outcomes

Dietary intake data are presented in Table 3 . Initially, participants using the mobile diary reported consuming an average total energy intake of 1814.3 (SD 753.9; range 501.6-3930.9) kcal, whereas those using the paper diary reported an average of 1664.1 (SD 845.1; range 505.4-5536.9) kcal. The average total energy intake was significantly higher in the mobile diary group compared to the paper diary group (t 38 =7.39; P =.05). At the end of the 3-month intervention, participants in the mobile diary group reported an average of 1664.0 (SD 563.4; range 724.6-3336.4) kcal of total energy intake, while those in the paper diary group reported an average of 1093.4 (SD 753.9; range 259.7-2399.0) kcal. The data indicate that dietary fat, carbohydrates, and sugar lower after the intervention compared with baseline ( Table 3 ).

Nutrients	Mobile diary		Paper diary		value	value
Nutrients	Baseline	3 Months	Baseline	3 Months
Energy (kcal), n (%)	1814.3 (100)	1664.1 (100)	1664.1 (100)	1093.4 (100)	.	.08
Dietary fat (% of total intake), n (%)	84.9 (24.7)	72.4 (22.6)	73.8 (22.4)	49.0 (22.4)	.	.39
Carbohydrates (% of total intake), n (%)	188.6 (53.5)	178.8 (54.7)	180.8 (54.)	108.1 (51.2)	.	.13
Protein (% of total intake), n (%)	73.0 (21.8)	73.6 (22.7)	71.6 (23.6)	58.5 (26.4)	.33	.33
Dietary fiber (g/1000 kcal), mean (SD)	7.7 (2.6)	8.8 (4)	8.9 (5.4)	10.5 (8.1)	.09	.18
Sugars (g/1000 kcal), mean (SD)	37.2 (23.9)	41.5 (26.6)	44.6 (28.8)	32.6 (22.0)	.	.06
Total vegetables, mean (SD)	1.3 (1.1)	1.1 (0.8)	1.4 (1.1)	1.2 (0.8)	.23	.91
Total fruits, mean (SD)	0.7 (1.1)	0.9 (1.1)	1.1 (1.8)	0.5 (0.7)	.30	.28

a Before and after intervention comparison regardless of group.

b The italic values indicate that dietary fat, carbohydrates, and sugar lower after intervention compared with baseline.

c Between groups change over time.

d % is the contribution to total energy intake.

e Cup equivalent.

Linear mixed modeling results showed a significant overall decrease in total energy intake ( P =.005), dietary fat intake ( P =.01), carbohydrate intake ( P =.008), and sugar intake ( P =.04) from baseline to 3 months. However, we detected no significant group differences ( P >.05). The results for the interaction of group and visits ( P =.08) for total energy intake indicated a nonsignificant trend for a greater decrease between visits for the paper group. The results of the analyses on total sugar intake show a significant trend for an overall decrease in sugar intake ( P =.04) and a nonsignificant trend for a more significant decrease in sugars reported between visits for the paper group ( P =.06). The results of protein, fibers, total vegetables, and total fruit did not show significant differences between the 2 groups or between study visits.

Principal Findings

Traditionally, behavioral lifestyle interventions incorporating self-monitoring as a behavior change technique use paper diaries to record multiple health behaviors. Advancements in smartphone technology with freely available mobile apps now enable self-monitoring. A few trials examining behavior changes using smartphone apps to self-monitor lifestyle modification enhanced behavioral weight loss [ 16 , 17 ], diabetic outcomes [ 10 ], and metabolic outcomes [ 18 ]. However, the efficacy of smartphone apps versus paper diaries has not been tested in the context of consideration for user preference in the type of dietary self-monitoring diaries. As such, this 3-month behavioral lifestyle intervention study implemented in a diabetes education program comparatively examined the effects of smartphone apps and paper diaries to self-monitor multiple lifestyle behaviors on changes in selected dietary outcomes.

Our demographic analyses indicated that participants were primarily female (n=29/39, 74.4%) and older adults aged 60 years or older (n=22/39, 56.5%). Most of the study participants (n=26/39, 66.7%) preferred using a mobile diary, including smartphone apps, for self-monitoring multiple behaviors. Current literature reports that older adults use their smartphones primarily for retrieving information or using it as a classic phone [ 19 ]; however, this study demonstrated a relatively high preference for using a smartphone among older adults (n=11, 55%). We found general acceptance for the mobile diary apps but observed sex differences. Female participants were more likely to use a paper diary, while male participants preferred a mobile diary. There was a lack of awareness of mobile diary apps among female participants and a lack of interest by older female participants.

