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The 4 Types of Reliability in Research | Definitions & Examples

The 4 Types of Reliability in Research | Definitions & Examples

Published on August 8, 2019 by Fiona Middleton . Revised on June 22, 2023.

Reliability tells you how consistently a method measures something. When you apply the same method to the same sample under the same conditions, you should get the same results. If not, the method of measurement may be unreliable or bias may have crept into your research.

There are four main types of reliability. Each can be estimated by comparing different sets of results produced by the same method.

Test-retest reliability, interrater reliability, parallel forms reliability, internal consistency, which type of reliability applies to my research, other interesting articles, frequently asked questions about types of reliability.

Test-retest reliability measures the consistency of results when you repeat the same test on the same sample at a different point in time. You use it when you are measuring something that you expect to stay constant in your sample.

Why it’s important

Many factors can influence your results at different points in time: for example, respondents might experience different moods, or external conditions might affect their ability to respond accurately.

Test-retest reliability can be used to assess how well a method resists these factors over time. The smaller the difference between the two sets of results, the higher the test-retest reliability.

How to measure it

To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results.

Test-retest reliability example

You devise a questionnaire to measure the IQ of a group of participants (a property that is unlikely to change significantly over time).You administer the test two months apart to the same group of people, but the results are significantly different, so the test-retest reliability of the IQ questionnaire is low.

Improving test-retest reliability

When designing tests or questionnaires , try to formulate questions, statements, and tasks in a way that won’t be influenced by the mood or concentration of participants.
When planning your methods of data collection , try to minimize the influence of external factors, and make sure all samples are tested under the same conditions.
Remember that changes or recall bias can be expected to occur in the participants over time, and take these into account.

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Interrater reliability (also called interobserver reliability) measures the degree of agreement between different people observing or assessing the same thing. You use it when data is collected by researchers assigning ratings, scores or categories to one or more variables , and it can help mitigate observer bias .

People are subjective, so different observers’ perceptions of situations and phenomena naturally differ. Reliable research aims to minimize subjectivity as much as possible so that a different researcher could replicate the same results.

When designing the scale and criteria for data collection, it’s important to make sure that different people will rate the same variable consistently with minimal bias . This is especially important when there are multiple researchers involved in data collection or analysis.

To measure interrater reliability, different researchers conduct the same measurement or observation on the same sample. Then you calculate the correlation between their different sets of results. If all the researchers give similar ratings, the test has high interrater reliability.

Interrater reliability example

A team of researchers observe the progress of wound healing in patients. To record the stages of healing, rating scales are used, with a set of criteria to assess various aspects of wounds. The results of different researchers assessing the same set of patients are compared, and there is a strong correlation between all sets of results, so the test has high interrater reliability.

Improving interrater reliability

Clearly define your variables and the methods that will be used to measure them.
Develop detailed, objective criteria for how the variables will be rated, counted or categorized.
If multiple researchers are involved, ensure that they all have exactly the same information and training.

Parallel forms reliability measures the correlation between two equivalent versions of a test. You use it when you have two different assessment tools or sets of questions designed to measure the same thing.

If you want to use multiple different versions of a test (for example, to avoid respondents repeating the same answers from memory), you first need to make sure that all the sets of questions or measurements give reliable results.

The most common way to measure parallel forms reliability is to produce a large set of questions to evaluate the same thing, then divide these randomly into two question sets.

The same group of respondents answers both sets, and you calculate the correlation between the results. High correlation between the two indicates high parallel forms reliability.

Parallel forms reliability example

A set of questions is formulated to measure financial risk aversion in a group of respondents. The questions are randomly divided into two sets, and the respondents are randomly divided into two groups. Both groups take both tests: group A takes test A first, and group B takes test B first. The results of the two tests are compared, and the results are almost identical, indicating high parallel forms reliability.

Improving parallel forms reliability

Ensure that all questions or test items are based on the same theory and formulated to measure the same thing.

Internal consistency assesses the correlation between multiple items in a test that are intended to measure the same construct.

You can calculate internal consistency without repeating the test or involving other researchers, so it’s a good way of assessing reliability when you only have one data set.

When you devise a set of questions or ratings that will be combined into an overall score, you have to make sure that all of the items really do reflect the same thing. If responses to different items contradict one another, the test might be unreliable.

Two common methods are used to measure internal consistency.

Average inter-item correlation : For a set of measures designed to assess the same construct, you calculate the correlation between the results of all possible pairs of items and then calculate the average.
Split-half reliability : You randomly split a set of measures into two sets. After testing the entire set on the respondents, you calculate the correlation between the two sets of responses.

Internal consistency example

A group of respondents are presented with a set of statements designed to measure optimistic and pessimistic mindsets. They must rate their agreement with each statement on a scale from 1 to 5. If the test is internally consistent, an optimistic respondent should generally give high ratings to optimism indicators and low ratings to pessimism indicators. The correlation is calculated between all the responses to the “optimistic” statements, but the correlation is very weak. This suggests that the test has low internal consistency.

Improving internal consistency

Take care when devising questions or measures: those intended to reflect the same concept should be based on the same theory and carefully formulated.

It’s important to consider reliability when planning your research design , collecting and analyzing your data, and writing up your research. The type of reliability you should calculate depends on the type of research and your methodology .

If possible and relevant, you should statistically calculate reliability and state this alongside your results .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

You can use several tactics to minimize observer bias .

Use masking (blinding) to hide the purpose of your study from all observers.
Triangulate your data with different data collection methods or sources.
Use multiple observers and ensure interrater reliability.
Train your observers to make sure data is consistently recorded between them.
Standardize your observation procedures to make sure they are structured and clear.

Reproducibility and replicability are related terms.

A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
A successful replication shows that the reliability of the results is high.

Research bias affects the validity and reliability of your research findings , leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

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Reliability In Psychology Research: Definitions & 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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Reliability in psychology research refers to the reproducibility or consistency of measurements. Specifically, it is the degree to which a measurement instrument or procedure yields the same results on repeated trials. A measure is considered reliable if it produces consistent scores across different instances when the underlying thing being measured has not changed.

Reliability ensures that responses are consistent across times and occasions for instruments like questionnaires . Multiple forms of reliability exist, including test-retest, inter-rater, and internal consistency.

For example, if people weigh themselves during the day, they would expect to see a similar reading. Scales that measured weight differently each time would be of little use.

The same analogy could be applied to a tape measure that measures inches differently each time it is used. It would not be considered reliable.

If findings from research are replicated consistently, they are reliable. A correlation coefficient can be used to assess the degree of reliability. If a test is reliable, it should show a high positive correlation.

Of course, it is unlikely the same results will be obtained each time as participants and situations vary. Still, a strong positive correlation between the same test results indicates reliability.

Reliability is important because unreliable measures introduce random error that attenuates correlations and makes it harder to detect real relationships.

Ensuring high reliability for key measures in psychology research helps boost the sensitivity, validity, and replicability of studies. Estimating and reporting reliable evidence is considered an important methodological practice.

There are two types of reliability: internal and external.

Internal reliability refers to how consistently different items within a single test measure the same concept or construct. It ensures that a test is stable across its components.
External reliability measures how consistently a test produces similar results over repeated administrations or under different conditions. It ensures that a test is stable over time and situations.

Some key aspects of reliability in psychology research include:

Test-retest reliability : The consistency of scores for the same person across two or more separate administrations of the same measurement procedure over time. High test-retest reliability suggests the measure provides a stable, reproducible score.
Interrater reliability : The level of agreement in scores on a measure between different raters or observers rating the same target. High interrater reliability suggests the ratings are objective and not overly influenced by rater subjectivity or bias.
Internal consistency reliability : The degree to which different test items or parts of an instrument that measure the same construct yield similar results. Analyzed statistically using Cronbach’s alpha, a high value suggests the items measure the same underlying concept.

Test-Retest Reliability

The test-retest method assesses the external consistency of a test. Examples of appropriate tests include questionnaires and psychometric tests. It measures the stability of a test over time.

A typical assessment would involve giving participants the same test on two separate occasions. If the same or similar results are obtained, then external reliability is established.

Here’s how it works:

A test or measurement is administered to participants at one point in time.
After a certain period, the same test is administered again to the same participants without any intervention or treatment in between.
The scores from the two administrations are then correlated using a statistical method, often Pearson’s correlation.
A high correlation between the scores from the two test administrations indicates good test-retest reliability, suggesting the test yields consistent results over time.

This method is especially useful for tests that measure stable traits or characteristics that aren’t expected to change over short periods.

The disadvantage of the test-retest method is that it takes a long time for results to be obtained. The reliability can be influenced by the time interval between tests and any events that might affect participants’ responses during this interval.

Beck et al. (1996) studied the responses of 26 outpatients on two separate therapy sessions one week apart, they found a correlation of .93 therefore demonstrating high test-restest reliability of the depression inventory.

This is an example of why reliability in psychological research is necessary, if it wasn’t for the reliability of such tests some individuals may not be successfully diagnosed with disorders such as depression and consequently will not be given appropriate therapy.

The timing of the test is important; if the duration is too brief, then participants may recall information from the first test, which could bias the results.

Alternatively, if the duration is too long, it is feasible that the participants could have changed in some important way which could also bias the results.

The test-retest method assesses the external consistency of a test. This refers to the degree to which different raters give consistent estimates of the same behavior. Inter-rater reliability can be used for interviews.

