what makes a marketing research study valid and reliable

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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• 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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• Reliability vs Validity in Research: Types & Examples

In everyday life, we probably use reliability to describe how something is valid. However, in research and testing, reliability and validity are not the same things.

When it comes to data analysis, reliability refers to how easily replicable an outcome is. For example, if you measure a cup of rice three times, and you get the same result each time, that result is reliable.

The validity, on the other hand, refers to the measurement’s accuracy. This means that if the standard weight for a cup of rice is 5 grams, and you measure a cup of rice, it should be 5 grams.

So, while reliability and validity are intertwined, they are not synonymous. If one of the measurement parameters, such as your scale, is distorted, the results will be consistent but invalid.

Data must be consistent and accurate to be used to draw useful conclusions. In this article, we’ll look at how to assess data reliability and validity, as well as how to apply it.

Read: Internal Validity in Research: Definition, Threats, Examples

What is Reliability?

When a measurement is consistent it’s reliable. But of course, reliability doesn’t mean your outcome will be the same, it just means it will be in the same range. 

For example, if you scored 95% on a test the first time and the next you score, 96%, your results are reliable.  So, even if there is a minor difference in the outcomes, as long as it is within the error margin, your results are reliable.

Reliability allows you to assess the degree of consistency in your results. So, if you’re getting similar results, reliability provides an answer to the question of how similar your results are.

What is Validity?

A measurement or test is valid when it correlates with the expected result. It examines the accuracy of your result.

Here’s where things get tricky: to establish the validity of a test, the results must be consistent. Looking at most experiments (especially physical measurements), the standard value that establishes the accuracy of a measurement is the outcome of repeating the test to obtain a consistent result.

Read: What is Participant Bias? How to Detect & Avoid It

For example, before I can conclude that all 12-inch rulers are one foot, I must repeat the experiment several times and obtain very similar results, indicating that 12-inch rulers are indeed one foot.

Most scientific experiments are inextricably linked in terms of validity and reliability. For example, if you’re measuring distance or depth, valid answers are likely to be reliable.

But for social experiences, one isn’t the indication of the other. For example, most people believe that people that wear glasses are smart. 

Of course, I’ll find examples of people who wear glasses and have high IQs (reliability), but the truth is that most people who wear glasses simply need their vision to be better (validity). 

So reliable answers aren’t always correct but valid answers are always reliable.

How Are Reliability and Validity Assessed?

When assessing reliability, we want to know if the measurement can be replicated. Of course, we’d have to change some variables to ensure that this test holds, the most important of which are time, items, and observers.

If the main factor you change when performing a reliability test is time, you’re performing a test-retest reliability assessment.

Read: What is Publication Bias? (How to Detect & Avoid It)

However, if you are changing items, you are performing an internal consistency assessment. It means you’re measuring multiple items with a single instrument.

Finally, if you’re measuring the same item with the same instrument but using different observers or judges, you’re performing an inter-rater reliability test.

Assessing Validity

Evaluating validity can be more tedious than reliability. With reliability, you’re attempting to demonstrate that your results are consistent, whereas, with validity, you want to prove the correctness of your outcome.

Although validity is mainly categorized under two sections (internal and external), there are more than fifteen ways to check the validity of a test. In this article, we’ll be covering four.

First, content validity, measures whether the test covers all the content it needs to provide the outcome you’re expecting. 

Suppose I wanted to test the hypothesis that 90% of Generation Z uses social media polls for surveys while 90% of millennials use forms. I’d need a sample size that accounts for how Gen Z and millennials gather information.

Next, criterion validity is when you compare your results to what you’re supposed to get based on a chosen criteria. There are two ways these could be measured, predictive or concurrent validity.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Following that, we have face validity . It’s how we anticipate a test to be. For instance, when answering a customer service survey, I’d expect to be asked about how I feel about the service provided.

Lastly, construct-related validity . This is a little more complicated, but it helps to show how the validity of research is based on different findings.

As a result, it provides information that either proves or disproves that certain things are related.