Compared to paper diaries, participants who used smartphone apps for self-monitoring showed slightly higher adherence to self-monitoring through the number of completed diaries. Given that little is known about the extent to which people adhere over time, the study used the number of diaries completed over a 3-month period to measure self-monitoring adherence. Assessment bias is one of the methodological challenges in using self-monitoring in behavioral lifestyle interventions due to the lack of defined self-monitoring adherence [ 17 ]. The use of technology and electronic devices that date- and timestamp the self-monitoring behavior (the diary entry) provides an objective validation of these self-reported behaviors [ 7 , 20 ]. Previous studies consistently demonstrate that mobile and web-based health apps increased adherence and promoted behavior changes [ 21 , 22 ]. However, we found no significant findings for protein, fiber, total fruit, and vegetable intake. This study is the first attempt in a sample of patients with T2DM to examine the use of multiple-behavior self-monitoring on nutritional outcomes. Previous researchers focused on only obese populations and included diet and physical activity self-monitoring as part of the intervention. These findings suggest that smartphone apps are as effective as paper diaries in facilitating self-monitoring of multiple health behaviors in diabetes management interventions.

There are several study limitations. First, the sample had well-controlled T2DM, which may not represent the general diabetes population with comorbid overweight or obesity. Second, due to the small sample size and the self-reported data collection, further studies with larger sample sizes are needed to improve the validity and generalizability of the results. Third, participants indeed self-selected their groups, so it may produce the differences due to allocation bias, and not necessarily due to mobile versus paper intervention. Therefore, it needs caution in generalizing the results, and further research with a randomized design is warranted to establish a more robust causal relationship between the intervention type (mobile vs paper) and the observed outcomes. Finally, we used the ASA24 during a phone interview recall rather than participants performing the recall with the web-based format, which deviates from the original intent of ASA24; however, we determined that using the ASA 24 during a phone interview was a more robust way to collect dietary recall to improve accuracy and validity of dietary recall assessment.

Conclusions

Implementing a 3-month, multiple-behavior, self-monitoring intervention in Diabetes Self-Management Education programs resulted in meaningful dietary intake on energy, fat, and carbohydrate intake, using whichever self-monitoring method participants chose according to their preferences. Long-term studies are needed to confirm our findings on dietary intake and examine other behavioral and disease outcomes that require monitoring.

Conflicts of Interest

None declared.

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Abbreviations

Automated Self-Administered 24-hour Dietary Recall-2014

type 2 diabetes mellitus

Edited by A Mavragani; submitted 21.07.23; peer-reviewed by K Mizokami-Stout, F Van der Ouderaa, S Rama Chandran; comments to author 08.11.23; revised version received 19.12.23; accepted 14.03.24; published 20.08.24.

©Jisook Ko, Jing Wang, Ngozi Mbue, Susan Schembre, Stanley Cron. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

Long-term annual mapping and spatial–temporal dynamic analysis of winter wheat in Shandong Province based on spatial–temporal data fusion (2000–2022)

Published: 20 August 2024
Volume 196 , article number 826 , ( 2024 )

Cite this article

Jinchang Zhao 1 ,
Xiaofang Sun 1 ,
Meng Wang 1 ,
Guicai Li 2 &
Xuehui Hou 3

Winter wheat, as one of the world’s key staple crops, plays a crucial role in ensuring food security and shaping international food trade policies. However, there has been a relative scarcity of high-resolution, long time-series winter wheat maps over the past few decades. This study utilized Landsat and Sentinel-2 data to produce maps depicting winter wheat distribution in Google Earth Engine (GEE). We further analyzed the comprehensive spatial–temporal dynamics of winter wheat cultivation in Shandong Province, China. The gap filling and Savitzky-Golay filter method (GF-SG) was applied to address temporal discontinuities in the Landsat NDVI (Normalized Difference Vegetation Index) time series. Six features based on phenological characteristics were used to distinguish winter wheat from other land cover types. The resulting maps spanned from 2000 to 2022, featuring a 30-m resolution from 2000 to 2017 and an improved 10-m resolution from 2018 to 2022. The overall accuracy of these maps ranged from 80.5 to 93.3%, with Kappa coefficients ranging from 71.3 to 909% and F1 scores from 84.2 to 96.9%. Over the analyzed period, the area dedicated to winter wheat cultivation experienced a decline from 2000 to 2011. However, a notable shift occurred with an increase in winter wheat acreage observed from 2014 to 2017 and a subsequent rise from 2018 to 2022. This research highlights the viability of using satellite observation data for the long-term mapping and monitoring of winter wheat. The proposed methodology has long-term implications for extending this mapping and monitoring approach to other similar areas.

