what makes a research valid

Frequently Asked Questions

What is reliability and validity in research.

Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes.

What is validity?

Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility.

What is reliability?

Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability.

What is reliability in psychology?

In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions.

What is test retest reliability?

Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time.

How to improve reliability of an experiment?

• Standardise procedures and instructions.
• Use consistent and precise measurement tools.
• Train observers or raters to reduce subjective judgments.
• Increase sample size to reduce random errors.
• Conduct pilot studies to refine methods.
• Repeat measurements or use multiple methods.
• Address potential sources of variability.

What is the difference between reliability and validity?

Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research.

Are interviews reliable and valid?

Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised.

Are IQ tests valid and reliable?

IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns.

Are questionnaires reliable and valid?

Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity.

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Home » Validity – Types, Examples and Guide

Validity – Types, Examples and Guide

Table of Contents

Validity is a fundamental concept in research, referring to the extent to which a test, measurement, or study accurately reflects or assesses the specific concept that the researcher is attempting to measure. Ensuring validity is crucial as it determines the trustworthiness and credibility of the research findings.

Research Validity

Research validity pertains to the accuracy and truthfulness of the research. It examines whether the research truly measures what it claims to measure. Without validity, research results can be misleading or erroneous, leading to incorrect conclusions and potentially flawed applications.

How to Ensure Validity in Research

Ensuring validity in research involves several strategies:

• Clear Operational Definitions : Define variables clearly and precisely.
• Use of Reliable Instruments : Employ measurement tools that have been tested for reliability.
• Pilot Testing : Conduct preliminary studies to refine the research design and instruments.
• Triangulation : Use multiple methods or sources to cross-verify results.
• Control Variables : Control extraneous variables that might influence the outcomes.

Types of Validity

Validity is categorized into several types, each addressing different aspects of measurement accuracy.

Internal Validity

Internal validity refers to the degree to which the results of a study can be attributed to the treatments or interventions rather than other factors. It is about ensuring that the study is free from confounding variables that could affect the outcome.

External Validity

External validity concerns the extent to which the research findings can be generalized to other settings, populations, or times. High external validity means the results are applicable beyond the specific context of the study.

Construct Validity

Construct validity evaluates whether a test or instrument measures the theoretical construct it is intended to measure. It involves ensuring that the test is truly assessing the concept it claims to represent.

Content Validity

Content validity examines whether a test covers the entire range of the concept being measured. It ensures that the test items represent all facets of the concept.

Criterion Validity

Criterion validity assesses how well one measure predicts an outcome based on another measure. It is divided into two types:

• Predictive Validity : How well a test predicts future performance.
• Concurrent Validity : How well a test correlates with a currently existing measure.

Face Validity

Face validity refers to the extent to which a test appears to measure what it is supposed to measure, based on superficial inspection. While it is the least scientific measure of validity, it is important for ensuring that stakeholders believe in the test’s relevance.

Importance of Validity

Validity is crucial because it directly affects the credibility of research findings. Valid results ensure that conclusions drawn from research are accurate and can be trusted. This, in turn, influences the decisions and policies based on the research.

Examples of Validity

• Internal Validity : A randomized controlled trial (RCT) where the random assignment of participants helps eliminate biases.
• External Validity : A study on educational interventions that can be applied to different schools across various regions.
• Construct Validity : A psychological test that accurately measures depression levels.
• Content Validity : An exam that covers all topics taught in a course.
• Criterion Validity : A job performance test that predicts future job success.

Where to Write About Validity in A Thesis

In a thesis, the methodology section should include discussions about validity. Here, you explain how you ensured the validity of your research instruments and design. Additionally, you may discuss validity in the results section, interpreting how the validity of your measurements affects your findings.

Applications of Validity

Validity has wide applications across various fields:

• Education : Ensuring assessments accurately measure student learning.
• Psychology : Developing tests that correctly diagnose mental health conditions.
• Market Research : Creating surveys that accurately capture consumer preferences.

Limitations of Validity

While ensuring validity is essential, it has its limitations:

• Complexity : Achieving high validity can be complex and resource-intensive.
• Context-Specific : Some validity types may not be universally applicable across all contexts.
• Subjectivity : Certain types of validity, like face validity, involve subjective judgments.

By understanding and addressing these aspects of validity, researchers can enhance the quality and impact of their studies, leading to more reliable and actionable results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Validity in research: a guide to measuring the right things

Last updated

27 February 2023

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Validity is necessary for all types of studies ranging from market validation of a business or product idea to the effectiveness of medical trials and procedures. So, how can you determine whether your research is valid? This guide can help you understand what validity is, the types of validity in research, and the factors that affect research validity.

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• What is validity?

In the most basic sense, validity is the quality of being based on truth or reason. Valid research strives to eliminate the effects of unrelated information and the circumstances under which evidence is collected. 

Validity in research is the ability to conduct an accurate study with the right tools and conditions to yield acceptable and reliable data that can be reproduced. Researchers rely on carefully calibrated tools for precise measurements. However, collecting accurate information can be more of a challenge.

Studies must be conducted in environments that don't sway the results to achieve and maintain validity. They can be compromised by asking the wrong questions or relying on limited data. 

Why is validity important in research?

Research is used to improve life for humans. Every product and discovery, from innovative medical breakthroughs to advanced new products, depends on accurate research to be dependable. Without it, the results couldn't be trusted, and products would likely fail. Businesses would lose money, and patients couldn't rely on medical treatments. 

While wasting money on a lousy product is a concern, lack of validity paints a much grimmer picture in the medical field or producing automobiles and airplanes, for example. Whether you're launching an exciting new product or conducting scientific research, validity can determine success and failure.

• What is reliability?

Reliability is the ability of a method to yield consistency. If the same result can be consistently achieved by using the same method to measure something, the measurement method is said to be reliable. For example, a thermometer that shows the same temperatures each time in a controlled environment is reliable.

While high reliability is a part of measuring validity, it's only part of the puzzle. If the reliable thermometer hasn't been properly calibrated and reliably measures temperatures two degrees too high, it doesn't provide a valid (accurate) measure of temperature. 

Similarly, if a researcher uses a thermometer to measure weight, the results won't be accurate because it's the wrong tool for the job. 

• How are reliability and validity assessed?

While measuring reliability is a part of measuring validity, there are distinct ways to assess both measurements for accuracy. 

How is reliability measured?

These measures of consistency and stability help assess reliability, including:

Consistency and stability of the same measure when repeated multiple times and conditions

Consistency and stability of the measure across different test subjects

Consistency and stability of results from different parts of a test designed to measure the same thing

How is validity measured?

Since validity refers to how accurately a method measures what it is intended to measure, it can be difficult to assess the accuracy. Validity can be estimated by comparing research results to other relevant data or theories.

The adherence of a measure to existing knowledge of how the concept is measured

The ability to cover all aspects of the concept being measured

The relation of the result in comparison with other valid measures of the same concept

• What are the types of validity in a research design?

Research validity is broadly gathered into two groups: internal and external. Yet, this grouping doesn't clearly define the different types of validity. Research validity can be divided into seven distinct groups.

Face validity : A test that appears valid simply because of the appropriateness or relativity of the testing method, included information, or tools used.

Content validity : The determination that the measure used in research covers the full domain of the content.

Construct validity : The assessment of the suitability of the measurement tool to measure the activity being studied.

Internal validity : The assessment of how your research environment affects measurement results. This is where other factors can’t explain the extent of an observed cause-and-effect response.

External validity : The extent to which the study will be accurate beyond the sample and the level to which it can be generalized in other settings, populations, and measures.

Statistical conclusion validity: The determination of whether a relationship exists between procedures and outcomes (appropriate sampling and measuring procedures along with appropriate statistical tests).

Criterion-related validity : A measurement of the quality of your testing methods against a criterion measure (like a “gold standard” test) that is measured at the same time.

• Examples of validity

Like different types of research and the various ways to measure validity, examples of validity can vary widely. These include:

A questionnaire may be considered valid because each question addresses specific and relevant aspects of the study subject.

In a brand assessment study, researchers can use comparison testing to verify the results of an initial study. For example, the results from a focus group response about brand perception are considered more valid when the results match that of a questionnaire answered by current and potential customers.

A test to measure a class of students' understanding of the English language contains reading, writing, listening, and speaking components to cover the full scope of how language is used.

• Factors that affect research validity

Certain factors can affect research validity in both positive and negative ways. By understanding the factors that improve validity and those that threaten it, you can enhance the validity of your study. These include:

Random selection of participants vs. the selection of participants that are representative of your study criteria

Blinding with interventions the participants are unaware of (like the use of placebos)

Manipulating the experiment by inserting a variable that will change the results

Randomly assigning participants to treatment and control groups to avoid bias

Following specific procedures during the study to avoid unintended effects

Conducting a study in the field instead of a laboratory for more accurate results

Replicating the study with different factors or settings to compare results

Using statistical methods to adjust for inconclusive data

What are the common validity threats in research, and how can their effects be minimized or nullified?

Research validity can be difficult to achieve because of internal and external threats that produce inaccurate results. These factors can jeopardize validity.

History: Events that occur between an early and later measurement

Maturation: The passage of time in a study can include data on actions that would have naturally occurred outside of the settings of the study

Repeated testing: The outcome of repeated tests can change the outcome of followed tests

Selection of subjects: Unconscious bias which can result in the selection of uniform comparison groups

Statistical regression: Choosing subjects based on extremes doesn't yield an accurate outcome for the majority of individuals

Attrition: When the sample group is diminished significantly during the course of the study

Maturation: When subjects mature during the study, and natural maturation is awarded to the effects of the study

While some validity threats can be minimized or wholly nullified, removing all threats from a study is impossible. For example, random selection can remove unconscious bias and statistical regression. 

Researchers can even hope to avoid attrition by using smaller study groups. Yet, smaller study groups could potentially affect the research in other ways. The best practice for researchers to prevent validity threats is through careful environmental planning and t reliable data-gathering methods. 

• How to ensure validity in your research

Researchers should be mindful of the importance of validity in the early planning stages of any study to avoid inaccurate results. Researchers must take the time to consider tools and methods as well as how the testing environment matches closely with the natural environment in which results will be used.

The following steps can be used to ensure validity in research:

Choose appropriate methods of measurement

Use appropriate sampling to choose test subjects

Create an accurate testing environment

How do you maintain validity in research?

Accurate research is usually conducted over a period of time with different test subjects. To maintain validity across an entire study, you must take specific steps to ensure that gathered data has the same levels of accuracy. 

Consistency is crucial for maintaining validity in research. When researchers apply methods consistently and standardize the circumstances under which data is collected, validity can be maintained across the entire study.