Types of Reliability

We have three main types of reliability assessment and here’s how they work:

1) Test-retest Reliability

This assessment refers to the consistency of outcomes over time. Testing reliability over time does not imply changing the amount of time it takes to conduct an experiment; rather, it means repeating the experiment multiple times in a short time.

For example, if I measure the length of my hair today, and tomorrow, I’ll most likely get the same result each time. 

A short period is relative in terms of reliability; two days for measuring hair length is considered short. But that’s far too long to test how quickly water dries on the sand.

A test-retest correlation is used to compare the consistency of your results. This is typically a scatter plot that shows how similar your values are between the two experiments.

If your answers are reliable, your scatter plots will most likely have a lot of overlapping points, but if they aren’t, the points (values) will be spread across the graph.

Read: Sampling Bias: Definition, Types + [Examples]

2) Internal Consistency

It’s also known as internal reliability. It refers to the consistency of results for various items when measured on the same scale.

This is particularly important in social science research, such as surveys, because it helps determine the consistency of people’s responses when asked the same questions.

Most introverts, for example, would say they enjoy spending time alone and having few friends. However, if some introverts claim that they either do not want time alone or prefer to be surrounded by many friends, it doesn’t add up.

These people who claim to be introverts or one this factor isn’t a reliable way of measuring introversion.

Internal reliability helps you prove the consistency of a test by varying factors. It’s a little tough to measure quantitatively but you could use the split-half correlation .

The split-half correlation simply means dividing the factors used to measure the underlying construct into two and plotting them against each other in the form of a scatter plot.

Introverts, for example, are assessed on their need for alone time as well as their desire to have as few friends as possible. If this plot is dispersed, likely, one of the traits does not indicate introversion.

3) Inter-Rater Reliability

This method of measuring reliability helps prevent personal bias. Inter-rater reliability assessment helps judge outcomes from the different perspectives of multiple observers.

A good example is if you ordered a meal and found it delicious. You could be biased in your judgment for several reasons, perception of the meal, your mood, and so on.

But it’s highly unlikely that six more people would agree that the meal is delicious if it isn’t. Another factor that could lead to bias is expertise. Professional dancers, for example, would perceive dance moves differently than non-professionals. 

Read: What is Experimenter Bias? Definition, Types & Mitigation

So, if a person dances and records it, and both groups (professional and unprofessional dancers) rate the video, there is a high likelihood of a significant difference in their ratings.

But if they both agree that the person is a great dancer, despite their opposing viewpoints, the person is likely a great dancer.

Types of Validity

Researchers use validity to determine whether a measurement is accurate or not. The accuracy of measurement is usually determined by comparing it to the standard value.

When a measurement is consistent over time and has high internal consistency, it increases the likelihood that it is valid.

1) Content Validity

This refers to determining validity by evaluating what is being measured. So content validity tests if your research is measuring everything it should to produce an accurate result.

For example, if I were to measure what causes hair loss in women. I’d have to consider things like postpartum hair loss, alopecia, hair manipulation, dryness, and so on.

By omitting any of these critical factors, you risk significantly reducing the validity of your research because you won’t be covering everything necessary to make an accurate deduction. 

Read: Data Cleaning: 7 Techniques + Steps to Cleanse Data

For example, a certain woman is losing her hair due to postpartum hair loss, excessive manipulation, and dryness, but in my research, I only look at postpartum hair loss. My research will show that she has postpartum hair loss, which isn’t accurate.

Yes, my conclusion is correct, but it does not fully account for the reasons why this woman is losing her hair.

2) Criterion Validity

This measures how well your measurement correlates with the variables you want to compare it with to get your result. The two main classes of criterion validity are predictive and concurrent.

3) Predictive validity

It helps predict future outcomes based on the data you have. For example, if a large number of students performed exceptionally well in a test, you can use this to predict that they understood the concept on which the test was based and will perform well in their exams.

4) Concurrent validity

On the other hand, involves testing with different variables at the same time. For example, setting up a literature test for your students on two different books and assessing them at the same time.