Inter-Rater Reliability

Inter-rater reliability, often termed inter-observer reliability, refers to the extent to which different raters or evaluators agree in assessing a particular phenomenon, behavior, or characteristic. It’s a measure of consistency and agreement between individuals scoring or evaluating the same items or behaviors.

High inter-rater reliability indicates that the findings or measurements are consistent across different raters, suggesting the results are not due to random chance or subjective biases of individual raters.

Statistical measures, such as Cohen’s Kappa or the Intraclass Correlation Coefficient (ICC), are often employed to quantify the level of agreement between raters, helping to ensure that findings are objective and reproducible.

Ensuring high inter-rater reliability is essential, especially in studies involving subjective judgment or observations, as it provides confidence that the findings are replicable and not heavily influenced by individual rater biases.

Note it can also be called inter-observer reliability when referring to observational research. Here, researchers observe the same behavior independently (to avoid bias) and compare their data. If the data is similar, then it is reliable.

Where observer scores do not significantly correlate, then reliability can be improved by:

Train observers in the observation techniques and ensure everyone agrees with them.
Ensuring behavior categories have been operationalized. This means that they have been objectively defined.

For example, if two researchers are observing ‘aggressive behavior’ of children at nursery they would both have their own subjective opinion regarding what aggression comprises.

In this scenario, they would be unlikely to record aggressive behavior the same, and the data would be unreliable.

However, if they were to operationalize the behavior category of aggression, this would be more objective and make it easier to identify when a specific behavior occurs.

For example, while “aggressive behavior” is subjective and not operationalized, “pushing” is objective and operationalized. Thus, researchers could count how many times children push each other over a certain duration of time.

Internal Consistency Reliability

Internal consistency reliability refers to how well different items on a test or survey that are intended to measure the same construct produce similar scores.

For example, a questionnaire measuring depression may have multiple questions tapping issues like sadness, changes in sleep and appetite, fatigue, and loss of interest. The assumption is that people’s responses across these different symptom items should be fairly consistent.

Cronbach’s alpha is a common statistic used to quantify internal consistency reliability. It calculates the average inter-item correlations among the test items. Values range from 0 to 1, with higher values indicating greater internal consistency. A good rule of thumb is that alpha should generally be above .70 to suggest adequate reliability.

An alpha of .90 for a depression questionnaire, for example, means there is a high average correlation between respondents’ scores on the different symptom items.

This suggests all the items are measuring the same underlying construct (depression) in a consistent manner. It taps the unidimensionality of the scale – evidence it is measuring one thing.

If some items were unrelated to others, the average inter-item correlations would be lower, resulting in a lower alpha. This would indicate the presence of multiple dimensions in the scale, rather than a unified single concept.

So, in summary, high internal consistency reliability evidenced through high Cronbach’s alpha provides support for the fact that various test items successfully tap into the same latent variable the researcher intends to measure. It suggests the items meaningfully cohere together to reliably measure that construct.

Split-Half Method

The split-half method assesses the internal consistency of a test, such as psychometric tests and questionnaires.

There, it measures the extent to which all parts of the test contribute equally to what is being measured.

The split-half approach provides another method of quantifying internal consistency by taking advantage of the natural variation when a single test is divided in half.

It’s somewhat cumbersome to implement but avoids limitations associated with Cronbach’s alpha. However, alpha remains much more widely used in practice due to its relative ease of calculation.

A test or questionnaire is split into two halves, typically by separating even-numbered items from odd-numbered items, or first-half items vs. second-half.
Each half is scored separately, and the scores are correlated using a statistical method, often Pearson’s correlation.
The correlation between the two halves gives an indication of the test’s reliability. A higher correlation suggests better reliability.
To adjust for the test’s shortened length (because we’ve split it in half), the Spearman-Brown prophecy formula is often applied to estimate the reliability of the full test based on the split-half reliability.

The reliability of a test could be improved by using this method. For example, any items on separate halves of a test with a low correlation (e.g., r = .25) should either be removed or rewritten.

The split-half method is a quick and easy way to establish reliability. However, it can only be effective with large questionnaires in which all questions measure the same construct. This means it would not be appropriate for tests that measure different constructs.

For example, the Minnesota Multiphasic Personality Inventory has sub scales measuring differently behaviors such as depression, schizophrenia, social introversion. Therefore the split-half method was not be an appropriate method to assess reliability for this personality test.

Validity vs. Reliability In Psychology

In psychology, validity and reliability are fundamental concepts that assess the quality of measurements.

Validity refers to the degree to which a measure accurately assesses the specific concept, trait, or construct that it claims to be assessing. It refers to the truthfulness of the measure.
Reliability refers to the overall consistency, stability, and repeatability of a measurement. It is concerned with how much random error might be distorting scores or introducing unwanted “noise” into the data.

A key difference is that validity refers to what’s being measured, while reliability refers to how consistently it’s being measured.

An unreliable measure cannot be truly valid because if a measure gives inconsistent, unpredictable scores, it clearly isn’t measuring the trait or quality it aims to measure in a truthful, systematic manner. Establishing reliability provides the foundation for determining the measure’s validity.

A pivotal understanding is that reliability is a necessary but not sufficient condition for validity.

It means a test can be reliable, consistently producing the same results, without being valid, or accurately measuring the intended attribute.

However, a valid test, one that truly measures what it purports to, must be reliable. In the pursuit of rigorous psychological research, both validity and reliability are indispensable.

Ideally, researchers strive for high scores on both -Validity to make sure you’re measuring the correct construct and reliability to make sure you’re measuring it accurately and precisely. The two qualities are independent but both crucial elements of strong measurement procedures.

Validity vs reliability as data research quality evaluation outline diagram. Labeled educational comparison with reliable or valid information vector illustration. Method, technique or test indication

Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the beck depression inventory The Psychological Corporation. San Antonio , TX.

Clifton, J. D. W. (2020). Managing validity versus reliability trade-offs in scale-building decisions. Psychological Methods, 25 (3), 259–270. https:// doi.org/10.1037/met0000236

Guttman, L. (1945). A basis for analyzing test-retest reliability. Psychometrika, 10 (4), 255–282. https://doi.org/10.1007/BF02288892

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

Jannarone, R. J., Macera, C. A., & Garrison, C. Z. (1987). Evaluating interrater agreement through “case-control” sampling. Biometrics, 43 (2), 433–437. https://doi.org/10.2307/2531825

LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11 (4), 815–852. https://doi.org/10.1177/1094428106296642

Watkins, M. W., & Pacheco, M. (2000). Interobserver agreement in behavioral research: Importance and calculation. Journal of Behavioral Education, 10 , 205–212

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Reliability and Validity – Definitions, Types & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On October 26, 2023

A researcher must test the collected data before making any conclusion. Every research design needs to be concerned with reliability and validity to measure the quality of the research.

What is Reliability?

Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.

Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.

Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.

What is the Validity?

Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid.

If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid.

Example: Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.

Example: Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.

Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.

Example: If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.

Internal Vs. External Validity

One of the key features of randomised designs is that they have significantly high internal and external validity.

Internal validity is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the variables .

Example: age, level, height, and grade.

External validity is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.

Also, read about Inductive vs Deductive reasoning in this article.

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Threats to Interval Validity

Threats of external validity, how to assess reliability and validity.

Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through various statistical methods depending on the types of validity, as explained below:

Types of Reliability

Types of validity.

As we discussed above, the reliability of the measurement alone cannot determine its validity. Validity is difficult to be measured even if the method is reliable. The following type of tests is conducted for measuring validity.

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How to Increase Reliability?

Use an appropriate questionnaire to measure the competency level.
Ensure a consistent environment for participants
Make the participants familiar with the criteria of assessment.
Train the participants appropriately.
Analyse the research items regularly to avoid poor performance.

How to Increase Validity?

Ensuring Validity is also not an easy job. A proper functioning method to ensure validity is given below:

The reactivity should be minimised at the first concern.
The Hawthorne effect should be reduced.
The respondents should be motivated.
The intervals between the pre-test and post-test should not be lengthy.
Dropout rates should be avoided.
The inter-rater reliability should be ensured.
Control and experimental groups should be matched with each other.

How to Implement Reliability and Validity in your Thesis?

According to the experts, it is helpful if to implement the concept of reliability and Validity. Especially, in the thesis and the dissertation, these concepts are adopted much. The method for implementation given below:

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.

Reliability – Types, Examples and Guide

Table of Contents

Reliability

Definition:

Reliability refers to the consistency, dependability, and trustworthiness of a system, process, or measurement to perform its intended function or produce consistent results over time. It is a desirable characteristic in various domains, including engineering, manufacturing, software development, and data analysis.

Reliability In Engineering

In engineering and manufacturing, reliability refers to the ability of a product, equipment, or system to function without failure or breakdown under normal operating conditions for a specified period. A reliable system consistently performs its intended functions, meets performance requirements, and withstands various environmental factors, stress, or wear and tear.

Reliability In Software Development

In software development, reliability relates to the stability and consistency of software applications or systems. A reliable software program operates consistently without crashing, produces accurate results, and handles errors or exceptions gracefully. Reliability is often measured by metrics such as mean time between failures (MTBF) and mean time to repair (MTTR).