Is there a need for validation of the research instrument before its implementation?

An essential part of validity is choosing the right research instrument or method for accurate results. Consider the thermometer that is reliable but still produces inaccurate results. You're unlikely to achieve research validity without activities like calibration, content, and construct validity.

• Understanding research validity for more accurate results

Without validity, research can't provide the accuracy necessary to deliver a useful study. By getting a clear understanding of validity in research, you can take steps to improve your research skills and achieve more accurate results.

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• Reliability vs Validity in Research | Differences, Types & Examples

Reliability vs Validity in Research | Differences, Types & Examples

Published on 3 May 2022 by Fiona Middleton . Revised on 10 October 2022.

Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique, or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.

It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research .

Table of contents

Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis.

Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.

What is reliability?

Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.

What is validity?

Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.

High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.

However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.

Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect your data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.

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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

Types of reliability

Different types of reliability can be estimated through various statistical methods.

Types of validity

The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.

To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalisability of the results).

The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.

Ensuring validity

If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability, or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data .

• Choose appropriate methods of measurement

Ensure that your method and measurement technique are of high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.

For example, to collect data on a personality trait, you could use a standardised questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or the findings of previous studies, and the questions should be carefully and precisely worded.

• Use appropriate sampling methods to select your subjects

To produce valid generalisable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession). Ensure that you have enough participants and that they are representative of the population.

Ensuring reliability

Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible.

• Apply your methods consistently

Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.

For example, if you are conducting interviews or observations, clearly define how specific behaviours or responses will be counted, and make sure questions are phrased the same way each time.

• Standardise the conditions of your research

When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions.

It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper. Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.

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Validity & Reliability In Research

A Plain-Language Explanation (With Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Kerryn Warren (PhD) | September 2023

Validity and reliability are two related but distinctly different concepts within research. Understanding what they are and how to achieve them is critically important to any research project. In this post, we’ll unpack these two concepts as simply as possible.

This post is based on our popular online course, Research Methodology Bootcamp . In the course, we unpack the basics of methodology  using straightfoward language and loads of examples. If you’re new to academic research, you definitely want to use this link to get 50% off the course (limited-time offer).

Overview: Validity & Reliability

• The big picture
• Validity 101
• Reliability 101 
• Key takeaways

First, The Basics…

First, let’s start with a big-picture view and then we can zoom in to the finer details.

Validity and reliability are two incredibly important concepts in research, especially within the social sciences. Both validity and reliability have to do with the measurement of variables and/or constructs – for example, job satisfaction, intelligence, productivity, etc. When undertaking research, you’ll often want to measure these types of constructs and variables and, at the simplest level, validity and reliability are about ensuring the quality and accuracy of those measurements .

As you can probably imagine, if your measurements aren’t accurate or there are quality issues at play when you’re collecting your data, your entire study will be at risk. Therefore, validity and reliability are very important concepts to understand (and to get right). So, let’s unpack each of them.

What Is Validity?

In simple terms, validity (also called “construct validity”) is all about whether a research instrument accurately measures what it’s supposed to measure .

For example, let’s say you have a set of Likert scales that are supposed to quantify someone’s level of overall job satisfaction. If this set of scales focused purely on only one dimension of job satisfaction, say pay satisfaction, this would not be a valid measurement, as it only captures one aspect of the multidimensional construct. In other words, pay satisfaction alone is only one contributing factor toward overall job satisfaction, and therefore it’s not a valid way to measure someone’s job satisfaction.

Oftentimes in quantitative studies, the way in which the researcher or survey designer interprets a question or statement can differ from how the study participants interpret it . Given that respondents don’t have the opportunity to ask clarifying questions when taking a survey, it’s easy for these sorts of misunderstandings to crop up. Naturally, if the respondents are interpreting the question in the wrong way, the data they provide will be pretty useless . Therefore, ensuring that a study’s measurement instruments are valid – in other words, that they are measuring what they intend to measure – is incredibly important.

There are various types of validity and we’re not going to go down that rabbit hole in this post, but it’s worth quickly highlighting the importance of making sure that your research instrument is tightly aligned with the theoretical construct you’re trying to measure .  In other words, you need to pay careful attention to how the key theories within your study define the thing you’re trying to measure – and then make sure that your survey presents it in the same way.

For example, sticking with the “job satisfaction” construct we looked at earlier, you’d need to clearly define what you mean by job satisfaction within your study (and this definition would of course need to be underpinned by the relevant theory). You’d then need to make sure that your chosen definition is reflected in the types of questions or scales you’re using in your survey . Simply put, you need to make sure that your survey respondents are perceiving your key constructs in the same way you are. Or, even if they’re not, that your measurement instrument is capturing the necessary information that reflects your definition of the construct at hand.

If all of this talk about constructs sounds a bit fluffy, be sure to check out Research Methodology Bootcamp , which will provide you with a rock-solid foundational understanding of all things methodology-related. Remember, you can take advantage of our 60% discount offer using this link.

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What Is Reliability?

As with validity, reliability is an attribute of a measurement instrument – for example, a survey, a weight scale or even a blood pressure monitor. But while validity is concerned with whether the instrument is measuring the “thing” it’s supposed to be measuring, reliability is concerned with consistency and stability . In other words, reliability reflects the degree to which a measurement instrument produces consistent results when applied repeatedly to the same phenomenon , under the same conditions .

As you can probably imagine, a measurement instrument that achieves a high level of consistency is naturally more dependable (or reliable) than one that doesn’t – in other words, it can be trusted to provide consistent measurements . And that, of course, is what you want when undertaking empirical research. If you think about it within a more domestic context, just imagine if you found that your bathroom scale gave you a different number every time you hopped on and off of it – you wouldn’t feel too confident in its ability to measure the variable that is your body weight 🙂

It’s worth mentioning that reliability also extends to the person using the measurement instrument . For example, if two researchers use the same instrument (let’s say a measuring tape) and they get different measurements, there’s likely an issue in terms of how one (or both) of them are using the measuring tape. So, when you think about reliability, consider both the instrument and the researcher as part of the equation.

As with validity, there are various types of reliability and various tests that can be used to assess the reliability of an instrument. A popular one that you’ll likely come across for survey instruments is Cronbach’s alpha , which is a statistical measure that quantifies the degree to which items within an instrument (for example, a set of Likert scales) measure the same underlying construct . In other words, Cronbach’s alpha indicates how closely related the items are and whether they consistently capture the same concept . 

Recap: Key Takeaways

Alright, let’s quickly recap to cement your understanding of validity and reliability:

• Validity is concerned with whether an instrument (e.g., a set of Likert scales) is measuring what it’s supposed to measure
• Reliability is concerned with whether that measurement is consistent and stable when measuring the same phenomenon under the same conditions.

In short, validity and reliability are both essential to ensuring that your data collection efforts deliver high-quality, accurate data that help you answer your research questions . So, be sure to always pay careful attention to the validity and reliability of your measurement instruments when collecting and analysing data. As the adage goes, “rubbish in, rubbish out” – make sure that your data inputs are rock-solid.

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Validity In Psychology Research: Types & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

In psychology research, validity refers to the extent to which a test or measurement tool accurately measures what it’s intended to measure. It ensures that the research findings are genuine and not due to extraneous factors.

Validity can be categorized into different types based on internal and external validity .

The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure. For example, a test of intelligence should measure intelligence and not something else (such as memory).

Internal and External Validity In Research

Internal validity refers to whether the effects observed in a study are due to the manipulation of the independent variable and not some other confounding factor.

In other words, there is a causal relationship between the independent and dependent variables .

Internal validity can be improved by controlling extraneous variables, using standardized instructions, counterbalancing, and eliminating demand characteristics and investigator effects.

External validity refers to the extent to which the results of a study can be generalized to other settings (ecological validity), other people (population validity), and over time (historical validity).

External validity can be improved by setting experiments more naturally and using random sampling to select participants.

Types of Validity In Psychology

Two main categories of validity are used to assess the validity of the test (i.e., questionnaire, interview, IQ test, etc.): Content and criterion.

• Content validity refers to the extent to which a test or measurement represents all aspects of the intended content domain. It assesses whether the test items adequately cover the topic or concept.
• Criterion validity assesses the performance of a test based on its correlation with a known external criterion or outcome. It can be further divided into concurrent (measured at the same time) and predictive (measuring future performance) validity.

Face Validity

Face validity is simply whether the test appears (at face value) to measure what it claims to. This is the least sophisticated measure of content-related validity, and is a superficial and subjective assessment based on appearance.

Tests wherein the purpose is clear, even to naïve respondents, are said to have high face validity. Accordingly, tests wherein the purpose is unclear have low face validity (Nevo, 1985).

A direct measurement of face validity is obtained by asking people to rate the validity of a test as it appears to them. This rater could use a Likert scale to assess face validity.

For example:

• The test is extremely suitable for a given purpose
• The test is very suitable for that purpose;
• The test is adequate
• The test is inadequate
• The test is irrelevant and, therefore, unsuitable

It is important to select suitable people to rate a test (e.g., questionnaire, interview, IQ test, etc.). For example, individuals who actually take the test would be well placed to judge its face validity.

Also, people who work with the test could offer their opinion (e.g., employers, university administrators, employers). Finally, the researcher could use members of the general public with an interest in the test (e.g., parents of testees, politicians, teachers, etc.).

The face validity of a test can be considered a robust construct only if a reasonable level of agreement exists among raters.

It should be noted that the term face validity should be avoided when the rating is done by an “expert,” as content validity is more appropriate.

Having face validity does not mean that a test really measures what the researcher intends to measure, but only in the judgment of raters that it appears to do so. Consequently, it is a crude and basic measure of validity.

A test item such as “ I have recently thought of killing myself ” has obvious face validity as an item measuring suicidal cognitions and may be useful when measuring symptoms of depression.

However, the implication of items on tests with clear face validity is that they are more vulnerable to social desirability bias. Individuals may manipulate their responses to deny or hide problems or exaggerate behaviors to present a positive image of themselves.

It is possible for a test item to lack face validity but still have general validity and measure what it claims to measure. This is good because it reduces demand characteristics and makes it harder for respondents to manipulate their answers.

For example, the test item “ I believe in the second coming of Christ ” would lack face validity as a measure of depression (as the purpose of the item is unclear).

This item appeared on the first version of The Minnesota Multiphasic Personality Inventory (MMPI) and loaded on the depression scale.

Because most of the original normative sample of the MMPI were good Christians, only a depressed Christian would think Christ is not coming back. Thus, for this particular religious sample, the item does have general validity but not face validity.