You’re measuring your students’ literature proficiency with these two books. If your students truly understood the subject, they should be able to correctly answer questions about both books.

5) Face Validity

Quantifying face validity might be a bit difficult because you are measuring the perception validity, not the validity itself. So, face validity is concerned with whether the method used for measurement will produce accurate results rather than the measurement itself.

If the method used for measurement doesn’t appear to test the accuracy of a measurement, its face validity is low.

Here’s an example: less than 40% of men over the age of 20 in Texas, USA, are at least 6 feet tall. The most logical approach would be to collect height data from men over the age of twenty in Texas, USA.

However, asking men over the age of 20 what their favorite meal is to determine their height is pretty bizarre. The method I am using to assess the validity of my research is quite questionable because it lacks correlation to what I want to measure.

6) Construct-Related Validity

Construct-related validity assesses the accuracy of your research by collecting multiple pieces of evidence. It helps determine the validity of your results by comparing them to evidence that supports or refutes your measurement.

7) Convergent validity

If you’re assessing evidence that strongly correlates with the concept, that’s convergent validity . 

8) Discriminant validity

Examines the validity of your research by determining what not to base it on. You are removing elements that are not a strong factor to help validate your research. Being a vegan, for example, does not imply that you are allergic to meat.

How to Ensure Validity and Reliability in Your Research

You need a bulletproof research design to ensure that your research is both valid and reliable. This means that your methods, sample, and even you, the researcher, shouldn’t be biased.

• Ensuring Reliability

To enhance the reliability of your research, you need to apply your measurement method consistently. The chances of reproducing the same results for a test are higher when you maintain the method you’re using to experiment.

For example, you want to determine the reliability of the weight of a bag of chips using a scale. You have to consistently use this scale to measure the bag of chips each time you experiment.

You must also keep the conditions of your research consistent. For instance, if you’re experimenting to see how quickly water dries on sand, you need to consider all of the weather elements that day.

So, if you experimented on a sunny day, the next experiment should also be conducted on a sunny day to obtain a reliable result.

Read: Survey Methods: Definition, Types, and Examples

• Ensuring Validity

There are several ways to determine the validity of your research, and the majority of them require the use of highly specific and high-quality measurement methods.

Before you begin your test, choose the best method for producing the desired results. This method should be pre-existing and proven.

Also, your sample should be very specific. If you’re collecting data on how dogs respond to fear, your results are more likely to be valid if you base them on a specific breed of dog rather than dogs in general.

Validity and reliability are critical for achieving accurate and consistent results in research. While reliability does not always imply validity, validity establishes that a result is reliable. Validity is heavily dependent on previous results (standards), whereas reliability is dependent on the similarity of your results.

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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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Key Takeaways:

• Types of validity to consider during quantitative research include internal, external, construct, and statistical
• Types of reliability that apply to quantitative research include test re-test, inter-rater, internal consistency, and parallel forms
• There are numerous challenges to achieving validity and reliability in quantitative research, but the right techniques can help overcome them

Quantitative research is used to investigate and analyze data to draw meaningful conclusions. Validity and reliability are two critical concepts in quantitative analysis that ensure the accuracy and consistency of the research results. Validity refers to the extent to which the research measures what it intends to measure, while reliability refers to the consistency and reproducibility of the research results over time. Ensuring validity and reliability is crucial in conducting high-quality research, as it increases confidence in the findings and conclusions drawn from the data.

This article aims to provide an in-depth analysis of the significance of validity and reliability in quantitative research. It will explore the different types of validity and reliability, their interrelationships, and the associated challenges and limitations.

In this Article:

The role of validity in quantitative research, the role of reliability in quantitative research, validity and reliability: how they differ and interrelate, challenges and limitations of ensuring validity and reliability, overcoming challenges and limitations to achieve validity and reliability, explore trusted quantitative solutions.

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Validity is crucial in maintaining the credibility and reliability of quantitative research outcomes. Therefore, it is critical to establish that the variables being measured in a study align with the research objectives and accurately reflect the phenomenon being investigated.