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This study is supported by the project of the National Key R&D Program of China (No. 2021YFB3901303), Agricultural Science and Technology Innovation Foundation of Shandong Academy of Agriculture Sciences, China (No. GXGC2021A26), the Natural Science Foundation of Shandong Province, China (No. ZR2022MD022, ZR2020MD021), and the Natural Science Foundation of Rizhao, China (No. RZ2021ZR14).

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Jinchang Zhao, Xiaofang Sun & Meng Wang

China Meteorological Administration, National Satellite Meteorological Center, China, Beijing, 100101, China

Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan, 250100, Shandong, China

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J.C.Z and X.F.S were responsible for paper writing, figure production, data processing, and data analysis. M.W, G.C.L and X.H.H worked mainly on data analysis and discussion of revised articles. All authors reviewed the manuscript.

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Zhao, J., Sun, X., Wang, M. et al. Long-term annual mapping and spatial–temporal dynamic analysis of winter wheat in Shandong Province based on spatial–temporal data fusion (2000–2022). Environ Monit Assess 196 , 826 (2024). https://doi.org/10.1007/s10661-024-12971-x

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BroadbandNow Research selected 11 large ISPs with “check availability” tools. These tools allow users to enter an address and receive a response as to whether wired and/or fixed wireless service is available.
From there, we selected nearly 65,000 addresses (from a set of more than 1 million) where at least one of these 11 ISPs offer service according to the FCC’s availability data.
We checked each address/provider combination, totaling 113,686 address-provider checks. We found that for 22.1 percent of combinations, the ISP’s tool indicates service is not available.
Some addresses are serviced by more than one of the 11 large ISPs according to FCC data. For 1.27 percent of addresses, none of the ISPs’ tools indicate service is available

Key Findings

The FCC claimed that roughly 21 million Americans still had no access to a broadband internet connection meeting the 25 Mbps download / 3 Mbps upload threshold as of the time we generated the data used in this study. Based on our findings, we estimate that an additional million Americans do not have service, totalling 22 million.
Texas has the most Americans without broadband in total, with 1.8 million lacking access to a 25/3 connection.
While all technologies are overstated in terms of coverage, DSL and fixed wireless were the worst offenders in the sample set, at 53% and 58%, respectively.

Estimating Broadband Availability By State

BroadbandNow Research checked more than 1,000 addresses manually in each state, except for Alaska and Hawaii, which we have excluded. Below is a map of unserved Americans by state, comparing FCC data to our manual checks across the country.

Overreporting by State

Although overreporting varies widely from state to state, it exists across the country. This is significant, as states are due to begin making decisions about where and how to deploy the millions of dollars they will be receiving through BEAD, and they will be utilizing this overstated data to do so. Below is a table showing the FCC’s estimate of residents without broadband access, compared to BroadbandNow’s estimate.