Construct Validity

Construct validity assesses how well a test or measure represents and captures an abstract theoretical concept, known as a construct. It indicates the degree to which the test accurately reflects the construct it intends to measure, often evaluated through relationships with other variables and measures theoretically connected to the construct.

Construct validity was invented by Cronbach and Meehl (1955). This type of content-related validity refers to the extent to which a test captures a specific theoretical construct or trait, and it overlaps with some of the other aspects of validity

Construct validity does not concern the simple, factual question of whether a test measures an attribute.

Instead, it is about the complex question of whether test score interpretations are consistent with a nomological network involving theoretical and observational terms (Cronbach & Meehl, 1955).

To test for construct validity, it must be demonstrated that the phenomenon being measured actually exists. So, the construct validity of a test for intelligence, for example, depends on a model or theory of intelligence .

Construct validity entails demonstrating the power of such a construct to explain a network of research findings and to predict further relationships.

The more evidence a researcher can demonstrate for a test’s construct validity, the better. However, there is no single method of determining the construct validity of a test.

Instead, different methods and approaches are combined to present the overall construct validity of a test. For example, factor analysis and correlational methods can be used.

Convergent validity

Convergent validity is a subtype of construct validity. It assesses the degree to which two measures that theoretically should be related are related.

It demonstrates that measures of similar constructs are highly correlated. It helps confirm that a test accurately measures the intended construct by showing its alignment with other tests designed to measure the same or similar constructs.

For example, suppose there are two different scales used to measure self-esteem:

Scale A and Scale B. If both scales effectively measure self-esteem, then individuals who score high on Scale A should also score high on Scale B, and those who score low on Scale A should score similarly low on Scale B.

If the scores from these two scales show a strong positive correlation, then this provides evidence for convergent validity because it indicates that both scales seem to measure the same underlying construct of self-esteem.

Concurrent Validity (i.e., occurring at the same time)

Concurrent validity evaluates how well a test’s results correlate with the results of a previously established and accepted measure, when both are administered at the same time.

It helps in determining whether a new measure is a good reflection of an established one without waiting to observe outcomes in the future.

If the new test is validated by comparison with a currently existing criterion, we have concurrent validity.

Very often, a new IQ or personality test might be compared with an older but similar test known to have good validity already.

Predictive Validity

Predictive validity assesses how well a test predicts a criterion that will occur in the future. It measures the test’s ability to foresee the performance of an individual on a related criterion measured at a later point in time. It gauges the test’s effectiveness in predicting subsequent real-world outcomes or results.

For example, a prediction may be made on the basis of a new intelligence test that high scorers at age 12 will be more likely to obtain university degrees several years later. If the prediction is born out, then the test has predictive validity.

Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin , 52, 281-302.

Hathaway, S. R., & McKinley, J. C. (1943). Manual for the Minnesota Multiphasic Personality Inventory . New York: Psychological Corporation.

Kelley, T. L. (1927). Interpretation of educational measurements. New York : Macmillan.

Nevo, B. (1985). Face validity revisited . Journal of Educational Measurement , 22(4), 287-293.

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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing, Laurentian University , Sudbury, Ontario , Canada
• 2 Faculty of Health and Social Care , London South Bank University , London , UK
• Correspondence to : Dr Roberta Heale, School of Nursing, Laurentian University, Ramsey Lake Road, Sudbury, Ontario, Canada P3E2C6; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2015-102129

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Evidence-based practice includes, in part, implementation of the findings of well-conducted quality research studies. So being able to critique quantitative research is an important skill for nurses. Consideration must be given not only to the results of the study but also the rigour of the research. Rigour refers to the extent to which the researchers worked to enhance the quality of the studies. In quantitative research, this is achieved through measurement of the validity and reliability. 1

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Types of validity

The first category is content validity . This category looks at whether the instrument adequately covers all the content that it should with respect to the variable. In other words, does the instrument cover the entire domain related to the variable, or construct it was designed to measure? In an undergraduate nursing course with instruction about public health, an examination with content validity would cover all the content in the course with greater emphasis on the topics that had received greater coverage or more depth. A subset of content validity is face validity , where experts are asked their opinion about whether an instrument measures the concept intended.

Construct validity refers to whether you can draw inferences about test scores related to the concept being studied. For example, if a person has a high score on a survey that measures anxiety, does this person truly have a high degree of anxiety? In another example, a test of knowledge of medications that requires dosage calculations may instead be testing maths knowledge.

There are three types of evidence that can be used to demonstrate a research instrument has construct validity:

Homogeneity—meaning that the instrument measures one construct.

Convergence—this occurs when the instrument measures concepts similar to that of other instruments. Although if there are no similar instruments available this will not be possible to do.

Theory evidence—this is evident when behaviour is similar to theoretical propositions of the construct measured in the instrument. For example, when an instrument measures anxiety, one would expect to see that participants who score high on the instrument for anxiety also demonstrate symptoms of anxiety in their day-to-day lives. 2

The final measure of validity is criterion validity . A criterion is any other instrument that measures the same variable. Correlations can be conducted to determine the extent to which the different instruments measure the same variable. Criterion validity is measured in three ways:

Convergent validity—shows that an instrument is highly correlated with instruments measuring similar variables.

Divergent validity—shows that an instrument is poorly correlated to instruments that measure different variables. In this case, for example, there should be a low correlation between an instrument that measures motivation and one that measures self-efficacy.

Predictive validity—means that the instrument should have high correlations with future criterions. 2 For example, a score of high self-efficacy related to performing a task should predict the likelihood a participant completing the task.

Reliability

Reliability relates to the consistency of a measure. A participant completing an instrument meant to measure motivation should have approximately the same responses each time the test is completed. Although it is not possible to give an exact calculation of reliability, an estimate of reliability can be achieved through different measures. The three attributes of reliability are outlined in table 2 . How each attribute is tested for is described below.

Attributes of reliability

Homogeneity (internal consistency) is assessed using item-to-total correlation, split-half reliability, Kuder-Richardson coefficient and Cronbach's α. In split-half reliability, the results of a test, or instrument, are divided in half. Correlations are calculated comparing both halves. Strong correlations indicate high reliability, while weak correlations indicate the instrument may not be reliable. The Kuder-Richardson test is a more complicated version of the split-half test. In this process the average of all possible split half combinations is determined and a correlation between 0–1 is generated. This test is more accurate than the split-half test, but can only be completed on questions with two answers (eg, yes or no, 0 or 1). 3

Cronbach's α is the most commonly used test to determine the internal consistency of an instrument. In this test, the average of all correlations in every combination of split-halves is determined. Instruments with questions that have more than two responses can be used in this test. The Cronbach's α result is a number between 0 and 1. An acceptable reliability score is one that is 0.7 and higher. 1 , 3

Stability is tested using test–retest and parallel or alternate-form reliability testing. Test–retest reliability is assessed when an instrument is given to the same participants more than once under similar circumstances. A statistical comparison is made between participant's test scores for each of the times they have completed it. This provides an indication of the reliability of the instrument. Parallel-form reliability (or alternate-form reliability) is similar to test–retest reliability except that a different form of the original instrument is given to participants in subsequent tests. The domain, or concepts being tested are the same in both versions of the instrument but the wording of items is different. 2 For an instrument to demonstrate stability there should be a high correlation between the scores each time a participant completes the test. Generally speaking, a correlation coefficient of less than 0.3 signifies a weak correlation, 0.3–0.5 is moderate and greater than 0.5 is strong. 4

Equivalence is assessed through inter-rater reliability. This test includes a process for qualitatively determining the level of agreement between two or more observers. A good example of the process used in assessing inter-rater reliability is the scores of judges for a skating competition. The level of consistency across all judges in the scores given to skating participants is the measure of inter-rater reliability. An example in research is when researchers are asked to give a score for the relevancy of each item on an instrument. Consistency in their scores relates to the level of inter-rater reliability of the instrument.

Determining how rigorously the issues of reliability and validity have been addressed in a study is an essential component in the critique of research as well as influencing the decision about whether to implement of the study findings into nursing practice. In quantitative studies, rigour is determined through an evaluation of the validity and reliability of the tools or instruments utilised in the study. A good quality research study will provide evidence of how all these factors have been addressed. This will help you to assess the validity and reliability of the research and help you decide whether or not you should apply the findings in your area of clinical practice.

• Lobiondo-Wood G ,
• Shuttleworth M
• ↵ Laerd Statistics . Determining the correlation coefficient . 2013 . https://statistics.laerd.com/premium/pc/pearson-correlation-in-spss-8.php

Twitter Follow Roberta Heale at @robertaheale and Alison Twycross at @alitwy

Competing interests None declared.

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Reliability and validity: Importance in Medical Research

Affiliations.

• 1 Al-Nafees Medical College,Isra University, Islamabad, Pakistan.
• 2 Fauji Foundation Hospital, Foundation University Medical College, Islamabad, Pakistan.
• PMID: 34974579
• DOI: 10.47391/JPMA.06-861

Reliability and validity are among the most important and fundamental domains in the assessment of any measuring methodology for data-collection in a good research. Validity is about what an instrument measures and how well it does so, whereas reliability concerns the truthfulness in the data obtained and the degree to which any measuring tool controls random error. The current narrative review was planned to discuss the importance of reliability and validity of data-collection or measurement techniques used in research. It describes and explores comprehensively the reliability and validity of research instruments and also discusses different forms of reliability and validity with concise examples. An attempt has been taken to give a brief literature review regarding the significance of reliability and validity in medical sciences.

Keywords: Validity, Reliability, Medical research, Methodology, Assessment, Research tools..

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Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure.

Reliability alone is not enough, measures need to be reliable, as well as, valid. For example, if a weight measuring scale is wrong by 4kg (it deducts 4 kg of the actual weight), it can be specified as reliable, because the scale displays the same weight every time we measure a specific item. However, the scale is not valid because it does not display the actual weight of the item.

Research validity can be divided into two groups: internal and external. It can be specified that “internal validity refers to how the research findings match reality, while external validity refers to the extend to which the research findings can be replicated to other environments” (Pelissier, 2008, p.12).

Moreover, validity can also be divided into five types:

1. Face Validity is the most basic type of validity and it is associated with a highest level of subjectivity because it is not based on any scientific approach. In other words, in this case a test may be specified as valid by a researcher because it may seem as valid, without an in-depth scientific justification.

Example: questionnaire design for a study that analyses the issues of employee performance can be assessed as valid because each individual question may seem to be addressing specific and relevant aspects of employee performance.

2. Construct Validity relates to assessment of suitability of measurement tool to measure the phenomenon being studied. Application of construct validity can be effectively facilitated with the involvement of panel of ‘experts’ closely familiar with the measure and the phenomenon.