Several types of validity apply to various study designs; let’s take a deeper look at each one below:

Internal validity is concerned with the extent to which a study establishes a causal relationship between the independent and dependent variables. In other words, internal validity determines whether the changes observed in the conditional variable result from changes in the independent variable or some other factor.

External validity refers to the degree to which the findings of a study can be generalized to other populations and contexts. External validity helps ensure the results of a study are not limited to the specific people or context in which the study was conducted.

Construct validity refers to the degree to which a research study accurately measures the theoretical construct it intends to measure. Construct validity helps provide alignment between the study’s measures and the theoretical concept it aims to investigate.

Finally, statistical validity refers to the accuracy of the statistical tests used to analyze the data. Establishing statistical validity provides confidence that the conclusions drawn from the data are reliable and accurate.

To safeguard the validity of a study, researchers must carefully design their research methodology, select appropriate measures, and control for extraneous variables that may impact the results. Validity is especially crucial in fields such as medicine, where inaccurate research findings can have severe consequences for patients and healthcare practices.

Ensuring the consistency and reproducibility of research outcomes over time is crucial in quantitative research, and this is where the concept of reliability comes into play. Reliability is vital to building trust in the research findings and their ability to be replicated in diverse contexts.

Similar to validity, multiple types of reliability are pertinent to different research designs. Let’s take a closer look at each of these types of reliability below:

Test-retest reliability refers to the consistency of the results obtained when the same test is administered to the same group of participants at different times. This type of reliability is essential when researchers need to administer the same test multiple times to assess changes in behavior or attitudes over time.

Inter-rater reliability refers to the results’ consistency when different raters or observers monitor the same behavior or phenomenon. This type of reliability is vital when researchers are required to rely on different individuals to rate or observe the same behavior or phenomenon.

Internal consistency reliability refers to the degree to which the items or questions in a test or questionnaire measure the same construct. This type of reliability is important in studies where researchers use multiple items or questions to assess a particular construct, such as knowledge or quality of life.

Lastly, parallel forms reliability refers to the consistency of the results obtained when two different versions of the same test are administered to the same group of participants. This type of reliability is important when researchers administer different versions of the same test to assess the consistency of the results.

Reliability in research is like the accuracy and consistency of a medical test. Just as a reliable medical test produces consistent and accurate results that physicians can trust to make informed decisions about patient care, a highly reliable study produces consistent and precise findings that researchers can trust to make knowledgeable conclusions about a particular phenomenon. To ensure reliability in a study, researchers must carefully select appropriate measures and establish protocols for administering the measures consistently. They must also take steps to control for extraneous variables that may impact the results.

Validity and reliability are two critical concepts in quantitative research that significantly determine the quality of research studies. While both terms are often used interchangeably, they refer to different aspects of research. Validity is the extent to which a research study measures what it claims to measure without being affected by extraneous factors or bias. In contrast, reliability is the degree to which the research results are consistent and stable over time and across different samples , methods, and evaluators.

Designing a research study that is both valid and reliable is essential for producing high-quality and trustworthy research findings. Finding this balance requires significant expertise, skill, and attention to detail. Ultimately, the goal is to produce research findings that are valid and reliable but also impactful and influential for the organization requesting them. Achieving this level of excellence requires a deep understanding of the nuances and complexities of research methodology and a commitment to excellence and rigor in all aspects of the research process.

Ensuring validity and reliability in quantitative research is not without its challenges. Some of the factors to consider include:

1. Measuring Complex Constructs or Variables One of the main challenges is the difficulty in accurately measuring complex constructs or variables. For instance, measuring constructs such as intelligence or personality can be complicated due to their multi-dimensional nature, and it can be challenging to capture all aspects accurately.

2. Limitations of Data Collection Instruments In addition, the measures or instruments used to collect data can also be limited in their sensitivity or specificity. This can impact the study’s validity and reliability, as accurate and precise measures can lead to incorrect conclusions and unreliable results. For example, a scale that measures depression but does not include all relevant symptoms may not accurately capture the construct being studied.