State	Overreporting Percent	FCC’s Unserved Americans Estimate	BBN Unserved Americans Estimate	Population
Alabama	6%	1,054,706	1,116,590	4,902,000
Arizona	9%	314,833	342,696	7,279,000
Arkansas	6%	544,051	576,524	3,018,000
California	6.7%	1,199,574	1,279,378	39,538,223
Colorado	6%	201,186	214,223	5,758,000
Connecticut	17%	76,558	89,489	3,605,944
Delaware	13%	4,645	5,252	974,000
Florida	7%	1,232,869	1,321,232	21,477,000
Georgia	6%	928,510	988,274	10,614,000
Idaho	0.3%	114,315	114,708	1,787,000
Illinois	1%	403,130	408,888	12,672,000
Indiana	3.7%	391,568	406,128	6,732,000
Iowa	1%	126,019	127,565	3,155,000
Kansas	1.3%	96,772	98,111	2,913,000
Kentucky	5.5%	707,590	747,089	4,468,000
Louisiana	6%	800,897	848,193	4,649,000
Maine	6.6%	150,593	160,559	1,344,000
Maryland	4%	115,444	120,071	6,046,000
Massachusetts	39%	86,020	119,702	6,892,503
Michigan	11%	968,282	1,073,869	9,986,857
Minnesota	0.8%	286,902	289,255	5,700,671
Mississippi	2%	799,885	819,252	2,975,000
Missouri	1.6%	428,228	435,304	6,136,000
Montana	3.8%	229,468	238,169	1,069,000
Nebraska	2.5%	106,766	109,455	1,934,000
Nevada	4.7%	48,171	50,441	3,080,000
New Hampshire	16%	91,254	106,298	1,360,000
New Jersey	5.7%	246,523	260,754	8,882,190
New Mexico	2.5%	248,244	254,665	2,096,000
New York	13.7%	777,418	883,888	19,453,561
North Carolina	5%	1,229,580	1,295,238	10,488,000
North Dakota	1%	29,254	29,282	762,000
Ohio	7.3%	728,438	781,659	11,689,100
Oklahoma	3.5%	301,778	312,556	3,954,000
Oregon	5%	299,552	314,403	4,218,000
Pennsylvania	3.5%	785,312	812,975	12,802,000
Rhode Island	52.8%	8,653	13,227	1,059,000
South Carolina	4%	479,536	500,052	5,149,000
South Dakota	1.7%	64,692	65,810	885,000
Tennessee	16%	545,367	631,857	6,829,000
Texas	2%	1,810,316	1,849,329	29,145,505
Utah	0.5%	73,345	73,714	3,206,000
Vermont	2.3%	95,905	98,116	624,000
Virginia	7.5%	716,774	771,157	8,541,000
Washington	3%	515,361	532,208	7,614,000
Washington, D.C.	1.6%	1,670	1,698	706,000
West Virginia	2%	668,679	681,630	1,792,000
Wisconsin	2%	629,869	643,265	5,822,000
Wyoming	3%	54,294	55,953	579,000

Overreporting Stats by Tech Type

While all types of internet coverage is overstated, some were moreso, like fixed wireless and DSL. In the case of DSL, this might be partially due to providers winding down support for the service in many markets, while still claiming coverage in those areas to the FCC.

Here is a breakdown of national overreporting by technology, nationwide:

Cable – 14.1%
DSL – 53.4%
Fiber – 25.7%
Fixed Wireless – 58.4%

Improving Mapping Efforts Going Forward

We believe that crowdsourced studies like ours are one way to improve the quality and reliability of broadband data being collected today. More broadly, there are several arguments for how to proceed with improving broadband mapping efforts in the U.S.:

Incorporate more granular and accurate data: The FCC’s current mapping relies too heavily on data self-reported by internet service providers (ISPs). We advocate for the use of more detailed, location-specific data gathered through on-the-ground surveys, crowdsourcing, and other verification methods to get a truer picture of broadband availability and speeds.
Use a variety of data sources: We argue for integrating data from multiple sources beyond just ISP reporting, such as speed test data, consumer feedback, and information from state and local governments. Triangulating data points could help paint a more comprehensive picture.
Increase transparency and accountability: Making mapping methodologies and source data more transparent could help increase accountability and allow for error-checking and public input. We believe that public feedback and challenge processes are essential to further refine the maps.
Update maps more frequently: With the fast pace of broadband deployment, maps can quickly become outdated. More frequent data collection and updates could help keep pace with changes on the ground.
Validate data: Allocating resources for the FCC or third parties to independently validate a sample of provider-reported data through audits or on-the-ground testing could improve data quality and keep ISPs honest in their reporting.
Collaborate with states and localities: Leveraging mapping efforts already underway at state and local levels and collaborating to share data and methodologies could yield more accurate and detailed maps.

About This Study

BroadbandNow Research manually checked provider availability at 11 ISP web sites that offer online “check availability” tools. We checked at least one provider for 64,956 addresses from a dataset of more than 1 million addresses. In total, there were 113,686 provider-address combinations checked, of which a sample was re-checked for errors. Based on the results of the study, we estimate a 22.1 percent error rate for provider-address combinations across the U.S.

For addresses with multiple providers checked, 1.27 percent of the time none of the providers offered service. Many small and mid-sized providers do not offer online “check availability” tools and so could not be checked manually. Approximately one-third of the addresses in our sample did not have a local ISP alternative (according to the FCC). When we only assume that these addresses are unserved, the total unserved population in the US is more than 22 million.

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Originally Published: 03/25/2024

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Data Analysis in Research: Types & Methods

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Data analysis in quantitative research

Phase I: Data Validation

Phase II: Data Editing

Phase III: Data Coding

Descriptive statistics

Measures of Frequency

Measures of Central Tendency

Measures of Dispersion or Variation

Measures of Position

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