Example: with the application of construct validity the levels of leadership competency in any given organisation can be effectively assessed by devising questionnaire to be answered by operational level employees and asking questions about the levels of their motivation to do their duties in a daily basis.

3. Criterion-Related Validity involves comparison of tests results with the outcome. This specific type of validity correlates results of assessment with another criterion of assessment.

Example: nature of customer perception of brand image of a specific company can be assessed via organising a focus group. The same issue can also be assessed through devising questionnaire to be answered by current and potential customers of the brand. The higher the level of correlation between focus group and questionnaire findings, the high the level of criterion-related validity.

4. Formative Validity refers to assessment of effectiveness of the measure in terms of providing information that can be used to improve specific aspects of the phenomenon.

Example: when developing initiatives to increase the levels of effectiveness of organisational culture if the measure is able to identify specific weaknesses of organisational culture such as employee-manager communication barriers, then the level of formative validity of the measure can be assessed as adequate.

5. Sampling Validity (similar to content validity) ensures that the area of coverage of the measure within the research area is vast. No measure is able to cover all items and elements within the phenomenon, therefore, important items and elements are selected using a specific pattern of sampling method depending on aims and objectives of the study.

Example: when assessing a leadership style exercised in a specific organisation, assessment of decision-making style would not suffice, and other issues related to leadership style such as organisational culture, personality of leaders, the nature of the industry etc. need to be taken into account as well.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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Reliability vs. Validity in Research: Types & Examples

When it comes to research, getting things right is crucial. That’s where the concepts of “Reliability vs Validity in Research” come in. 

Imagine it like a balancing act – making sure your measurements are consistent and accurate at the same time. This is where test-retest reliability, having different researchers check things, and keeping things consistent within your research plays a big role. 

As we dive into this topic, we’ll uncover the differences between reliability and validity, see how they work together, and learn how to use them effectively.

Understanding Reliability vs. Validity in Research

When it comes to collecting data and conducting research, two crucial concepts stand out: reliability and validity. 

These pillars uphold the integrity of research findings, ensuring that the data collected and the conclusions drawn are both meaningful and trustworthy. Let’s dive into the heart of the concepts, reliability, and validity, to comprehend their significance in the realm of research truly.

What is reliability?

Reliability refers to the consistency and dependability of the data collection process. It’s like having a steady hand that produces the same result each time it reaches for a task. 

In the research context, reliability is all about ensuring that if you were to repeat the same study using the same reliable measurement technique, you’d end up with the same results. It’s like having multiple researchers independently conduct the same experiment and getting outcomes that align perfectly.

Imagine you’re using a thermometer to measure the temperature of the water. You have a reliable measurement if you dip the thermometer into the water multiple times and get the same reading each time. This tells you that your method and measurement technique consistently produce the same results, whether it’s you or another researcher performing the measurement.

What is validity?

On the other hand, validity refers to the accuracy and meaningfulness of your data. It’s like ensuring that the puzzle pieces you’re putting together actually form the intended picture. When you have validity, you know that your method and measurement technique are consistent and capable of producing results aligned with reality.

Think of it this way; Imagine you’re conducting a test that claims to measure a specific trait, like problem-solving ability. If the test consistently produces results that accurately reflect participants’ problem-solving skills, then the test has high validity. In this case, the test produces accurate results that truly correspond to the trait it aims to measure.

In essence, while reliability assures you that your data collection process is like a well-oiled machine producing the same results, validity steps in to ensure that these results are not only consistent but also relevantly accurate. 

Together, these concepts provide researchers with the tools to conduct research that stands on a solid foundation of dependable methods and meaningful insights.

Types of Reliability

Let’s explore the various types of reliability that researchers consider to ensure their work stands on solid ground.

High test-retest reliability

Test-retest reliability involves assessing the consistency of measurements over time. It’s like taking the same measurement or test twice – once and then again after a certain period. If the results align closely, it indicates that the measurement is reliable over time. Think of it as capturing the essence of stability. 

Inter-rater reliability

When multiple researchers or observers are part of the equation, interrater reliability comes into play. This type of reliability assesses the level of agreement between different observers when evaluating the same phenomenon. It’s like ensuring that different pairs of eyes perceive things in a similar way. 

Internal reliability

Internal consistency dives into the harmony among different items within a measurement tool aiming to assess the same concept. This often comes into play in surveys or questionnaires, where participants respond to various items related to a single construct. If the responses to these items consistently reflect the same underlying concept, the measurement is said to have high internal consistency. 

Types of validity

Let’s explore the various types of validity that researchers consider to ensure their work stands on solid ground.

Content validity

It delves into whether a measurement truly captures all dimensions of the concept it intends to measure. It’s about making sure your measurement tool covers all relevant aspects comprehensively. 

Imagine designing a test to assess students’ understanding of a history chapter. It exhibits high content validity if the test includes questions about key events, dates, and causes. However, if it focuses solely on dates and omits causation, its content validity might be questionable.

Construct validity

It assesses how well a measurement aligns with established theories and concepts. It’s like ensuring that your measurement is a true representation of the abstract construct you’re trying to capture. 

Criterion validity

Criterion validity examines how well your measurement corresponds to other established measurements of the same concept. It’s about making sure your measurement accurately predicts or correlates with external criteria.

Differences between reliability and validity in research

Let’s delve into the differences between reliability and validity in research.

While both reliability and validity contribute to trustworthy research, they address distinct aspects. Reliability ensures consistent results, while validity ensures accurate and relevant results that reflect the true nature of the measured concept.

Example of Reliability and Validity in Research

In this section, we’ll explore instances that highlight the differences between reliability and validity and how they play a crucial role in ensuring the credibility of research findings.

Example of reliability

Imagine you are studying the reliability of a smartphone’s battery life measurement. To collect data, you fully charge the phone and measure the battery life three times in the same controlled environment—same apps running, same brightness level, and same usage patterns. 

If the measurements consistently show a similar battery life duration each time you repeat the test, it indicates that your measurement method is reliable. The consistent results under the same conditions assure you that the battery life measurement can be trusted to provide dependable information about the phone’s performance.

Example of validity

Researchers collect data from a group of participants in a study aiming to assess the validity of a newly developed stress questionnaire. To ensure validity, they compare the scores obtained from the stress questionnaire with the participants’ actual stress levels measured using physiological indicators such as heart rate variability and cortisol levels. 

If participants’ scores correlate strongly with their physiological stress levels, the questionnaire is valid. This means the questionnaire accurately measures participants’ stress levels, and its results correspond to real variations in their physiological responses to stress. 

Validity assessed through the correlation between questionnaire scores and physiological measures ensures that the questionnaire is effectively measuring what it claims to measure participants’ stress levels.

In the world of research, differentiating between reliability and validity is crucial. Reliability ensures consistent results, while validity confirms accurate measurements. Using tools like QuestionPro enhances data collection for both reliability and validity. For instance, measuring self-esteem over time showcases reliability, and aligning questions with theories demonstrates validity. 

QuestionPro empowers researchers to achieve reliable and valid results through its robust features, facilitating credible research outcomes. Contact QuestionPro to create a free account or learn more!

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Validity vs. Reliability in Research: What's the Difference?

Introduction

What is the difference between reliability and validity in a study, what is an example of reliability and validity, how to ensure validity and reliability in your research, critiques of reliability and validity.

In research, validity and reliability are crucial for producing robust findings. They provide a foundation that assures scholars, practitioners, and readers alike that the research's insights are both accurate and consistent. However, the nuanced nature of qualitative data often blurs the lines between these concepts, making it imperative for researchers to discern their distinct roles.

This article seeks to illuminate the intricacies of reliability and validity, highlighting their significance and distinguishing their unique attributes. By understanding these critical facets, qualitative researchers can ensure their work not only resonates with authenticity but also trustworthiness.

In the domain of research, whether qualitative or quantitative , two concepts often arise when discussing the quality and rigor of a study: reliability and validity . These two terms, while interconnected, have distinct meanings that hold significant weight in the world of research.

Reliability, at its core, speaks to the consistency of a study. If a study or test measures the same concept repeatedly and yields the same results, it demonstrates a high degree of reliability. A common method for assessing reliability is through internal consistency reliability, which checks if multiple items that measure the same concept produce similar scores.

Another method often used is inter-rater reliability , which gauges the consistency of scores given by different raters. This approach is especially amenable to qualitative research , and it can help researchers assess the clarity of their code system and the consistency of their codings . For a study to be more dependable, it's imperative to ensure a sufficient measurement of reliability is achieved.

On the other hand, validity is concerned with accuracy. It looks at whether a study truly measures what it claims to. Within the realm of validity, several types exist. Construct validity, for instance, verifies that a study measures the intended abstract concept or underlying construct. If a research aims to measure self-esteem and accurately captures this abstract trait, it demonstrates strong construct validity.

Content validity ensures that a test or study comprehensively represents the entire domain of the concept it seeks to measure. For instance, if a test aims to assess mathematical ability, it should cover arithmetic, algebra, geometry, and more to showcase strong content validity.

Criterion validity is another form of validity that ensures that the scores from a test correlate well with a measure from a related outcome. A subset of this is predictive validity, which checks if the test can predict future outcomes. For instance, if an aptitude test can predict future job performance, it can be said to have high predictive validity.

The distinction between reliability and validity becomes clear when one considers the nature of their focus. While reliability is concerned with consistency and reproducibility, validity zeroes in on accuracy and truthfulness.

A research tool can be reliable without being valid. For instance, faulty instrument measures might consistently give bad readings (reliable but not valid). Conversely, in discussions about test reliability, the same test measure administered multiple times could sometimes hit the mark and at other times miss it entirely, producing different test scores each time. This would make it valid in some instances but not reliable.

For a study to be robust, it must achieve both reliability and validity. Reliability ensures the study's findings are reproducible while validity confirms that it accurately represents the phenomena it claims to. Ensuring both in a study means the results are both dependable and accurate, forming a cornerstone for high-quality research.

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Understanding the nuances of reliability and validity becomes clearer when contextualized within a real-world research setting. Imagine a qualitative study where a researcher aims to explore the experiences of teachers in urban schools concerning classroom management. The primary method of data collection is semi-structured interviews .

To ensure the reliability of this qualitative study, the researcher crafts a consistent list of open-ended questions for the interview. This ensures that, while each conversation might meander based on the individual’s experiences, there remains a core set of topics related to classroom management that every participant addresses.

The essence of reliability in this context isn't necessarily about garnering identical responses but rather about achieving a consistent approach to data collection and subsequent interpretation . As part of this commitment to reliability, two researchers might independently transcribe and analyze a subset of these interviews. If they identify similar themes and patterns in their independent analyses, it suggests a consistent interpretation of the data, showcasing inter-rater reliability .