3. Sources of Error and Bias in Data Collection The data collection process itself can introduce sources of error or bias, which can impact the validity and reliability of the study. For instance, measurement errors can occur due to the limitations of the measuring instrument or human error during data collection. In addition, response bias can arise when participants provide socially desirable answers, while sampling bias can occur when the sample is not representative of the studied population.

4. The Complexity of Achieving Meaningful and Accurate Research Findings There are also some limitations to validity and reliability in research studies. For example, achieving internal validity by controlling for extraneous variables may only sometimes ensure external validity or the ability to generalize findings to other populations or settings. This can be a limitation for researchers who wish to apply their findings to a larger population or different contexts.

Additionally, while reliability is essential for producing consistent and reproducible results, it does not guarantee the accuracy or truth of the findings. This means that even if a study has reliable results, it may still need to be revised in terms of accuracy. These limitations remind us that research is a complex process, and achieving validity and reliability is just one part of the giant puzzle of producing accurate and meaningful research.

Researchers can adopt various measures and techniques to overcome the challenges and limitations in ensuring validity and reliability in research studies.

One such approach is to use multiple measures or instruments to assess the same construct. In addition, various steps can help identify commonalities and differences across measures, thereby providing a more comprehensive understanding of the construct being studied.

Inter-rater reliability checks can also be conducted to ensure different raters or observers consistently interpret and rate the same data. This can reduce measurement errors and improve the reliability of the results. Additionally, data-cleaning techniques can be used to identify and remove any outliers or errors in the data.

Finally, researchers can use appropriate statistical methods to assess the validity and reliability of their measures. For example, factor analysis identifies the underlying factors contributing to the construct being studied, while test-retest reliability helps evaluate the consistency of results over time. By adopting these measures and techniques, researchers can crease t their findings’ overall quality and usefulness.

The backbone of any quantitative research lies in the validity and reliability of the data collected. These factors ensure the data accurately reflects the intended research objectives and is consistent and reproducible. By carefully balancing the interrelationship between validity and reliability and using appropriate techniques to overcome challenges, researchers protect the credibility and impact of their work. This is essential in producing high-quality research that can withstand scrutiny and drive progress.

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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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Reliability and validity: Importance in Medical Research

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• 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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• Biomedical Research*
• Reproducibility of Results

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Designing and validating a research questionnaire - Part 1

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India

Carlo Caduff

1 Department of Global Health and Social Medicine, King’s College London, London, United Kingdom

Questionnaires are often used as part of research studies to collect data from participants. However, the information obtained through a questionnaire is dependent on how it has been designed, used, and validated. In this article, we look at the types of research questionnaires, their applications and limitations, and how a new questionnaire is developed.

INTRODUCTION

In research studies, questionnaires are commonly used as data collection tools, either as the only source of information or in combination with other techniques in mixed-method studies. However, the quality and accuracy of data collected using a questionnaire depend on how it is designed, used, and validated. In this two-part series, we discuss how to design (part 1) and how to use and validate (part 2) a research questionnaire. It is important to emphasize that questionnaires seek to gather information from other people and therefore entail a social relationship between those who are doing the research and those who are being researched. This social relationship comes with an obligation to learn from others , an obligation that goes beyond the purely instrumental rationality of gathering data. In that sense, we underscore that any research method is not simply a tool but a situation, a relationship, a negotiation, and an encounter. This points to both ethical questions (what is the relationship between the researcher and the researched?) and epistemological ones (what are the conditions under which we can know something?).

At the start of any kind of research project, it is crucial to select the right methodological approach. What is the research question, what is the research object, and what can a questionnaire realistically achieve? Not every research question and not every research object are suitable to the questionnaire as a method. Questionnaires can only provide certain kinds of empirical evidence and it is thus important to be aware of the limitations that are inherent in any kind of methodology.

WHAT IS A RESEARCH QUESTIONNAIRE?