Reliability In Data Analysis and Statistics

In data analysis and statistics, reliability refers to the consistency and repeatability of measurements or assessments. For example, if a measurement instrument consistently produces similar results when measuring the same quantity or if multiple raters consistently agree on the same assessment, it is considered reliable. Reliability is often assessed using statistical measures such as test-retest reliability, inter-rater reliability, or internal consistency.

Research Reliability

Research reliability refers to the consistency, stability, and repeatability of research findings . It indicates the extent to which a research study produces consistent and dependable results when conducted under similar conditions. In other words, research reliability assesses whether the same results would be obtained if the study were replicated with the same methodology, sample, and context.

What Affects Reliability in Research

Several factors can affect the reliability of research measurements and assessments. Here are some common factors that can impact reliability:

Measurement Error

Measurement error refers to the variability or inconsistency in the measurements that is not due to the construct being measured. It can arise from various sources, such as the limitations of the measurement instrument, environmental factors, or the characteristics of the participants. Measurement error reduces the reliability of the measure by introducing random variability into the data.

Rater/Observer Bias

In studies involving subjective assessments or ratings, the biases or subjective judgments of the raters or observers can affect reliability. If different raters interpret and evaluate the same phenomenon differently, it can lead to inconsistencies in the ratings, resulting in lower inter-rater reliability.

Participant Factors

Characteristics or factors related to the participants themselves can influence reliability. For example, factors such as fatigue, motivation, attention, or mood can introduce variability in responses, affecting the reliability of self-report measures or performance assessments.

Instrumentation

The quality and characteristics of the measurement instrument can impact reliability. If the instrument lacks clarity, has ambiguous items or instructions, or is prone to measurement errors, it can decrease the reliability of the measure. Poorly designed or unreliable instruments can introduce measurement error and decrease the consistency of the measurements.

Sample Size

Sample size can affect reliability, especially in studies where the reliability coefficient is based on correlations or variability within the sample. A larger sample size generally provides more stable estimates of reliability, while smaller samples can yield less precise estimates.

Time Interval

The time interval between test administrations can impact test-retest reliability. If the time interval is too short, participants may recall their previous responses and answer in a similar manner, artificially inflating the reliability coefficient. On the other hand, if the time interval is too long, true changes in the construct being measured may occur, leading to lower test-retest reliability.

Content Sampling

The specific items or questions included in a measure can affect reliability. If the measure does not adequately sample the full range of the construct being measured or if the items are too similar or redundant, it can result in lower internal consistency reliability.

Scoring and Data Handling

Errors in scoring, data entry, or data handling can introduce variability and impact reliability. Inaccurate or inconsistent scoring procedures, data entry mistakes, or mishandling of missing data can affect the reliability of the measurements.

Context and Environment

The context and environment in which measurements are obtained can influence reliability. Factors such as noise, distractions, lighting conditions, or the presence of others can introduce variability and affect the consistency of the measurements.

Types of Reliability

There are several types of reliability that are commonly discussed in research and measurement contexts. Here are some of the main types of reliability:

Test-Retest Reliability

This type of reliability assesses the consistency of a measure over time. It involves administering the same test or measure to the same group of individuals on two separate occasions and then comparing the results. If the scores are similar or highly correlated across the two testing points, it indicates good test-retest reliability.

Inter-Rater Reliability

Inter-rater reliability examines the degree of agreement or consistency between different raters or observers who are assessing the same phenomenon. It is commonly used in subjective evaluations or assessments where judgments are made by multiple individuals. High inter-rater reliability suggests that different observers are likely to reach the same conclusions or make consistent assessments.

Internal Consistency Reliability

Internal consistency reliability assesses the extent to which the items or questions within a measure are consistent with each other. It is commonly measured using techniques such as Cronbach’s alpha. High internal consistency reliability indicates that the items within a measure are measuring the same construct or concept consistently.

Parallel Forms Reliability

Parallel forms reliability assesses the consistency of different versions or forms of a test that are intended to measure the same construct. Two equivalent versions of a test are administered to the same group of individuals, and the scores are compared to determine the level of agreement between the forms.

Split-Half Reliability

Split-half reliability involves splitting a measure into two halves and examining the consistency between the two halves. It can be done by dividing the items into odd-even pairs or by randomly splitting the items. The scores from the two halves are then compared to assess the degree of consistency.

Alternate Forms Reliability

Alternate forms reliability is similar to parallel forms reliability, but it involves administering two different versions of a test to the same group of individuals. The two forms should be equivalent and measure the same construct. The scores from the two forms are then compared to assess the level of agreement.

Applications of Reliability

Reliability has several important applications across various fields and disciplines. Here are some common applications of reliability:

Psychological and Educational Testing

Reliability is crucial in psychological and educational testing to ensure that the scores obtained from assessments are consistent and dependable. It helps to determine the accuracy and stability of measures such as intelligence tests, personality assessments, academic exams, and aptitude tests.

Market Research

In market research, reliability is important for ensuring consistent and dependable data collection. Surveys, questionnaires, and other data collection instruments need to have high reliability to obtain accurate and consistent responses from participants. Reliability analysis helps researchers identify and address any issues that may affect the consistency of the data.

Health and Medical Research

Reliability is essential in health and medical research to ensure that measurements and assessments used in studies are consistent and trustworthy. This includes the reliability of diagnostic tests, patient-reported outcome measures, observational measures, and psychometric scales. High reliability is crucial for making valid inferences and drawing reliable conclusions from research findings.

Quality Control and Manufacturing

Reliability analysis is widely used in industries such as manufacturing and quality control to assess the reliability of products and processes. It helps to identify and address sources of variation and inconsistency, ensuring that products meet the required standards and specifications consistently.

Social Science Research

Reliability plays a vital role in social science research, including fields such as sociology, anthropology, and political science. It is used to assess the consistency of measurement tools, such as surveys or observational protocols, to ensure that the data collected is reliable and can be trusted for analysis and interpretation.

Performance Evaluation

Reliability is important in performance evaluation systems used in organizations and workplaces. Whether it’s assessing employee performance, evaluating the reliability of scoring rubrics, or measuring the consistency of ratings by supervisors, reliability analysis helps ensure fairness and consistency in the evaluation process.

Psychometrics and Scale Development

Reliability analysis is a fundamental step in psychometrics, which involves developing and validating measurement scales. Researchers assess the reliability of items and subscales to ensure that the scale measures the intended construct consistently and accurately.

Examples of Reliability

Here are some examples of reliability in different contexts:

Test-Retest Reliability Example: A researcher administers a personality questionnaire to a group of participants and then administers the same questionnaire to the same participants after a certain period, such as two weeks. The scores obtained from the two administrations are highly correlated, indicating good test-retest reliability.

Inter-Rater Reliability Example: Multiple teachers assess the essays of a group of students using a standardized grading rubric. The ratings assigned by the teachers show a high level of agreement or correlation, indicating good inter-rater reliability.

Internal Consistency Reliability Example: A researcher develops a questionnaire to measure job satisfaction. The researcher administers the questionnaire to a group of employees and calculates Cronbach’s alpha to assess internal consistency. The calculated value of Cronbach’s alpha is high (e.g., above 0.8), indicating good internal consistency reliability.

Parallel Forms Reliability Example: Two versions of a mathematics exam are created, which are designed to measure the same mathematical skills. Both versions of the exam are administered to the same group of students, and the scores from the two versions are highly correlated, indicating good parallel forms reliability.

Split-Half Reliability Example: A researcher develops a survey to measure self-esteem. The survey consists of 20 items, and the researcher randomly divides the items into two halves. The scores obtained from each half of the survey show a high level of agreement or correlation, indicating good split-half reliability.

Alternate Forms Reliability Example: A researcher develops two versions of a language proficiency test, which are designed to measure the same language skills. Both versions of the test are administered to the same group of participants, and the scores from the two versions are highly correlated, indicating good alternate forms reliability.

Where to Write About Reliability in A Thesis

When writing about reliability in a thesis, there are several sections where you can address this topic. Here are some common sections in a thesis where you can discuss reliability:

Introduction :

In the introduction section of your thesis, you can provide an overview of the study and briefly introduce the concept of reliability. Explain why reliability is important in your research field and how it relates to your study objectives.

Theoretical Framework:

If your thesis includes a theoretical framework or a literature review, this is a suitable section to discuss reliability. Provide an overview of the relevant theories, models, or concepts related to reliability in your field. Discuss how other researchers have measured and assessed reliability in similar studies.

Methodology:

The methodology section is crucial for addressing reliability. Describe the research design, data collection methods, and measurement instruments used in your study. Explain how you ensured the reliability of your measurements or data collection procedures. This may involve discussing pilot studies, inter-rater reliability, test-retest reliability, or other techniques used to assess and improve reliability.

Data Analysis:

In the data analysis section, you can discuss the statistical techniques employed to assess the reliability of your data. This might include measures such as Cronbach’s alpha, Cohen’s kappa, or intraclass correlation coefficients (ICC), depending on the nature of your data and research design. Present the results of reliability analyses and interpret their implications for your study.

Discussion:

In the discussion section, analyze and interpret the reliability results in relation to your research findings and objectives. Discuss any limitations or challenges encountered in establishing or maintaining reliability in your study. Consider the implications of reliability for the validity and generalizability of your results.