Validity , on the other hand, is anchored in ensuring that the research genuinely captures and represents the lived experiences and sentiments of teachers concerning classroom management. To establish content validity, the list of interview questions is thoroughly reviewed by a panel of educational experts. Their feedback ensures that the questions encompass the breadth of issues and concerns related to classroom management in urban school settings.

As the interviews are conducted, the researcher pays close attention to the depth and authenticity of responses. After the interviews, member checking could be employed, where participants review the researcher's interpretation of their responses to ensure that their experiences and perspectives have been accurately captured. This strategy helps in affirming the study's construct validity, ensuring that the abstract concept of "experiences with classroom management" has been truthfully and adequately represented.

In this example, we can see that while the interview study is rooted in qualitative methods and subjective experiences, the principles of reliability and validity can still meaningfully inform the research process. They serve as guides to ensure the research's findings are both dependable and genuinely reflective of the participants' experiences.

Ensuring validity and reliability in research, irrespective of its qualitative or quantitative nature, is pivotal to producing results that are both trustworthy and robust. Here's how you can integrate these concepts into your study to ensure its rigor:

Reliability is about consistency. One of the most straightforward ways to gauge it in quantitative research is using test-retest reliability. It involves administering the same test to the same group of participants on two separate occasions and then comparing the results.

A high degree of similarity between the two sets of results indicates good reliability. This can often be measured using a correlation coefficient, where a value closer to 1 indicates a strong positive consistency between the two test iterations.

Validity, on the other hand, ensures that the research genuinely measures what it intends to. There are various forms of validity to consider. Convergent validity ensures that two measures of the same construct or those that should theoretically be related, are indeed correlated. For example, two different measures assessing self-esteem should show similar results for the same group, highlighting that they are measuring the same underlying construct.

Face validity is the most basic form of validity and is gauged by the sheer appearance of the measurement tool. If, at face value, a test seems like it measures what it claims to, it has face validity. This is often the first step and is usually followed by more rigorous forms of validity testing.

Criterion-related validity, a subtype of the previously discussed criterion validity, evaluates how well the outcomes of a particular test or measurement correlate with another related measure. For example, if a new tool is developed to measure reading comprehension, its results can be compared with those of an established reading comprehension test to assess its criterion-related validity. If the results show a strong correlation, it's a sign that the new tool is valid.

Ensuring both validity and reliability requires deliberate planning, meticulous testing, and constant reflection on the study's methods and results. This might involve using established scales or measures with proven validity and reliability, conducting pilot studies to refine measurement tools, and always staying cognizant of the fact that these two concepts are important considerations for research robustness.

While reliability and validity are foundational concepts in many traditional research paradigms, they have not escaped scrutiny, especially from critical and poststructuralist perspectives. These critiques often arise from the fundamental philosophical differences in how knowledge, truth, and reality are perceived and constructed.

From a poststructuralist viewpoint, the very pursuit of a singular "truth" or an objective reality is questionable. In such a perspective, multiple truths exist, each shaped by its own socio-cultural, historical, and individual contexts.

Reliability, with its emphasis on consistent replication, might then seem at odds with this understanding. If truths are multiple and shifting, how can consistency across repeated measures or observations be a valid measure of anything other than the research instrument's stability?

Validity, too, faces critique. In seeking to ensure that a study measures what it purports to measure, there's an implicit assumption of an observable, knowable reality. Poststructuralist critiques question this foundation, arguing that reality is too fluid, multifaceted, and influenced by power dynamics to be pinned down by any singular measurement or representation.

Moreover, the very act of determining "validity" often requires an external benchmark or "gold standard." This brings up the issue of who determines this standard and the power dynamics and potential biases inherent in such decisions.

Another point of contention is the way these concepts can inadvertently prioritize certain forms of knowledge over others. For instance, privileging research that meets stringent reliability and validity criteria might marginalize more exploratory, interpretive, or indigenous research methods. These methods, while offering deep insights, might not align neatly with traditional understandings of reliability and validity, potentially relegating them to the periphery of "accepted" knowledge production.

To be sure, reliability and validity serve as guiding principles in many research approaches. However, it's essential to recognize their limitations and the critiques posed by alternative epistemologies. Engaging with these critiques doesn't diminish the value of reliability and validity but rather enriches our understanding of the multifaceted nature of knowledge and the complexities of its pursuit.

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Validity and Reliability

The principles of validity and reliability are fundamental cornerstones of the scientific method.

This article is a part of the guide:

• Types of Validity
• Definition of Reliability
• Content Validity
• Construct Validity
• External Validity

Browse Full Outline

• 1 Validity and Reliability
• 2 Types of Validity
• 3.1 Population Validity
• 3.2 Ecological Validity
• 4 Internal Validity
• 5.1.1 Concurrent Validity
• 5.1.2 Predictive Validity
• 6 Content Validity
• 7.1 Convergent and Discriminant Validity
• 8 Face Validity
• 9 Definition of Reliability
• 10.1 Reproducibility
• 10.2 Replication Study
• 11 Interrater Reliability
• 12 Internal Consistency Reliability
• 13 Instrument Reliability

Together, they are at the core of what is accepted as scientific proof, by scientist and philosopher alike.

By following a few basic principles, any experimental design will stand up to rigorous questioning and skepticism.

What is Reliability?

The idea behind reliability is that any significant results must be more than a one-off finding and be inherently repeatable .

Other researchers must be able to perform exactly the same experiment , under the same conditions and generate the same results. This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis .

Without this replication of statistically significant results , the experiment and research have not fulfilled all of the requirements of testability .

This prerequisite is essential to a hypothesis establishing itself as an accepted scientific truth.

For example, if you are performing a time critical experiment, you will be using some type of stopwatch. Generally, it is reasonable to assume that the instruments are reliable and will keep true and accurate time. However, diligent scientists take measurements many times, to minimize the chances of malfunction and maintain validity and reliability.

At the other extreme, any experiment that uses human judgment is always going to come under question.

For example, if observers rate certain aspects, like in Bandura’s Bobo Doll Experiment , then the reliability of the test is compromised. Human judgment can vary wildly between observers , and the same individual may rate things differently depending upon time of day and current mood.

This means that such experiments are more difficult to repeat and are inherently less reliable. Reliability is a necessary ingredient for determining the overall validity of a scientific experiment and enhancing the strength of the results.

Debate between social and pure scientists, concerning reliability, is robust and ongoing.

What is Validity?

Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method.

For example, there must have been randomization of the sample groups and appropriate care and diligence shown in the allocation of controls .

Internal validity dictates how an experimental design is structured and encompasses all of the steps of the scientific research method .

Even if your results are great, sloppy and inconsistent design will compromise your integrity in the eyes of the scientific community. Internal validity and reliability are at the core of any experimental design.

External validity is the process of examining the results and questioning whether there are any other possible causal relationships.

Control groups and randomization will lessen external validity problems but no method can be completely successful. This is why the statistical proofs of a hypothesis called significant , not absolute truth.

Any scientific research design only puts forward a possible cause for the studied effect.

There is always the chance that another unknown factor contributed to the results and findings. This extraneous causal relationship may become more apparent, as techniques are refined and honed.

If you have constructed your experiment to contain validity and reliability then the scientific community is more likely to accept your findings.

Eliminating other potential causal relationships, by using controls and duplicate samples, is the best way to ensure that your results stand up to rigorous questioning.

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Martyn Shuttleworth (Oct 20, 2008). Validity and Reliability. Retrieved Aug 04, 2024 from Explorable.com: https://explorable.com/validity-and-reliability

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The Validity of Psychological Research

How do we know whether a finding is legitimate or not.

Posted August 27, 2018

There is a distinction that one learns about on the first day of a course in psychological research methods. It is the difference between internal and external validity.

Internal validity is scientific validity. The extent to which a researcher devises a solid experiment, controls for confounding variables, and executes the procedure as planned determines a finding’s internal validity. If it were to come to the researcher’s attention that a confounding variable for which they did not control could also explain their result, then the finding’s internal validity would be called into question. This validity is concerned with what happens inside the lab, while the experiment is happening.

External validity, in contrast, is ecological validity. How well does the researcher’s finding generalize to the world outside the lab? You could control for all the variables perfectly, execute your procedure flawlessly, and run a pristine experiment. But if the stimuli you’re using aren’t representative of what people are likely to encounter in real life, then the experiment lacks external validity. This validity is concerned with what happens outside the lab, what to make of the result after all the nitty-gritty has been finely tuned.

Ideally, a researcher would conduct an experiment that is unassailably valid both internally and externally. However, in practice, these considerations usually involve a tradeoff . The more externally valid your stimuli—the more precisely they can be measured and controlled—the more sterile they become and thereby reflect less of the inherent messiness of our everyday experience. The more realistic you make your stimuli, the less meticulous you’re able to be about what exactly you’re showing your participant.

The upshot is that without internal validity, you can’t draw scientific conclusions. But without external validity, you have nothing worth drawing conclusions about. Practically speaking, the best a researcher can hope for is a healthy and reasonable balance between the two. But how well does psychological research actually balance the tension between these two considerations? Is it fifty-fifty, equal parts external and internal? Or does one get prioritized at the expense of the other?

There is an important asymmetry between internal and external validity, which gives insight into the answer to this question. It has to do with how these different kinds of validity are measured. Scientists are trained every day of their professional lives to be sensitive to internal validity. They can spot a confounding variable in a study from a mile away. And once one is identified, it’s difficult to shake it off as inconsequential to the study’s findings. Perhaps more importantly, it’s embarrassing for a scientist to run a shoddy experiment in which people can easily point out procedural flaws. It is, in short, relatively obvious how to optimize for internal validity.

But external validity is not so easily optimized for. It is much more difficult to point at an experiment and claim that it bears little resemblance to the real world in a crucial and undeniable way. Such an issue will be regarded as perhaps a good point, but ultimately just an opinion. The experimental stimuli aren’t intended to be representative of the whole of the human cognitive experience, after all, but only a specific part of it. That’s what makes the experimental variables so well controlled and the theoretical predictions so parsimonious in the first place. There is also no such embarrassment of an accusation about lack of external validity, but rather a certain pride associated with being a hardline scientist who studies her phenomena of interest with clinical precision and ardent fastidiousness.

The result is that is psychological research biases toward internal rather than external validity. It can more easily be measured, and consequently is a much more apparent badge that proclaims, “here is the work of a legitimate scientist.” Those scientists who prioritize internal validity and scientific legitimacy are better positioned for promotion, and this influences the constituency of the institution of psychological research as a whole to favor those who care more for the considerations of an internal than the external. The problem is that while psychological research becomes more ostensibly scientific, it becomes less connected to that which it intends to study. Human behavior is a fundamentally messy topic, and psychology benefits from existing in the tension between these two kinds of validity. Both are, after all, necessary for truly valid psychological research and neither is sufficient on its own. If we lose our sense of external validity because it is a trickier metric to optimize then psychological research suffers just as much as if we failed to construct solid experiments.