A research questionnaire can be defined as a data collection tool consisting of a series of questions or items that are used to collect information from respondents and thus learn about their knowledge, opinions, attitudes, beliefs, and behavior and informed by a positivist philosophy of the natural sciences that consider methods mainly as a set of rules for the production of knowledge; questionnaires are frequently used instrumentally as a standardized and standardizing tool to ask a set of questions to participants. Outside of such a positivist philosophy, questionnaires can be seen as an encounter between the researcher and the researched, where knowledge is not simply gathered but negotiated through a distinct form of communication that is the questionnaire.

STRENGTHS AND LIMITATIONS OF QUESTIONNAIRES

A questionnaire may not always be the most appropriate way of engaging with research participants and generating knowledge that is needed for a research study. Questionnaires have advantages that have made them very popular, especially in quantitative studies driven by a positivist philosophy: they are a low-cost method for the rapid collection of large amounts of data, even from a wide sample. They are practical, can be standardized, and allow comparison between groups and locations. However, it is important to remember that a questionnaire only captures the information that the method itself (as the structured relationship between the researcher and the researched) allows for and that the respondents are willing to provide. For example, a questionnaire on diet captures what the respondents say they eat and not what they are eating. The problem of social desirability emerges precisely because the research process itself involves a social relationship. This means that respondents may often provide socially acceptable and idealized answers, particularly in relation to sensitive questions, for example, alcohol consumption, drug use, and sexual practices. Questionnaires are most useful for studies investigating knowledge, beliefs, values, self-understandings, and self-perceptions that reflect broader social, cultural, and political norms that may well diverge from actual practices.

TYPES OF RESEARCH QUESTIONNAIRES

Research questionnaires may be classified in several ways:

Depending on mode of administration

Research questionnaires may be self-administered (by the research participant) or researcher administered. Self-administered (also known as self-reported or self-completed) questionnaires are designed to be completed by respondents without assistance from a researcher. Self-reported questionnaires may be administered to participants directly during hospital or clinic visits, mailed through the post or E-mail, or accessed through websites. This technique allows respondents to answer at their own pace and simplifies research costs and logistics. The anonymity offered by self-reporting may facilitate more accurate answers. However, the disadvantages are that there may be misinterpretations of questions and low response rates. Significantly, relevant context information is missing to make sense of the answers provided. Researcher-reported (or interviewer-reported) questionnaires may be administered face-to-face or through remote techniques such as telephone or videoconference and are associated with higher response rates. They allow the researcher to have a better understanding of how the data are collected and how answers are negotiated, but are more resource intensive and require more training from the researchers.

The choice between self-administered and researcher-administered questionnaires depends on various factors such as the characteristics of the target audience (e.g., literacy and comprehension level and ability to use technology), costs involved, and the need for confidentiality/privacy.

Depending on the format of the questions

Research questionnaires can have structured or semi-structured formats. Semi-structured questionnaires allow respondents to answer more freely and on their terms, with no restrictions on their responses. They allow for unusual or surprising responses and are useful to explore and discover a range of answers to determine common themes. Typically, the analysis of responses to open-ended questions is more complex and requires coding and analysis. In contrast, structured questionnaires provide a predefined set of responses for the participant to choose from. The use of standard items makes the questionnaire easier to complete and allows quick aggregation, quantification, and analysis of the data. However, structured questionnaires can be restrictive if the scope of responses is limited and may miss potential answers. They also may suggest answers that respondents may not have considered before. Respondents may be forced to fit their answers into the predetermined format and may not be able to express personal views and say what they really want to say or think. In general, this type of questionnaire can turn the research process into a mechanical, anonymous survey with little incentive for participants to feel engaged, understood, and taken seriously.

STRUCTURED QUESTIONS: FORMATS

Some examples of close-ended questions include:

e.g., Please indicate your marital status:

• Prefer not to say.

e.g., Describe your areas of work (circle or tick all that apply):

• Clinical service
• Administration
• Strongly agree
• Strongly disagree.
• Numerical scales: Please rate your current pain on a scale of 1–10 where 1 is no pain and 10 is the worst imaginable pain
• Symbolic scales: For example, the Wong-Baker FACES scale to rate pain in older children
• Ranking: Rank the following cities as per the quality of public health care, where 1 is the best and 5 is the worst.