Conclusion:

In the conclusion section, summarize the main points discussed in your thesis regarding reliability. Emphasize the importance of reliability in research and highlight any recommendations or suggestions for future studies to enhance reliability.

Importance of Reliability

Reliability is of utmost importance in research, measurement, and various practical applications. Here are some key reasons why reliability is important:

Consistency : Reliability ensures consistency in measurements and assessments. Consistent results indicate that the measure or instrument is stable and produces similar outcomes when applied repeatedly. This consistency allows researchers and practitioners to have confidence in the reliability of the data collected and the conclusions drawn from it.
Accuracy : Reliability is closely linked to accuracy. A reliable measure produces results that are close to the true value or state of the phenomenon being measured. When a measure is unreliable, it introduces error and uncertainty into the data, which can lead to incorrect interpretations and flawed decision-making.
Trustworthiness : Reliability enhances the trustworthiness of measurements and assessments. When a measure is reliable, it indicates that it is dependable and can be trusted to provide consistent and accurate results. This is particularly important in fields where decisions and actions are based on the data collected, such as education, healthcare, and market research.
Comparability : Reliability enables meaningful comparisons between different groups, individuals, or time points. When measures are reliable, differences or changes observed can be attributed to true differences in the underlying construct, rather than measurement error. This allows for valid comparisons and evaluations, both within a study and across different studies.
Validity : Reliability is a prerequisite for validity. Validity refers to the extent to which a measure or assessment accurately captures the construct it is intended to measure. If a measure is unreliable, it cannot be valid, as it does not consistently reflect the construct of interest. Establishing reliability is an important step in establishing the validity of a measure.
Decision-making : Reliability is crucial for making informed decisions based on data. Whether it’s evaluating employee performance, diagnosing medical conditions, or conducting research studies, reliable measurements and assessments provide a solid foundation for decision-making processes. They help to reduce uncertainty and increase confidence in the conclusions drawn from the data.
Quality Assurance : Reliability is essential for maintaining quality assurance in various fields. It allows organizations to assess and monitor the consistency and dependability of their processes, products, and services. By ensuring reliability, organizations can identify areas of improvement, address sources of variation, and deliver consistent and high-quality outcomes.

Limitations of Reliability

Here are some limitations of reliability:

Limited to consistency: Reliability primarily focuses on the consistency of measurements and findings. However, it does not guarantee the accuracy or validity of the measurements. A measurement can be consistent but still systematically biased or flawed, leading to inaccurate results. Reliability alone cannot address validity concerns.
Context-dependent: Reliability can be influenced by the specific context, conditions, or population under study. A measurement or instrument that demonstrates high reliability in one context may not necessarily exhibit the same level of reliability in a different context. Researchers need to consider the specific characteristics and limitations of their study context when interpreting reliability.
Inadequate for complex constructs: Reliability is often based on the assumption of unidimensionality, which means that a measurement instrument is designed to capture a single construct. However, many real-world phenomena are complex and multidimensional, making it challenging to assess reliability accurately. Reliability measures may not adequately capture the full complexity of such constructs.
Susceptible to systematic errors: Reliability focuses on minimizing random errors, but it may not detect or address systematic errors or biases in measurements. Systematic errors can arise from flaws in the measurement instrument, data collection procedures, or sample selection. Reliability assessments may not fully capture or address these systematic errors, leading to biased or inaccurate results.
Relies on assumptions: Reliability assessments often rely on certain assumptions, such as the assumption of measurement invariance or the assumption of stable conditions over time. These assumptions may not always hold true in real-world research settings, particularly when studying dynamic or evolving phenomena. Failure to meet these assumptions can compromise the reliability of the research.
Limited to quantitative measures: Reliability is typically applied to quantitative measures and instruments, which can be problematic when studying qualitative or subjective phenomena. Reliability measures may not fully capture the richness and complexity of qualitative data, limiting their applicability in certain research domains.

Also see Reliability Vs Validity

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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 .

Reliability reflects whether an instrument produces consistent results when applied to the same phenomenon, under the same conditions.

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.

Psst… there’s more!

This post is an extract from our bestselling Udemy Course, Methodology Bootcamp . If you want to work smart, you don't want to miss this .

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The 4 Types of Reliability in Research | Definitions & Examples

The 4 Types of Reliability in Research | Definitions & Examples

Published on 3 May 2022 by Fiona Middleton . Revised on 26 August 2022.

There are four main types of reliability. Each can be estimated by comparing different sets of results produced by the same method.

Test-retest reliability, interrater reliability, parallel forms reliability, internal consistency, which type of reliability applies to my research.

Why test-retest reliability is important

Test-retest reliability can be used to assess how well a method resists these factors over time. The smaller the difference between the two sets of results, the higher the test-retest reliability.

How to measure test-retest reliability

To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results.

Improving test-retest reliability

When designing tests or questionnaires , try to formulate questions, statements, and tasks in a way that won’t be influenced by the mood or concentration of participants.
When planning your methods of data collection , try to minimise the influence of external factors, and make sure all samples are tested under the same conditions.
Remember that changes can be expected to occur in the participants over time, and take these into account.

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Inter-rater reliability (also called inter-observer reliability) measures the degree of agreement between different people observing or assessing the same thing. You use it when data is collected by researchers assigning ratings, scores or categories to one or more variables .

Why inter-rater reliability is important

People are subjective, so different observers’ perceptions of situations and phenomena naturally differ. Reliable research aims to minimise subjectivity as much as possible so that a different researcher could replicate the same results.

When designing the scale and criteria for data collection, it’s important to make sure that different people will rate the same variable consistently with minimal bias. This is especially important when there are multiple researchers involved in data collection or analysis.

How to measure inter-rater reliability

To measure inter-rater reliability, different researchers conduct the same measurement or observation on the same sample. Then you calculate the correlation between their different sets of results. If all the researchers give similar ratings, the test has high inter-rater reliability.

Improving inter-rater reliability

Clearly define your variables and the methods that will be used to measure them.
Develop detailed, objective criteria for how the variables will be rated, counted, or categorised.
If multiple researchers are involved, ensure that they all have exactly the same information and training.

Why parallel forms reliability is important

How to measure parallel forms reliability

The most common way to measure parallel forms reliability is to produce a large set of questions to evaluate the same thing, then divide these randomly into two question sets.

The same group of respondents answers both sets, and you calculate the correlation between the results. High correlation between the two indicates high parallel forms reliability.

Improving parallel forms reliability

Ensure that all questions or test items are based on the same theory and formulated to measure the same thing.

Internal consistency assesses the correlation between multiple items in a test that are intended to measure the same construct.

You can calculate internal consistency without repeating the test or involving other researchers, so it’s a good way of assessing reliability when you only have one dataset.

Why internal consistency is important

How to measure internal consistency

Two common methods are used to measure internal consistency.

Average inter-item correlation : For a set of measures designed to assess the same construct, you calculate the correlation between the results of all possible pairs of items and then calculate the average.
Split-half reliability : You randomly split a set of measures into two sets. After testing the entire set on the respondents, you calculate the correlation between the two sets of responses.

Improving internal consistency

Take care when devising questions or measures: those intended to reflect the same concept should be based on the same theory and carefully formulated.

It’s important to consider reliability when planning your research design , collecting and analysing your data, and writing up your research. The type of reliability you should calculate depends on the type of research and your methodology .

If possible and relevant, you should statistically calculate reliability and state this alongside your results .

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Understanding Reliability and Validity

These related research issues ask us to consider whether we are studying what we think we are studying and whether the measures we use are consistent.

Reliability

Reliability is the extent to which an experiment, test, or any measuring procedure yields the same result on repeated trials. Without the agreement of independent observers able to replicate research procedures, or the ability to use research tools and procedures that yield consistent measurements, researchers would be unable to satisfactorily draw conclusions, formulate theories, or make claims about the generalizability of their research. In addition to its important role in research, reliability is critical for many parts of our lives, including manufacturing, medicine, and sports.

Reliability is such an important concept that it has been defined in terms of its application to a wide range of activities. For researchers, four key types of reliability are:

Equivalency Reliability

Equivalency reliability is the extent to which two items measure identical concepts at an identical level of difficulty. Equivalency reliability is determined by relating two sets of test scores to one another to highlight the degree of relationship or association. In quantitative studies and particularly in experimental studies, a correlation coefficient, statistically referred to as r , is used to show the strength of the correlation between a dependent variable (the subject under study), and one or more independent variables , which are manipulated to determine effects on the dependent variable. An important consideration is that equivalency reliability is concerned with correlational, not causal, relationships.

For example, a researcher studying university English students happened to notice that when some students were studying for finals, their holiday shopping began. Intrigued by this, the researcher attempted to observe how often, or to what degree, this these two behaviors co-occurred throughout the academic year. The researcher used the results of the observations to assess the correlation between studying throughout the academic year and shopping for gifts. The researcher concluded there was poor equivalency reliability between the two actions. In other words, studying was not a reliable predictor of shopping for gifts.

Stability Reliability

Stability reliability (sometimes called test, re-test reliability) is the agreement of measuring instruments over time. To determine stability, a measure or test is repeated on the same subjects at a future date. Results are compared and correlated with the initial test to give a measure of stability.