This is a consideration that gets overlooked in the “ replication crisis ” in which psychological research currently finds itself. The usual approach to addressing this crisis lies with better statistical analyses, which decrease the probability of a false or misleading finding. While this is surely a critical contribution to the internal validity of psychological findings, it takes external validity out from under the spotlight of attention. Psychology, the thinking goes, will not become healthy by an attempt to render it more externally valid, but only by making it more rigorously scientific.

This thinking, to my mind, is only one side of the story. Sure, psychology can improve its statistical methodology in the service of the field's betterment. But too stringent a focus on the internal runs the risk of leading to an equally illegitimate state of the science which pursues a significance that is more statistical than psychological. Our aversion to getting our hands dirty with the veridical messiness of the human experience may lead to us to forfeit the opportunity to go out there and actually work with nature itself , opting instead for the safety and clarity of the laboratory setting. This seems hardly like a psychology worth replicating.

Cody Kommers is a PhD student in Experimental Psychology at Oxford.

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What Makes Valid Research? How to Verify if a Source is Credible on the Internet

January 28, 2019 David Childs Democracy & Me Blog , The Role Of Media 57

By Dr. David Childs, Ph.D. Northern Kentucky University Introduction Computer and digital technology has increased at an astounding rate within the last several decades. With the advent of various informational Internet resources such as social media, online articles, books and so forth many people purport to do thorough research, but lack the understanding of what research means. The advent of search engines has given everyone the illusion that they have done research and are experts on a particular topic. In reality, people simply pull information from unreliable sources, thinking that they have researched a topic thoroughly. What makes a source not reliable? What makes certain information unreliable and untrustworthy? This article will offer information and resources to help people be able to differentiate between what is a valid source of knowledge and what is not. What is research? Research should involve a thorough reading and analysis of an adequate number of sources on a given subject. One does not have to have a college degree to do research. But the proper time should be devoted in order to draw valid conclusions that can be held up as reliable research. As a side note, some information cannot be obtained without proper research methodologies and even research tools. Examples of this is research in the natural sciences such as biology, chemistry or physics, or in the social sciences in areas such as history, economics or sociology. With the hard sciences one must conduct countless experiments to arrive at certain conclusions that cannot be obtained by simply reading a lot of Internet articles and watching videos. Furthermore, to do valid historical work one must study many reliable primary sources or conduct countless interviews with people who were present during a certain time period the historian is studying. So in this way, valid natural or social science experiments cannot be replaced by reading a few articles on the Internet. At the very least, one can read the work of experts who have devoted their life to research in a particular subject. Teachers in K-12 schools often have not spent their lives conducting research in their field (Of course there are many exceptions to this). Even though some teachers may not be researchers, they have devoted their lives to studying, reading and mastering their content. In this way, a middle school science teacher (for example) can read thoroughly within a certain discipline and gain a wide enough knowledge base on a topic to become a reliable source of information and somewhat of an expert. The knowledge they have gained was achieved through much time and effort. There is no shortcut for conducting research on a topic thoroughly and adequately. In contemporary times, when many individuals do research, their primary means of gathering information is through the Internet. The Internet can be a great resource for gathering information, problems arise when people cannot differentiate between reliable and unreliable sources. Below are some key components that one should consider when trying to verify if an online source is credible. How to Find Reliable Information on the Internet 1) Identify the source of the information and determine whether it is reliable and credible. A good starting point for this is to identify the name of the writer and or the organization from which the source was derived. Is the source reputable and reliable? Is the person or organization a respected authority on the subject matter? What makes a person or organization an authority on a particular topic? It has become very easy to publish information on the Internet and as a result there are many people purporting to be an expert in a particular field that are not qualified to write on that topic. A good way to understand the danger of this is to liken it to public school teachers teaching subjects outside of their certification in order to remedy teacher shortages. For example, one might find a teacher certified in social studies teaching high school math. In this cases, students are not getting the proper instruction in math. In the same way, there is a lot information on the Internet written by individuals that have no expertise in the particular content in which they are writing about. For example, many people that dispute climate change and global warming are not scientists and often rely on political rhetoric to support their claims. Scientists who do work in climate change have devoted their entire lives to research in that area, often holding undergraduate and several graduate degrees in subjects like geology and earth science. When a person is thought to be a well-known and respected expert in a certain field, they have a proven track record of careful study and research and are validated by reputable institutions that are known for producing reliable research. Often non-experts will spend just a few days or weeks “researching” climate change, in an effort to “dispute” data that is backed by decades of careful research. One does not have to have a Ph.D. to understand and challenge mainstream scientific knowledge, but time and energy devoted to research cannot be bypassed.    2) Checking sources for validity against other reliable sources. It is important when doing research on the Internet to check the provided information against other reliable sources to verify accuracy. For example, if every reputable source reports that cigarette smoking causes cancer and one source says otherwise, the lone source should be questioned until further notice because it has no credibility or way to verify its information. When checking facts and data for accuracy provided in an Internet source one should look for reliable and trusted sources. These might include academic articles, books, universities, museums, mainline reputable religious organizations, government agencies and academic associations. Libraries, universities and professional organizations usually provide reliable information. There is a growing public mistrust of long established institutions that has added to the level of uncertainty about knowledge. But it is important to know that institutions have credibility for good reason. Their history, information and knowledge base is backed by hard work, and long held traditions.    3) Is the information presented in a biased way? When one is reading an article or any information on the internet it is important to determine if that information has a specific agenda or goal in mind. What is the author’s agenda? Does the author or organization have a particular religious, sociological or political bent? These factors determine the validity of an information source. For example, oftentimes newspapers will feature op-ed pieces in which the author states up front that the article is largely based on their personal views. Therefore, when one reads an op-ed piece, they understand going into the article that it will be slanted to the right or left or toward a certain worldview. The article is not be completely useless, but the reader should realize they have to sort through the bias and decided what information is helpful to them in their research.  The reader should also search for possible bias in the information presented (Could be political, sociological, religious bias, or other ideas drawn from a particular worldview) and or even claims made that seem unrealistic or unreasonable with no evidence to back it up. 4) Search for citations that support the claims made by the author or organization. Most articles or information on the web will provide a link to do further research on the topic or to back claims made. When this information is not adequately provided one can assume that the source is not reputable. In addition, a site can have many citations but the sources may not be credible or reliable sources. Health and fitness writer Robin Reichert states the following about the topic reliable sources. Readers should “follow the links provided” in the article to “verify that the citations in fact support the writer’s claims. Look for at least two other credible citations to support the information.” Furthermore, readers should “always follow-up on citations that the writer provides to ensure that the assertions are supported by other sources.” It is also important to note that the end designation of a website can help determine credibility. When websites end in “.com” they are often are for profit organizations and trying to sell a product or service. When one comes across a site that ends in “.org” they are often non-profit organizations and thus have a particular social cause they are trying to advance or advocate for. Government agency websites always end in “.gov” while educational institutions end in “.edu.” Government agencies, educational institutions or non-profits generally offer reliable and trustworthy information. Teachers in middle and high schools attempt should spend more time having students do research papers as it teaches students the value of citing valid sources. The projects often call for proper citations using one of the various styles of citation with the most popular being APA, MLA and Chicago. How to Verify if a Source is Credible on the Internet Below I have provided a number of resources for our average internet researchers, students and teachers. The idea of truth and valid, reliable resources are being challenged because people are unsure as to what information is valid and what is not. The links below offer a number of resources that can further offer tools to help  to understand how to do research properly. Resources and References A Comprehensive Guide to APA Citations and Format EasyBib Guide to Citing and Writing in APA Format MLA General Format Formatting a Research Paper EasyBib Guide to MLA 8 Format Chicago Manual of Style 17th Edition Evaluating Internet Resources Check It Out: Verifying Information and Sources in News Coverage How to Do Research: A Step-By-Step Guide: Get Started How can I tell if a website is credible? Detecting Fake News at its Source: Machine learning system aims to determine if an information outlet is accurate or biased. What does “research” mean and are you doing it?

This is a great source of information. There are many times I am reading an article or a research paper revolving around my work. A lot of times I find the information is skewed by antidotal evidence or bias. In addition, what helps here is discussing what websites are more credible vs others. I had no idea .com and .org had differences. One being for profit and the other being not for profit. This goes into what kind of addenda they have and what they want the reader to learn vs providing all of the facts. Lastly, looking at the resources provided and the validity of them is very important. I just read an article today that was advocating for fire based ambulance services vs private and all of the sources were extremely old, none of which were from this or the last decade. So, how can I find the article credible? Bottom line, I can’t.

I thought this article was very informative and gave great information on determining if a resource is reliable or not. I feel like we were never necessarily taught how to find reliable resources. There is a lot of “fake information” online and it can be hard to tell what an accurate resource is and what is not an accurate resource. I thought this article gave some great ways to make sure you have a credible resource. I think this is what is wrong with technology though, there is a lot of fake news that people think is real and from there it creates numerous inaccurate ideas.

I have always had a hard time finding credible resources when I have had to do research for assignments. Especially since the pandemic hit, I think it’s even harder to find credible sources because of all the fake news that has been spread. When I use an online resource, I never put much thought into thinking if it is credible enough or not. If I find a resource that fits, I use it.

I’m a very naive and gullible person that overlooks the sources of where I found the information. Fake news is also more popular than ever and I like how this article helps depict articles to decipher if they are fake or legitimate

I like that this article explains how to properly identify a credible source. We live in a time where it is so easy to believe sources online. It is easier than every for people to upload any information online for people to access and eventually use as not-credible sources.

I like how this article forms a cohesive and understandable format for checking for reliable resources. It also shows how to think critically about the articles used for research.

I like that this article informs about whether an article is credible or not. Doing pre -research to make sure that you are getting the same information for all of your sources. I like that the article tells us to look at bias in our sources because that is a really big factor.

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What Are Survey Validity and Reliability?

Let’s start by agreeing that it isn’t always easy to measure people’s attitudes, thoughts, and feelings. People are complex. They may not always want to divulge what they really think or they may not be able to accurately report what they think. Nevertheless, behavioral scientists persist, using surveys, experiments, and observations to learn why people do what they do.   

At the heart of good research methods are two concepts known as survey validity and reliability. Digging into these concepts can get a bit wonky. However, understanding validity and reliability is important for both the people who conduct and consume research. Thus, we lay out the details of both constructs in this blog.