A matrix questionnaire consists of a series of rows with items to be answered with a series of columns providing the same answer options. This is an efficient way of getting the respondent to provide answers to multiple questions. The EORTC QLQ-C30 is an example of a matrix questionnaire.[ 1 ]

For a more detailed review of the types of research questions, readers are referred to a paper by Boynton and Greenhalgh.[ 2 ]

USING PRE-EXISTING QUESTIONNAIRES VERSUS DEVELOPING A NEW QUESTIONNAIRE

Before developing a questionnaire for a research study, a researcher can check whether there are any preexisting-validated questionnaires that might be adapted and used for the study. The use of validated questionnaires saves time and resources needed to design a new questionnaire and allows comparability between studies.

However, certain aspects need to be kept in mind: is the population/context/purpose for which the original questionnaire was designed similar to the new study? Is cross-cultural adaptation required? Are there any permission needed to use the questionnaire? In many situations, the development of a new questionnaire may be more appropriate given that any research project entails both methodological and epistemological questions: what is the object of knowledge and what are the conditions under which it can be known? It is important to understand that the standardizing nature of questionnaires contributes to the standardization of objects of knowledge. Thus, the seeming similarity in the object of study across diverse locations may be an artifact of the method. Whatever method one uses, it will always operate as the ground on which the object of study is known.

DESIGNING A NEW RESEARCH QUESTIONNAIRE

Once the researcher has decided to design a new questionnaire, several steps should be considered:

Gathering content

It creates a conceptual framework to identify all relevant areas for which the questionnaire will be used to collect information. This may require a scoping review of the published literature, appraising other questionnaires on similar topics, or the use of focus groups to identify common themes.

Create a list of questions

Questions need to be carefully formulated with attention to language and wording to avoid ambiguity and misinterpretation. Table 1 lists a few examples of poorlyworded questions that could have been phrased in a more appropriate manner. Other important aspects to be noted are:

Examples of poorly phrased questions in a research questionnaire

• Provide a brief introduction to the research study along with instructions on how to complete the questionnaire
• Allow respondents to indicate levels of intensity in their replies, so that they are not forced into “yes” or “no” answers where intensity of feeling may be more appropriate
• Collect specific and detailed data wherever possible – this can be coded into categories. For example, age can be captured in years and later classified as <18 years, 18–45 years, 46 years, and above. The reverse is not possible
• Avoid technical terms, slang, and abbreviations. Tailor the reading level to the expected education level of respondents
• The format of the questionnaire should be attractive with different sections for various subtopics. The font should be large and easy to read, especially if the questionnaire is targeted at the elderly
• Question sequence: questions should be arranged from general to specific, from easy to difficult, from facts to opinions, and sensitive topics should be introduced later in the questionnaire.[ 3 ] Usually, demographic details are captured initially followed by questions on other aspects
• Use contingency questions: these are questions which need to be answered only by a subgroup of the respondents who provide a particular answer to a previous question. This ensures that participants only respond to relevant sections of the questionnaire, for example, Do you smoke? If yes, then how long have you been smoking? If not, then please go to the next section.

TESTING A QUESTIONNAIRE

A questionnaire needs to be valid and reliable, and therefore, any new questionnaire needs to be pilot tested in a small sample of respondents who are representative of the larger population. In addition to validity and reliability, pilot testing provides information on the time taken to complete the questionnaire and whether any questions are confusing or misleading and need to be rephrased. Validity indicates that the questionnaire measures what it claims to measure – this means taking into consideration the limitations that come with any questionnaire-based study. Reliability means that the questionnaire yields consistent responses when administered repeatedly even by different researchers, and any variations in the results are due to actual differences between participants and not because of problems with the interpretation of the questions or their responses. In the next article in this series, we will discuss methods to determine the reliability and validity of a questionnaire.

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• Volume 18, Issue 3
• Validity and reliability in quantitative studies
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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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