An example of stability reliability would be the method of maintaining weights used by the U.S. Bureau of Standards. Platinum objects of fixed weight (one kilogram, one pound, etc...) are kept locked away. Once a year they are taken out and weighed, allowing scales to be reset so they are "weighing" accurately. Keeping track of how much the scales are off from year to year establishes a stability reliability for these instruments. In this instance, the platinum weights themselves are assumed to have a perfectly fixed stability reliability.

Internal Consistency

Internal consistency is the extent to which tests or procedures assess the same characteristic, skill or quality. It is a measure of the precision between the observers or of the measuring instruments used in a study. This type of reliability often helps researchers interpret data and predict the value of scores and the limits of the relationship among variables.

For example, a researcher designs a questionnaire to find out about college students' dissatisfaction with a particular textbook. Analyzing the internal consistency of the survey items dealing with dissatisfaction will reveal the extent to which items on the questionnaire focus on the notion of dissatisfaction.

Interrater Reliability

Interrater reliability is the extent to which two or more individuals (coders or raters) agree. Interrater reliability addresses the consistency of the implementation of a rating system.

A test of interrater reliability would be the following scenario: Two or more researchers are observing a high school classroom. The class is discussing a movie that they have just viewed as a group. The researchers have a sliding rating scale (1 being most positive, 5 being most negative) with which they are rating the student's oral responses. Interrater reliability assesses the consistency of how the rating system is implemented. For example, if one researcher gives a "1" to a student response, while another researcher gives a "5," obviously the interrater reliability would be inconsistent. Interrater reliability is dependent upon the ability of two or more individuals to be consistent. Training, education and monitoring skills can enhance interrater reliability.

Related Information: Reliability Example

An example of the importance of reliability is the use of measuring devices in Olympic track and field events. For the vast majority of people, ordinary measuring rulers and their degree of accuracy are reliable enough. However, for an Olympic event, such as the discus throw, the slightest variation in a measuring device -- whether it is a tape, clock, or other device -- could mean the difference between the gold and silver medals. Additionally, it could mean the difference between a new world record and outright failure to qualify for an event. Olympic measuring devices, then, must be reliable from one throw or race to another and from one competition to another. They must also be reliable when used in different parts of the world, as temperature, air pressure, humidity, interpretation, or other variables might affect their readings.

Validity refers to the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. While reliability is concerned with the accuracy of the actual measuring instrument or procedure, validity is concerned with the study's success at measuring what the researchers set out to measure.

Researchers should be concerned with both external and internal validity. External validity refers to the extent to which the results of a study are generalizable or transferable. (Most discussions of external validity focus solely on generalizability; see Campbell and Stanley, 1966. We include a reference here to transferability because many qualitative research studies are not designed to be generalized.)

Internal validity refers to (1) the rigor with which the study was conducted (e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and wasn't measured) and (2) the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore (Huitt, 1998). In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.

Scholars discuss several types of internal validity. For brief discussions of several types of internal validity, click on the items below:

Face Validity

Face validity is concerned with how a measure or procedure appears. Does it seem like a reasonable way to gain the information the researchers are attempting to obtain? Does it seem well designed? Does it seem as though it will work reliably? Unlike content validity, face validity does not depend on established theories for support (Fink, 1995).

Criterion Related Validity

Criterion related validity, also referred to as instrumental validity, is used to demonstrate the accuracy of a measure or procedure by comparing it with another measure or procedure which has been demonstrated to be valid.

For example, imagine a hands-on driving test has been shown to be an accurate test of driving skills. By comparing the scores on the written driving test with the scores from the hands-on driving test, the written test can be validated by using a criterion related strategy in which the hands-on driving test is compared to the written test.

Construct Validity

Construct validity seeks agreement between a theoretical concept and a specific measuring device or procedure. For example, a researcher inventing a new IQ test might spend a great deal of time attempting to "define" intelligence in order to reach an acceptable level of construct validity.

Construct validity can be broken down into two sub-categories: Convergent validity and discriminate validity. Convergent validity is the actual general agreement among ratings, gathered independently of one another, where measures should be theoretically related. Discriminate validity is the lack of a relationship among measures which theoretically should not be related.

To understand whether a piece of research has construct validity, three steps should be followed. First, the theoretical relationships must be specified. Second, the empirical relationships between the measures of the concepts must be examined. Third, the empirical evidence must be interpreted in terms of how it clarifies the construct validity of the particular measure being tested (Carmines & Zeller, p. 23).

Content Validity

Content Validity is based on the extent to which a measurement reflects the specific intended domain of content (Carmines & Zeller, 1991, p.20).

Content validity is illustrated using the following examples: Researchers aim to study mathematical learning and create a survey to test for mathematical skill. If these researchers only tested for multiplication and then drew conclusions from that survey, their study would not show content validity because it excludes other mathematical functions. Although the establishment of content validity for placement-type exams seems relatively straight-forward, the process becomes more complex as it moves into the more abstract domain of socio-cultural studies. For example, a researcher needing to measure an attitude like self-esteem must decide what constitutes a relevant domain of content for that attitude. For socio-cultural studies, content validity forces the researchers to define the very domains they are attempting to study.

Related Information: Validity Example

Many recreational activities of high school students involve driving cars. A researcher, wanting to measure whether recreational activities have a negative effect on grade point average in high school students, might conduct a survey asking how many students drive to school and then attempt to find a correlation between these two factors. Because many students might use their cars for purposes other than or in addition to recreation (e.g., driving to work after school, driving to school rather than walking or taking a bus), this research study might prove invalid. Even if a strong correlation was found between driving and grade point average, driving to school in and of itself would seem to be an invalid measure of recreational activity.

The challenges of achieving reliability and validity are among the most difficult faced by researchers. In this section, we offer commentaries on these challenges.

Difficulties of Achieving Reliability

It is important to understand some of the problems concerning reliability which might arise. It would be ideal to reliably measure, every time, exactly those things which we intend to measure. However, researchers can go to great lengths and make every attempt to ensure accuracy in their studies, and still deal with the inherent difficulties of measuring particular events or behaviors. Sometimes, and particularly in studies of natural settings, the only measuring device available is the researcher's own observations of human interaction or human reaction to varying stimuli. As these methods are ultimately subjective in nature, results may be unreliable and multiple interpretations are possible. Three of these inherent difficulties are quixotic reliability, diachronic reliability and synchronic reliability.

Quixotic reliability refers to the situation where a single manner of observation consistently, yet erroneously, yields the same result. It is often a problem when research appears to be going well. This consistency might seem to suggest that the experiment was demonstrating perfect stability reliability. This, however, would not be the case.

For example, if a measuring device used in an Olympic competition always read 100 meters for every discus throw, this would be an example of an instrument consistently, yet erroneously, yielding the same result. However, quixotic reliability is often more subtle in its occurrences than this. For example, suppose a group of German researchers doing an ethnographic study of American attitudes ask questions and record responses. Parts of their study might produce responses which seem reliable, yet turn out to measure felicitous verbal embellishments required for "correct" social behavior. Asking Americans, "How are you?" for example, would in most cases, elicit the token, "Fine, thanks." However, this response would not accurately represent the mental or physical state of the respondents.

Diachronic reliability refers to the stability of observations over time. It is similar to stability reliability in that it deals with time. While this type of reliability is appropriate to assess features that remain relatively unchanged over time, such as landscape benchmarks or buildings, the same level of reliability is more difficult to achieve with socio-cultural phenomena.

For example, in a follow-up study one year later of reading comprehension in a specific group of school children, diachronic reliability would be hard to achieve. If the test were given to the same subjects a year later, many confounding variables would have impacted the researchers' ability to reproduce the same circumstances present at the first test. The final results would almost assuredly not reflect the degree of stability sought by the researchers.

Synchronic reliability refers to the similarity of observations within the same time frame; it is not about the similarity of things observed. Synchronic reliability, unlike diachronic reliability, rarely involves observations of identical things. Rather, it concerns itself with particularities of interest to the research.

For example, a researcher studies the actions of a duck's wing in flight and the actions of a hummingbird's wing in flight. Despite the fact that the researcher is studying two distinctly different kinds of wings, the action of the wings and the phenomenon produced is the same.

Comments on a Flawed, Yet Influential Study

An example of the dangers of generalizing from research that is inconsistent, invalid, unreliable, and incomplete is found in the Time magazine article, "On A Screen Near You: Cyberporn" (De Witt, 1995). This article relies on a study done at Carnegie Mellon University to determine the extent and implications of online pornography. Inherent to the study are methodological problems of unqualified hypotheses and conclusions, unsupported generalizations and a lack of peer review.

Ignoring the functional problems that manifest themselves later in the study, it seems that there are a number of ethical problems within the article. The article claims to be an exhaustive study of pornography on the Internet, (it was anything but exhaustive), it resembles a case study more than anything else. Marty Rimm, author of the undergraduate paper that Time used as a basis for the article, claims the paper was an "exhaustive study" of online pornography when, in fact, the study based most of its conclusions about pornography on the Internet on the "descriptions of slightly more than 4,000 images" (Meeks, 1995, p. 1). Some USENET groups see hundreds of postings in a day.