What is Survey Validity?

Validity refers to how reasonable, accurate, and justifiable a claim, conclusion, or decision is. Within the context of survey research, validity is the answer to the question: does this research show what it claims to show? There are four types of validity within survey research.

Four Types of Survey Validity

• Statistical validity

Statistical validity is an assessment of how well the numbers in a study support the claims being made. Suppose a survey says 25% of people believe the Earth is flat. An assessment of statistical validity asks whether that 25% is based on a sample of 12 or 12,000.

There is no one way to evaluate claims of statistical validity. For a survey or poll, judgments of statistical validity may entail looking at the margin of error. For studies that examine the association between multiple variables or conduct an experiment, judgments of statistical validity may entail examining the study’s effect size or statistical significance . Regardless of the particulars of the study, statistical validity is concerned with whether what the research claims is supported by the data.

• Construct validity

Construct validity is an assessment of how well a research team has measured or manipulated the variable(s) in their study. Assessments of construct validity can range from a subjective judgment about whether questions look like they measure what they’re supposed to measure to a mathematical assessment of how well different questions or measures are related to each other.

• Face validity – Do the items used in a study look like they measure what they’re supposed to? That’s the type of judgment researchers make when assessing face validity. There’s no fancy math, just a judgment about whether things look right on the surface. 

Face validity is sometimes assessed by experts. In the case of a survey instrument to measure beliefs about whether the earth is flat, a researcher may want to show the initial version of the instrument to an expert on the flat earth theory to get their feedback as to whether the items look right.

• Content validity – Content validity is a judgment about whether your survey instrument captures all the relevant components of what you’re trying to measure.  

For example, suppose we wrote 10 items to measure flat-Earth beliefs. An assessment of content validity would judge how well these questions cover different conceptual components of the flat-Earth conspiracy. 

Obviously, the scale would need to include items measuring people’s beliefs about the shape of the Earth (e.g., do you believe the Earth is flat?). But given how much flat-Earth beliefs contradict basic science and information from official channels like NASA, we might also include questions that measure trust in science (e.g., The scientific method usually leads to accurate conclusions) and government institutions (e.g., Most of what NASA says about the shape of the Earth is false). 

Content validity is one of the most important aspects of validity, and it largely depends on one’s theory about the construct. For example, if one’s theory of intelligence includes creativity as a component (creativity is part of the ‘content’ of intelligence) a test cannot be valid if it does not measure creativity. Many theoretical disagreements about measurement center around content validity. 

• Criterion validity – Unlike face validity and content validity, criterion validity is a more objective measure of whether an item or scale measures what it is supposed to measure. 

To establish criterion validity researchers may look to see if their instrument predicts a concrete, real world-behavior. In our flat-Earth example, we might assess whether people who score high in flat-Earth beliefs spend more time watching flat-Earth videos on YouTube or attend flat-Earth events. If people who score high on the measure also tend to engage in behaviors associated with flat-Earth beliefs, we have evidence of criterion validity.

• External validity

Almost all research relies on sampling . Because researchers do not have the time and resources to talk to everyone they are interested in studying, they often rely on a sample of people to make inferences about a larger population. 

External validity is concerned with assessing how well the findings from a single study apply to people, settings, and circumstances not included in the study. In other words, external validity is concerned with how well the results from a study generalize to other people, places, and situations.

Perhaps the easiest way to think about external validity is with polling. Opinion polls ask a sample of people what they think about a policy, topic, or political candidate at a particular moment. An assessment of external validity considers how the sample was gathered and whether it is likely that people in the sample represent people in the population who did not participate in the research. With some types of research such as polling, external validity is always a concern .    

• Internal validity (for experiments)

Finally, a fourth type of validity that only applies to experiments or A/B tests is internal validity. Internal validity assesses whether the research team has designed and carried out their work in a way that allows you to have confidence that the results of their study are due only to the manipulated (i.e. independent) variables. 

What is Survey Reliability? 

Everyone knows what it means for something to be reliable. Reliable things are dependable and consistent. Survey reliability means the same thing. When assessing reliability, researchers want to know whether the measures they use produce consistent and dependable results.

Imagine you’re interested in measuring whether people believe in the flat-Earth conspiracy theory. According to some polling, as many as 1 in 6 U.S. adults are unsure if the Earth is round. 

If beliefs about the roundness of the Earth are the construct we’re interested in measuring, we have to decide how to operationalize , or measure, that construct. Often, researchers operationalize a construct with a survey instrument—questions intended to measure a belief or attitude. At other times, a construct can be operationalized by observing behavior or people’s verbal or written descriptions of a topic.

Whichever way a construct is operationalized, researchers need to know whether their measures are reliable, and reliability is often assessed in three different ways. 

3 Ways to Assess Survey Reliability

• Test-retest reliability

If I asked 1,000 people today if they believe the Earth is round and asked the same questions next week or next month, would the results be similar? If so, then we would say the questions have high test-retest reliability. Questions that produce different results each time participants answer them have poor reliability and are not useful for research. 

• Internal reliability

Internal reliability applies to measures with multiple self-report items. So, if we created a 10-item instrument to measure belief in a flat-Earth, an assessment of internal reliability would examine whether people who tend to agree with one item (e.g., the Earth is flat) also agree with other items in the scale (e.g., images from space showing the Earth as round are fake).   

• Interrater reliability

Sometimes, researchers collect data that requires judgment about participants’ responses. Imagine, for example, observing people’s behavior within an internet chat room devoted to the flat-Earth conspiracy. One way to measure belief in a flat-Earth would be to make judgments about how much each person’s postings indicate their belief that the Earth is flat. 

Interrater reliability is an assessment of how well the judgments of two or more different raters agree with one another. So, if one coder believes that a participant’s written response indicates a strong belief in a flat-Earth, how likely is another person to independently agree.   

Measuring Survey Reliability and Validity: Putting Things Together

The information above is technical. So, how do people evaluate reliability and validity in the real world? Do they work through a checklist of the concepts above? Not really. 

When evaluating research, judgments of reliability and validity are often based on a mixture of information provided by the research team and critical evaluation by the consumer. Take, for example, the polling question about flat-Earth beliefs at the beginning.

The data suggesting that as many as 1 in 6 U.S. adults are unsure about the shape of the Earth was released by a prominent polling organization. In their press release, the organization claimed that just 84% of U.S. adults believe that the earth is round. But is that true?

To evaluate the validity of this claim we might inspect the questions that were asked (face validity), what the margin of error is and how many people participated in the poll (statistical validity), and where the participants came from and how they were sampled (external validity). 

In assessing these characteristics, we might ask whether we would get the same result with differently worded questions, whether there were enough people in the poll to feel confident about the margin of error, and whether another sample of adults would produce the same or different results.

Some forms of reliability and validity are harder to pin down than others. But without considering reliability and validity it is hard to evaluate whether any form of research really shows what it claims to show. 

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• J Indian Prosthodont Soc
• v.18(1); Jan-Mar 2018

Study validity

N. gopi chander.

Professor, Department of Prosthodontics, SRM Dental College, SRM University, Chennai, Tamil Nadu, India

The country on average produces 500 prosthodontic research studies annually. Lesser studies among these are getting translated into progressive research. These findings are unable to translate to advanced stage due to major limitations in the study design, methodology, motivation, interest, and funding. The study design, instrumentation used, and data collection make the inferences obtained from these studies less valid. The validity of the study design is essential for both in terms of internal acceptance for standardization and in terms of external recognition for universal acceptance.[ 1 ]

The name valid is derived from the Latin word validus indicating strong. This implies that the design and methodology followed should be strong and accepted globally. Validity in the study design denotes that the accuracy, trustworthiness of instruments used, and data or findings collected are highly ordered and obtained with a reduced systemic error. When the validity is within acceptable limits, it aids in wider acceptance and it leads to progressive research.

The validity is of two types: internal validity and external validity. The internal validity is the steps taken or standards followed by the researchers in the study environment to obtain the truthful results. The external validity is the generalization followed for wider acceptance of global population.[ 2 ] Although these validation procedures are essential for the clinical studies, greater care is necessary for in vitro studies for the progressive research.

Numerous factors affect the validity of the study. The internal validity is affected by the size of the subject/specimen, type or variability of the subject, attrition of the samples, maturation, time taken for evaluation, history, and instrument or assessment sensitivity.[ 3 ] The external validity is controlled by population representation, time/duration of evaluation, research environment, researcher characteristics, data collection, interaction of the subject to research, and control of independent variables.[ 4 ] It is essential that these factors are understood in study design and controlled for robust study design and acceptance.

The study validity can be evaluated by translation or criteria. It can also be measured by content, face, predictable, creative, concurrent, convergent and divergent, or dissimilar measures of validity.[ 4 ] The validity in the study can be improved by defining the aim and objective of the study, synchronizing the assessment measures to the objectives. In addition, it is advisable to compare with the outside environment or external measure for wider acceptance.[ 5 ]

The structure of the study design can have different levels of validity. The randomized clinical trial has higher internal validity than cohort, case-control, or cross-sectional studies. The observational studies have higher external validity compared to interventional studies. Adequate measure should be followed to avoid the issues, and it has to be optimized to obtain the essential validity in the study. The adaptation of appropriate study protocols such as CONSORT and STROBE aids in obtaining essential standardization. In vitro studies following the regular guidelines listed by the ISO, ADA, and BIS can establish higher norms and acceptance.[ 6 ]

Adherence to the study design, protocol, and following the validity measures aids in better appreciation of the studies and can enhance the translatory research to an advanced stage.

Validity, Accuracy and Reliability Explained with Examples

This is part of the NSW HSC science curriculum part of the Working Scientifically skills.

Part 1 – Validity

Part 2 – Accuracy

Part 3 – Reliability

Science experiments are an essential part of high school education, helping students understand key concepts and develop critical thinking skills. However, the value of an experiment lies in its validity, accuracy, and reliability. Let's break down these terms and explore how they can be improved and reduced, using simple experiments as examples.

Target Analogy to Understand Accuracy and Reliability

The target analogy is a classic way to understand the concepts of accuracy and reliability in scientific measurements and experiments. 

Accuracy refers to how close a measurement is to the true or accepted value. In the analogy, it's how close the arrows come to hitting the bullseye (represents the true or accepted value).

Reliability  refers to the consistency of a set of measurements. Reliable data can be reproduced under the same conditions. In the analogy, it's represented by how tightly the arrows are grouped together, regardless of whether they hit the bullseye. Therefore, we can have scientific results that are reliable but inaccurate.