Considering the thousands of USENET groups, 4,000 images no longer carries the authoritative weight that its author intended. The real problem is that the study (an undergraduate paper similar to a second-semester composition assignment) was based not on pornographic images themselves, but on the descriptions of those images. This kind of reduction detracts significantly from the integrity of the final claims made by the author. In fact, this kind of research is commensurate with doing a study of the content of pornographic movies based on the titles of the movies, then making sociological generalizations based on what those titles indicate. (This is obviously a problem with a number of types of validity, because Rimm is not studying what he thinks he is studying, but instead something quite different. )

The author of the Time article, Philip Elmer De Witt writes, "The research team at CMU has undertaken the first systematic study of pornography on the Information Superhighway" (Godwin, 1995, p. 1). His statement is problematic in at least three ways. First, the research team actually consisted of a few of Rimm's undergraduate friends with no methodological training whatsoever. Additionally, no mention of the degree of interrater reliability is made. Second, this systematic study is actually merely a "non-randomly selected subset of commercial bulletin-board systems that focus on selling porn" (Godwin, p. 6). As pornography vending is actually just a small part of the whole concerning the use of pornography on the Internet, the entire premise of this study's content validity is firmly called into question. Finally, the use of the term "Information Superhighway" is a false assessment of what in actuality is only a few USENET groups and BBSs (Bulletin Board System), which make up only a small fraction of the entire "Information Superhighway" traffic. Essentially, what is here is yet another violation of content validity.

De Witt is quoted as saying: "In an 18-month study, the team surveyed 917,410 sexually-explicit pictures, descriptions, short-stories and film clips. On those USENET newsgroups where digitized images are stored, 83.5 percent of the pictures were pornographic" (De Witt 40).

Statistically, some interesting contradictions arise. The figure 917,410 was taken from adult-oriented BBSs--none came from actual USENET groups or the Internet itself. This is a glaring discrepancy. Out of the 917,410 files, 212,114 are only descriptions (Hoffman & Novak, 1995, p.2). The question is, how many actual images did the "researchers" see?

"Between April and July 1994, the research team downloaded all available images (3,254)...the team encountered technical difficulties with 13 percent of these images...This left a total of 2,830 images for analysis" (p. 2). This means that out of 917,410 files discussed in this study, 914,580 of them were not even pictures! As for the 83.5 percent figure, this is actually based on "17 alt.binaries groups that Rimm considered pornographic" (p. 2).

In real terms, 17 USENET groups is a fraction of a percent of all USENET groups available. Worse yet, Time claimed that "...only about 3 percent of all messages on the USENET [represent pornographic material], while the USENET itself represents 11.5 percent of the traffic on the Internet" (De Witt, p. 40).

Time neglected to carry the interpretation of this data out to its logical conclusion, which is that less than half of 1 percent (3 percent of 11 percent) of the images on the Internet are associated with newsgroups that contain pornographic imagery. Furthermore, of this half percent, an unknown but even smaller percentage of the messages in newsgroups that are 'associated with pornographic imagery', actually contained pornographic material (Hoffman & Novak, p. 3).

Another blunder can be seen in the avoidance of peer-review, which suggests that there was some political interests being served in having the study become a Time cover story. Marty Rimm contracted the Georgetown Law Review and Time in an agreement to publish his study as long as they kept it under lock and key. During the months before publication, many interested scholars and professionals tried in vain to obtain a copy of the study in order to check it for flaws. De Witt justified not letting such peer-review take place, and also justified the reliability and validity of the study, on the grounds that because the Georgetown Law Review had accepted it, it was therefore reliable and valid, and needed no peer-review. What he didn't know, was that law reviews are not edited by professionals, but by "third year law students" (Godwin, p. 4).

There are many consequences of the failure to subject such a study to the scrutiny of peer review. If it was Rimm's desire to publish an article about on-line pornography in a manner that legitimized his article, yet escaped the kind of critical review the piece would have to undergo if published in a scholarly journal of computer-science, engineering, marketing, psychology, or communications. What better venue than a law journal? A law journal article would have the added advantage of being taken seriously by law professors, lawyers, and legally-trained policymakers. By virtue of where it appeared, it would automatically be catapulted into the center of the policy debate surrounding online censorship and freedom of speech (Godwin).

Herein lies the dangerous implication of such a study: Because the questions surrounding pornography are of such immediate political concern, the study was placed in the forefront of the U.S. domestic policy debate over censorship on the Internet, (an integral aspect of current anti-First Amendment legislation) with little regard for its validity or reliability.

On June 26, the day the article came out, Senator Grassley, (co-sponsor of the anti-porn bill, along with Senator Dole) began drafting a speech that was to be delivered that very day in the Senate, using the study as evidence. The same day, at the same time, Mike Godwin posted on WELL (Whole Earth 'Lectronic Link, a forum for professionals on the Internet) what turned out to be the overstatement of the year: "Philip's story is an utter disaster, and it will damage the debate about this issue because we will have to spend lots of time correcting misunderstandings that are directly attributable to the story" (Meeks, p. 7).

As Godwin was writing this, Senator Grassley was speaking to the Senate: "Mr. President, I want to repeat that: 83.5 percent of the 900,000 images reviewed--these are all on the Internet--are pornographic, according to the Carnegie-Mellon study" ( p. 7). Several days later, Senator Dole was waving the magazine in front of the Senate like a battle flag.

Donna Hoffman, professor at Vanderbilt University, summed up the dangerous political implications by saying, "The critically important national debate over First Amendment rights and restrictions of information on the Internet and other emerging media requires facts and informed opinion, not hysteria" (p.1).

In addition to the hysteria, Hoffman sees a plethora of other problems with the study. "Because the content analysis and classification scheme are 'black boxes,'" Hoffman said, "because no reliability and validity results are presented, because no statistical testing of the differences both within and among categories for different types of listings has been performed, and because not a single hypothesis has been tested, formally or otherwise, no conclusions should be drawn until the issues raised in this critique are resolved" (p. 4).

However, the damage has already been done. This questionable research by an undergraduate engineering major has been generalized to such an extent that even the U.S. Senate, and in particular Senators Grassley and Dole, have been duped, albeit through the strength of their own desires to see only what they wanted to see.

Annotated Bibliography

American Psychological Association. (1985). Standards for educational and psychological testing. Washington, DC: Author.

This work on focuses on reliability, validity and the standards that testers need to achieve in order to ensure accuracy.

Babbie, E.R. & Huitt, R.E. (1979). The practice of social research 2nd ed . Belmont, CA: Wadsworth Publishing.

An overview of social research and its applications.

Beauchamp, T. L., Faden, R.R., Wallace, Jr., R.J. & Walters, L . ( 1982). Ethical issues in social science research. Baltimore and London: The Johns Hopkins University Press.

A systematic overview of ethical issues in Social Science Research written by researchers with firsthand familiarity with the situations and problems researchers face in their work. This book raises several questions of how reliability and validity can be affected by ethics.

Borman, K.M. et al. (1986). Ethnographic and qualitative research design and why it doesn't work. American behavioral scientist 30 , 42-57.

The authors pose questions concerning threats to qualitative research and suggest solutions.

Bowen, K. A. (1996, Oct. 12). The sin of omission -punishable by death to internal validity: An argument for integration of quantitative research methods to strengthen internal validity. Available: http://trochim.human.cornell.edu/gallery/bowen/hss691.htm

An entire Web site that examines the merits of integrating qualitative and quantitative research methodologies through triangulation. The author argues that improving the internal validity of social science will be the result of such a union.

Brinberg, D. & McGrath, J.E. (1985). Validity and the research process . Beverly Hills: Sage Publications.

The authors investigate validity as value and propose the Validity Network Schema, a process by which researchers can infuse validity into their research.

Bussières, J-F. (1996, Oct.12). Reliability and validity of information provided by museum Web sites. Available: http://www.oise.on.ca/~jfbussieres/issue.html

This Web page examines the validity of museum Web sites which calls into question the validity of Web-based resources in general. Addresses the issue that all Websites should be examined with skepticism about the validity of the information contained within them.

Campbell, D. T. & Stanley, J.C. (1963). Experimental and quasi-experimental designs for research. Boston: Houghton Mifflin.

An overview of experimental research that includes pre-experimental designs, controls for internal validity, and tables listing sources of invalidity in quasi-experimental designs. Reference list and examples.

Carmines, E. G. & Zeller, R.A. (1991). Reliability and validity assessment . Newbury Park: Sage Publications.

An introduction to research methodology that includes classical test theory, validity, and methods of assessing reliability.

Carroll, K. M. (1995). Methodological issues and problems in the assessment of substance use. Psychological Assessment, Sep. 7 n3 , 349-58.

Discusses methodological issues in research involving the assessment of substance abuse. Introduces strategies for avoiding problems with the reliability and validity of methods.

Connelly, F. M. & Clandinin, D.J. (1990). Stories of experience and narrative inquiry. Educational Researcher 19:5 , 2-12.

A survey of narrative inquiry that outlines criteria, methods, and writing forms. It includes a discussion of risks and dangers in narrative studies, as well as a research agenda for curricula and classroom studies.

De Witt, P.E. (1995, July 3). On a screen near you: Cyberporn. Time, 38-45.

This is an exhaustive Carnegie Mellon study of online pornography by Marty Rimm, electrical engineering student.

Fink, A., ed. (1995). The survey Handbook, v.1 .Thousand Oaks, CA: Sage.

A guide to survey; this is the first in a series referred to as the "survey kit". It includes bibliograpgical references. Addresses survey design, analysis, reporting surveys and how to measure the validity and reliability of surveys.