• Validity  refers to how well an experiment investigates the aim or tests the underlying hypothesis. While validity is not represented in this target analogy, the validity of an experiment can sometimes be assessed by using the accuracy of results as a proxy. Experiments that produce accurate results are likely to be valid as invalid experiments usually do not yield accurate result.

Validity refers to how well an experiment measures what it is supposed to measure and investigates the aim.

Ask yourself the questions:

• "Is my experimental method and design suitable?"
• "Is my experiment testing or investigating what it's suppose to?"

For example, if you're investigating the effect of the volume of water (independent variable) on plant growth, your experiment would be valid if you measure growth factors like height or leaf size (these would be your dependent variables).

However, validity entails more than just what's being measured. When assessing validity, you should also examine how well the experimental methodology investigates the aim of the experiment.

Assessing Validity

An experiment’s procedure, the subsequent methods of analysis of the data, the data itself, and the conclusion you draw from the data, all have their own associated validities. It is important to understand this division because there are different factors to consider when assessing the validity of any single one of them. The validity of an experiment as a whole , depends on the individual validities of these components.

When assessing the validity of the procedure , consider the following:

• Does the procedure control all necessary variables except for the dependent and independent variables? That is, have you isolated the effect of the independent variable on the dependent variable?
• Does this effect you have isolated actually address the aim and/or hypothesis?
• Does your method include enough repetitions for a reliable result? (Read more about reliability below)

When assessing the validity of the method of analysis of the data , consider the following:

• Does the analysis extrapolate or interpolate the experimental data? Generally, interpolation is valid, but extrapolation is invalid. This because by extrapolating, you are ‘peering out into the darkness’ – just because your data showed a certain trend for a certain range it does not mean that this trend will hold for all.
• Does the analysis use accepted laws and mathematical relationships? That is, do the equations used for analysis have scientific or mathematical base? For example, `F = ma` is an accepted law in physics, but if in the analysis you made up a relationship like `F = ma^2` that has no scientific or mathematical backing, the method of analysis is invalid.
• Is the most appropriate method of analysis used? Consider the differences between using a table and a graph. In a graph, you can use the gradient to minimise the effects of systematic errors and can also reduce the effect of random errors. The visual nature of a graph also allows you to easily identify outliers and potentially exclude them from analysis. This is why graphical analysis is generally more valid than using values from tables.

When assessing the validity of your results , consider the following: 

• Is your primary data (data you collected from your own experiment) BOTH accurate and reliable? If not, it is invalid.
• Are the secondary sources you may have used BOTH reliable and accurate?

When assessing the validity of your conclusion , consider the following:

• Does your conclusion relate directly to the aim or the hypothesis?

How to Improve Validity

Ways of improving validity will differ across experiments. You must first identify what area(s) of the experiment’s validity is lacking (is it the procedure, analysis, results, or conclusion?). Then, you must come up with ways of overcoming the particular weakness. 

Below are some examples of this.

Example – Validity in Chemistry Experiment 

Let's say we want to measure the mass of carbon dioxide in a can of soft drink.

The following steps are followed:

• Weigh an unopened can of soft drink on an electronic balance.
• Open the can.
• Place the can on a hot plate until it begins to boil.
• When cool, re-weigh the can to determine the mass loss.

To ensure this experiment is valid, we must establish controlled variables:

• type of soft drink used
• temperature at which this experiment is conducted
• period of time before soft drink is re-weighed

Despite these controlled variables, this experiment is invalid because it actually doesn't help us measure the mass of carbon dioxide in the soft drink. This is because by heating the soft drink until it boils, we are also losing water due to evaporation. As a result, the mass loss measured is not only due to the loss of carbon dioxide, but also water. A simple way to improve the validity of this experiment is to not heat it; by simply opening the can of soft drink, carbon dioxide in the can will escape without loss of water.

Example – Validity in Physics Experiment

Let's say we want to measure the value of gravitational acceleration `g` using a simple pendulum system, and the following equation:

$$T = 2\pi \sqrt{\frac{l}{g}}$$

• `T` is the period of oscillation
• `l` is the length of string attached to the mass
• `g` is the acceleration due to gravity

• Cut a piece of a string or dental floss so that it is 1.0 m long.
• Attach a 500.0 g mass of high density to the end of the string.
• Attach the other end of the string to the retort stand using a clamp.
• Starting at an angle of less than 10º, allow the pendulum to swing and measure the pendulum’s period for 10 oscillations using a stopwatch.
• Repeat the experiment with 1.2 m, 1.5 m and 1.8 m strings.

The controlled variables we must established in this experiment include:

• mass used in the pendulum
• location at which the experiment is conducted

The validity of this experiment depends on the starting angle of oscillation. The above equation (method of analysis) is only true for small angles (`\theta < 15^{\circ}`) such that `\sin \theta = \theta`. We also want to make sure the pendulum system has a small enough surface area to minimise the effect of air resistance on its oscillation.

In this instance, it would be invalid to use a pair of values (length and period) to calculate the value of gravitational acceleration. A more appropriate method of analysis would be to plot the length and period squared to obtain a linear relationship, then use the value of the gradient of the line of best fit to determine the value of `g`. 

Accuracy refers to how close the experimental measurements are to the true value.

Accuracy depends on

• the validity of the experiment
• the degree of error:
• systematic errors are those that are systemic in your experiment. That is, they effect every single one of your data points consistently, meaning that the cause of the error is always present. For example, it could be a badly calibrated temperature gauge that reports every reading 5 °C above the true value.
• random errors are errors that occur inconsistently. For example, the temperature gauge readings might be affected by random fluctuations in room temperature. Some readings might be above the true value, some might then be below the true value.
• sensitivity of equipment used.

Assessing Accuracy 

The effect of errors and insensitive equipment can both be captured by calculating the percentage error:

$$\text{% error} = \frac{\text{|experimental value – true value|}}{\text{true value}} \times 100%$$

Generally, measurements are considered accurate when the percentage error is less than 5%. You should always take the context of the experimental into account when assessing accuracy. 

While accuracy and validity have different definitions, the two are closely related. Accurate results often suggest that the underlying experiment is valid, as invalid experiments are unlikely to produce accurate results.

In a simple pendulum experiment, if your measurements of the pendulum's period are close to the calculated value, your experiment is accurate. A table showing sample experimental measurements vs accepted values from using the equation above is shown below. 

All experimental values in the table above are within 5% of accepted (theoretical) values, they are therefore considered as accurate. 

How to Improve Accuracy

• Remove systematic errors : for example, if the experiment’s measuring instruments are poorly calibrated, then you should correctly calibrate it before doing the experiment again.
• Reduce the influence of random errors : this can be done by having more repetitions in the experiment and reporting the average values. This is because if you have enough of these random errors – some above the true value and some below the true value – then averaging them will make them cancel each other out This brings your average value closer and closer to the true value.
• Use More Sensitive Equipments: For example, use a recording to measure time by analysing motion of an object frame by frame, instead of using a stopwatch. The sensitivity of an equipment can be measured by the limit of reading . For example, stopwatches may only measure to the nearest millisecond – that is their limit of reading. But recordings can be analysed to the frame. And, depending on the frame rate of the camera, this could mean measuring to the nearest microsecond.
• Obtain More Measurements and Over a Wider Range:  In some cases, the relationship between two variables can be more accurately determined by testing over a wider range. For example, in the pendulum experiment, periods when strings of various lengths are used can be measured. In this instance, repeating the experiment does not relate to reliability because we have changed the value of the independent variable tested.

Reliability

Reliability involves the consistency of your results over multiple trials.

Assessing Reliability

The reliability of an experiment can be broken down into the reliability of the procedure and the reliability of the final results.

The reliability of the procedure refers to how consistently the steps of your experiment produce similar results. For example, if an experiment produces the same values every time it is repeated, then it is highly reliable. This can be assessed quantitatively by looking at the spread of measurements, using statistical tests such as greatest deviation from the mean, standard deviations, or z-scores.

Ask yourself: "Is my result reproducible?"

The reliability of results cannot be assessed if there is only one data point or measurement obtained in the experiment. There must be at least 3. When you're repeating the experiment to assess the reliability of its results, you must follow the  same steps , use the  same value  for the independent variable. Results obtained from methods with different steps cannot be assessed for their reliability.

Obtaining only one measurement in an experiment is not enough because it could be affected by errors and have been produced due to pure chance. Repeating the experiment and obtaining the same or similar results will increase your confidence that the results are reproducible (therefore reliable).

In the soft drink experiment, reliability can be assessed by repeating the steps at least three times:

The mass loss measured in all three trials are fairly consistent, suggesting that the reliability of the underly method is high.

The reliability of the final results refers to how consistently your final data points (e.g. average value of repeated trials) point towards the same trend. That is, how close are they all to the trend line? This can be assessed quantitatively using the `R^2` value. `R^2` value ranges between 0 and 1, a value of 0 suggests there is no correlation between data points, and a value of 1 suggests a perfect correlation with no variance from trend line.

In the pendulum experiment, we can calculate the `R^2` value (done in Excel) by using the final average period values measured for each pendulum length.

Here, a `R^2` value of 0.9758 suggests the four average values are fairly close to the overall linear trend line (low variance from trend line). Thus, the results are fairly reliable. 

How to Improve Reliability

A common misconception is that increasing the number of trials increases the reliability of the procedure . This is not true. The only way to increase the reliability of the procedure is to revise it. This could mean using instruments that are less susceptible to random errors, which cause measurements to be more variable.

Increasing the number of trials actually increases the reliability of the final results . This is because having more repetitions reduces the influence of random errors and brings the average values closer to the true values. Generally, the closer experimental values are to true values, the closer they are to the true trend. That is, accurate data points are generally reliable and all point towards the same trend.

Reliable but Inaccurate / Invalid

It is important to understand that results from an experiment can be reliable (consistent), but inaccurate (deviate greatly from theoretical values) and/or invalid. In this case, your procedure  is reliable, but your final results likely are not.

Examples of Reliability

Using the soft drink example again, if the mass losses measured for three soft drinks (same brand and type of drink) are consistent, then it's reliable. 

Using the pendulum example again, if you get similar period measurements every time you repeat the experiment, it’s reliable.  

However, in both cases, if the underlying methods are invalid, the consistent results would be invalid and inaccurate (despite being reliable).

Do you have trouble understanding validity, accuracy or reliability in your science experiment or depth study?

Consider getting personalised help from our 1-on-1 mentoring program !

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Textbooks and handbooks and manuals will have a secondary function for chemists and physicists and biologists, of course. But in the sciences, articles are the standard and  preferred publication form. 

In the social sciences and humanities , where knowledge develops a little less rapidly or is driven less by issues of time-sensitivity , journal articles and books are more often used together.

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