Fink, A., ed. (1995). How to measure survey reliability and validity v. 7 . Thousand Oaks, CA: Sage.

This volume seeks to select and apply reliability criteria and select and apply validity criteria. The fundamental principles of scaling and scoring are considered.

Godwin, M. (1995, July). JournoPorn, dissection of the Time article. Available: http://www.hotwired.com

A detailed critique of Time magazine's Cyberporn , outlining flaws of methodology as well as exploring the underlying assumptions of the article.

Hambleton, R.K. & Zaal, J.N., eds. (1991). Advances in educational and psychological testing . Boston: Kluwer Academic.

Information on the concepts of reliability and validity in psychology and education.

Harnish, D.L. (1992). Human judgment and the logic of evidence: A critical examination of research methods in special education transition literature . In D.L. Harnish et al. eds., Selected readings in transition.

This article investigates threats to validity in special education research.

Haynes, N. M. (1995). How skewed is 'the bell curve'? Book Product Reviews . 1-24.

This paper claims that R.J. Herrnstein and C. Murray's The Bell Curve: Intelligence and Class Structure in American Life does not have scientific merit and claims that the bell curve is an unreliable measure of intelligence.

Healey, J. F. (1993). Statistics: A tool for social research, 3rd ed . Belmont: Wadsworth Publishing.

Inferential statistics, measures of association, and multivariate techniques in statistical analysis for social scientists are addressed.

Helberg, C. (1996, Oct.12). Pitfalls of data analysis (or how to avoid lies and damned lies). Available: http//maddog/fammed.wisc.edu/pitfalls/

A discussion of things researchers often overlook in their data analysis and how statistics are often used to skew reliability and validity for the researchers purposes.

Hoffman, D. L. and Novak, T.P. (1995, July). A detailed critique of the Time article: Cyberporn. Available: http://www.hotwired.com

A methodological critique of the Time article that uncovers some of the fundamental flaws in the statistics and the conclusions made by De Witt.

Huitt, William G. (1998). Internal and External Validity . http://www.valdosta.peachnet.edu/~whuitt/psy702/intro/valdgn.html

A Web document addressing key issues of external and internal validity.

Jones, J. E. & Bearley, W.L. (1996, Oct 12). Reliability and validity of training instruments. Organizational Universe Systems. Available: http://ous.usa.net/relval.htm

The authors discuss the reliability and validity of training design in a business setting. Basic terms are defined and examples provided.

Cultural Anthropology Methods Journal. (1996, Oct. 12). Available: http://www.lawrence.edu/~bradleyc/cam.html

An online journal containing articles on the practical application of research methods when conducting qualitative and quantitative research. Reliability and validity are addressed throughout.

Kirk, J. & Miller, M. M. (1986). Reliability and validity in qualitative research. Beverly Hills: Sage Publications.

This text describes objectivity in qualitative research by focusing on the issues of validity and reliability in terms of their limitations and applicability in the social and natural sciences.

Krakower, J. & Niwa, S. (1985). An assessment of validity and reliability of the institutinal perfarmance survey . Boulder, CO: National center for higher education management systems.

Educational surveys and higher education research and the efeectiveness of organization.

Lauer, J. M. & Asher, J.W. (1988). Composition Research. New York: Oxford University Press.

A discussion of empirical designs in the context of composition research as a whole.

Laurent, J. et al. (1992, Mar.) Review of validity research on the stanford-binet intelligence scale: 4th Ed. Psychological Assessment . 102-112.

This paper looks at the results of construct and criterion- related validity studies to determine if the SB:FE is a valid measure of intelligence.

LeCompte, M. D., Millroy, W.L., & Preissle, J. eds. (1992). The handbook of qualitative research in education. San Diego: Academic Press.

A compilation of the range of methodological and theoretical qualitative inquiry in the human sciences and education research. Numerous contributing authors apply their expertise to discussing a wide variety of issues pertaining to educational and humanities research as well as suggestions about how to deal with problems when conducting research.

McDowell, I. & Newell, C. (1987). Measuring health: A guide to rating scales and questionnaires . New York: Oxford University Press.

This gives a variety of examples of health measurement techniques and scales and discusses the validity and reliability of important health measures.

Meeks, B. (1995, July). Muckraker: How Time failed. Available: http://www.hotwired.com

A step-by-step outline of the events which took place during the researching, writing, and negotiating of the Time article of 3 July, 1995 titled: On A Screen Near You: Cyberporn .

Merriam, S. B. (1995). What can you tell from an N of 1?: Issues of validity and reliability in qualitative research. Journal of Lifelong Learning v4 , 51-60.

Addresses issues of validity and reliability in qualitative research for education. Discusses philosophical assumptions underlying the concepts of internal validity, reliability, and external validity or generalizability. Presents strategies for ensuring rigor and trustworthiness when conducting qualitative research.

Morris, L.L, Fitzgibbon, C.T., & Lindheim, E. (1987). How to measure performance and use tests. In J.L. Herman (Ed.), Program evaluation kit (2nd ed.). Newbury Park, CA: Sage.

Discussion of reliability and validity as it pertyains to measuring students' performance.

Murray, S., et al. (1979, April). Technical issues as threats to internal validity of experimental and quasi-experimental designs. San Francisco: University of California. 8-12.

(From Yang et al. bibliography--unavailable as of this writing.)

Russ-Eft, D. F. (1980). Validity and reliability in survey research. American Institutes for Research in the Behavioral Sciences August , 227 151.

An investigation of validity and reliability in survey research with and overview of the concepts of reliability and validity. Specific procedures for measuring sources of error are suggested as well as general suggestions for improving the reliability and validity of survey data. A extensive annotated bibliography is provided.

Ryser, G. R. (1994). Developing reliable and valid authentic assessments for the classroom: Is it possible? Journal of Secondary Gifted Education Fall, v6 n1 , 62-66.

Defines the meanings of reliability and validity as they apply to standardized measures of classroom assessment. This article defines reliability as scorability and stability and validity is seen as students' ability to use knowledge authentically in the field.

Schmidt, W., et al. (1982). Validity as a variable: Can the same certification test be valid for all students? Institute for Research on Teaching July, ED 227 151.

A technical report that presents specific criteria for judging content, instructional and curricular validity as related to certification tests in education.

Scholfield, P. (1995). Quantifying language. A researcher's and teacher's guide to gathering language data and reducing it to figures . Bristol: Multilingual Matters.

A guide to categorizing, measuring, testing, and assessing aspects of language. A source for language-related practitioners and researchers in conjunction with other resources on research methods and statistics. Questions of reliability, and validity are also explored.

Scriven, M. (1993). Hard-Won Lessons in Program Evaluation . San Francisco: Jossey-Bass Publishers.

A common sense approach for evaluating the validity of various educational programs and how to address specific issues facing evaluators.

Shou, P. (1993, Jan.). The singer loomis inventory of personality: A review and critique. [Paper presented at the Annual Meeting of the Southwest Educational Research Association.]

Evidence for reliability and validity are reviewed. A summary evaluation suggests that SLIP (developed by two Jungian analysts to allow examination of personality from the perspective of Jung's typology) appears to be a useful tool for educators and counselors.

Sutton, L.R. (1992). Community college teacher evaluation instrument: A reliability and validity study . Diss. Colorado State University.

Studies of reliability and validity in occupational and educational research.

Thompson, B. & Daniel, L.G. (1996, Oct.). Seminal readings on reliability and validity: A "hit parade" bibliography. Educational and psychological measurement v. 56 , 741-745.

Editorial board members of Educational and Psychological Measurement generated bibliography of definitive publications of measurement research. Many articles are directly related to reliability and validity.

Thompson, E. Y., et al. (1995). Overview of qualitative research . Diss. Colorado State University.

A discussion of strengths and weaknesses of qualitative research and its evolution and adaptation. Appendices and annotated bibliography.

Traver, C. et al. (1995). Case Study . Diss. Colorado State University.

This presentation gives an overview of case study research, providing definitions and a brief history and explanation of how to design research.

Trochim, William M. K. (1996) External validity. (. Available: http://trochim.human.cornell.edu/kb/EXTERVAL.htm

A comprehensive treatment of external validity found in William Trochim's online text about research methods and issues.

Trochim, William M. K. (1996) Introduction to validity. (. Available: hhttp://trochim.human.cornell.edu/kb/INTROVAL.htm

An introduction to validity found in William Trochim's online text about research methods and issues.

Trochim, William M. K. (1996) Reliability. (. Available: http://trochim.human.cornell.edu/kb/reltypes.htm

A comprehensive treatment of reliability found in William Trochim's online text about research methods and issues.

Validity. (1996, Oct. 12). Available: http://vislab-www.nps.navy.mil/~haga/validity.html

A source for definitions of various forms and types of reliability and validity.

Vinsonhaler, J. F., et al. (1983, July). Improving diagnostic reliability in reading through training. Institute for Research on Teaching ED 237 934.

This technical report investigates the practical application of a program intended to improve the diagnoses of reading deficient students. Here, reliability is assumed and a pragmatic answer to a specific educational problem is suggested as a result.

Wentland, E. J. & Smith, K.W. (1993). Survey responses: An evaluation of their validity . San Diego: Academic Press.

This book looks at the factors affecting response validity (or the accuracy of self-reports in surveys) and provides several examples with varying accuracy levels.

Wiget, A. (1996). Father juan greyrobe: Reconstructing tradition histories, and the reliability and validity of uncorroborated oral tradition. Ethnohistory 43:3 , 459-482.

This paper presents a convincing argument for the validity of oral histories in ethnographic research where at least some of the evidence can be corroborated through written records.

Yang, G. H., et al. (1995). Experimental and quasi-experimental educational research . Diss. Colorado State University.

This discussion defines experimentation and considers the rhetorical issues and advantages and disadvantages of experimental research. Annotated bibliography.

Yarroch, W. L. (1991, Sept.). The Implications of content versus validity on science tests. Journal of Research in Science Teaching , 619-629.

The use of content validity as the primary assurance of the measurement accuracy for science assessment examinations is questioned. An alternative accuracy measure, item validity, is proposed to look at qualitative comparisons between different factors.

Yin, R. K. (1989). Case study research: Design and methods . London: Sage Publications.

This book discusses the design process of case study research, including collection of evidence, composing the case study report, and designing single and multiple case studies.

Reliability vs. Validity in Research: Types & Examples

Explore how reliability vs validity in research determines quality. Learn the differences and types + examples. Get insights!

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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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..

Publication types

Biomedical Research*
Reproducibility of Results

The Meaning of Reliability in Sociology

Four Procedures for Assessing Reliability

Key Concepts
Major Sociologists
News & Issues
Research, Samples, and Statistics
Recommended Reading
Archaeology

Reliability is the degree to which a measurement instrument gives the same results each time that it is used, assuming that the underlying thing being measured does not change.

Key Takeaways: Reliability

If a measurement instrument provides similar results each time it is used (assuming that whatever is being measured stays the same over time), it is said to have high reliability.
Good measurement instruments should have both high reliability and high accuracy.
Four methods sociologists can use to assess reliability are the test-retest procedure, the alternate forms procedure, the split-halves procedure, and the internal consistency procedure.

Imagine that you’re trying to assess the reliability of a thermometer in your home. If the temperature in a room stays the same, a reliable thermometer will always give the same reading. A thermometer that lacks reliability would change even when the temperature does not. Note, however, that the thermometer does not have to be accurate in order to be reliable. It might always register three degrees too high, for example. Its degree of reliability has to do instead with the predictability of its relationship with whatever is being tested.

Methods to Assess Reliability

In order to assess reliability, the thing being measured must be measured more than once. For example, if you wanted to measure the length of a sofa to make sure it would fit through a door, you might measure it twice. If you get an identical measurement twice, you can be confident you measured reliably.

There are four procedures for assessing the reliability of a test. (Here, the term "test" refers to a group of statements on a questionnaire, an observer's quantitative or qualitative evaluation, or a combination of the two.)

The Test-Retest Procedure

Here, the same test is given two or more times. For example, you might create a questionnaire with a set of ten statements to assess confidence. These ten statements are then given to a subject twice at two different times. If the respondent gives similar answers both times, you can assume the questions assessed the subject's answers reliably.

One advantage of this method is that only one test needs to be developed for this procedure. However, there are a few downsides of the test-retest procedure. Events might occur between testing times that affect the respondents' answers; answers might change over time simply because people change and grow over time; and the subject might adjust to the test the second time around, think more deeply about the questions, and reevaluate their answers. For instance, in the example above, some respondents might have become more confident between the first and second testing session, which would make it more difficult to interpret the results of the test-retest procedure.

The Alternate Forms Procedure

In the alternate forms procedure (also called parallel forms reliability ), two tests are given. For example, you might create two sets of five statements measuring confidence. Subjects would be asked to take each of the five-statement questionnaires. If the person gives similar answers for both tests, you can assume you measured the concept reliably. One advantage is that cueing will be less of a factor because the two tests are different. However, it's important to ensure that both alternate versions of the test are indeed measuring the same thing.

The Split-Halves Procedure

In this procedure, a single test is given once. A grade is assigned to each half separately and grades are compared from each half. For example, you might have one set of ten statements on a questionnaire to assess confidence. Respondents take the test and the questions are then split into two sub-tests of five items each. If the score on the first half mirrors the score on the second half, you can presume that the test measured the concept reliably. On the plus side, history, maturation, and cueing aren't at play. However, scores can vary greatly depending on the way in which the test is divided into halves.

The Internal Consistency Procedure

Here, the same test is administered once, and the score is based upon average similarity of responses. For example, in a ten-statement questionnaire to measure confidence, each response can be seen as a one-statement sub-test. The similarity in responses to each of the ten statements is used to assess reliability. If the respondent doesn't answer all ten statements in a similar way, then one can assume that the test is not reliable. One way that researchers can assess internal consistency is by using statistical software to calculate Cronbach’s alpha .

With the internal consistency procedure, history, maturation, and cueing aren't a consideration. However, the number of statements in the test can affect the assessment of reliability when assessing it internally.

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Reliability and Validity of Measurement

Learning objectives.

Define reliability, including the different types and how they are assessed.
Define validity, including the different types and how they are assessed.
Describe the kinds of evidence that would be relevant to assessing the reliability and validity of a particular measure.

Again, measurement involves assigning scores to individuals so that they represent some characteristic of the individuals. But how do researchers know that the scores actually represent the characteristic, especially when it is a construct like intelligence, self-esteem, depression, or working memory capacity? The answer is that they conduct research using the measure to confirm that the scores make sense based on their understanding of the construct being measured. This is an extremely important point. Psychologists do not simply assume that their measures work. Instead, they collect data to demonstrate that they work. If their research does not demonstrate that a measure works, they stop using it.

As an informal example, imagine that you have been dieting for a month. Your clothes seem to be fitting more loosely, and several friends have asked if you have lost weight. If at this point your bathroom scale indicated that you had lost 10 pounds, this would make sense and you would continue to use the scale. But if it indicated that you had gained 10 pounds, you would rightly conclude that it was broken and either fix it or get rid of it. In evaluating a measurement method, psychologists consider two general dimensions: reliability and validity.

Reliability

Reliability refers to the consistency of a measure. Psychologists consider three types of consistency: over time (test-retest reliability), across items (internal consistency), and across different researchers (inter-rater reliability).

Test-Retest Reliability

When researchers measure a construct that they assume to be consistent across time, then the scores they obtain should also be consistent across time. Test-retest reliability is the extent to which this is actually the case. For example, intelligence is generally thought to be consistent across time. A person who is highly intelligent today will be highly intelligent next week. This means that any good measure of intelligence should produce roughly the same scores for this individual next week as it does today. Clearly, a measure that produces highly inconsistent scores over time cannot be a very good measure of a construct that is supposed to be consistent.

Assessing test-retest reliability requires using the measure on a group of people at one time, using it again on the same group of people at a later time, and then looking at test-retest correlation between the two sets of scores. This is typically done by graphing the data in a scatterplot and computing Pearson’s r . Figure 5.2 shows the correlation between two sets of scores of several university students on the Rosenberg Self-Esteem Scale, administered two times, a week apart. Pearson’s r for these data is +.95. In general, a test-retest correlation of +.80 or greater is considered to indicate good reliability.

Figure 5.2 Test-Retest Correlation Between Two Sets of Scores of Several College Students on the Rosenberg Self-Esteem Scale, Given Two Times a Week Apart

Again, high test-retest correlations make sense when the construct being measured is assumed to be consistent over time, which is the case for intelligence, self-esteem, and the Big Five personality dimensions. But other constructs are not assumed to be stable over time. The very nature of mood, for example, is that it changes. So a measure of mood that produced a low test-retest correlation over a period of a month would not be a cause for concern.

Internal Consistency

A second kind of reliability is internal consistency , which is the consistency of people’s responses across the items on a multiple-item measure. In general, all the items on such measures are supposed to reflect the same underlying construct, so people’s scores on those items should be correlated with each other. On the Rosenberg Self-Esteem Scale, people who agree that they are a person of worth should tend to agree that that they have a number of good qualities. If people’s responses to the different items are not correlated with each other, then it would no longer make sense to claim that they are all measuring the same underlying construct. This is as true for behavioural and physiological measures as for self-report measures. For example, people might make a series of bets in a simulated game of roulette as a measure of their level of risk seeking. This measure would be internally consistent to the extent that individual participants’ bets were consistently high or low across trials.

Like test-retest reliability, internal consistency can only be assessed by collecting and analyzing data. One approach is to look at a split-half correlation . This involves splitting the items into two sets, such as the first and second halves of the items or the even- and odd-numbered items. Then a score is computed for each set of items, and the relationship between the two sets of scores is examined. For example, Figure 5.3 shows the split-half correlation between several university students’ scores on the even-numbered items and their scores on the odd-numbered items of the Rosenberg Self-Esteem Scale. Pearson’s r for these data is +.88. A split-half correlation of +.80 or greater is generally considered good internal consistency.

Figure 5.3 Split-Half Correlation Between Several College Students’ Scores on the Even-Numbered Items and Their Scores on the Odd-Numbered Items of the Rosenberg Self-Esteem Scale

Perhaps the most common measure of internal consistency used by researchers in psychology is a statistic called Cronbach’s α (the Greek letter alpha). Conceptually, α is the mean of all possible split-half correlations for a set of items. For example, there are 252 ways to split a set of 10 items into two sets of five. Cronbach’s α would be the mean of the 252 split-half correlations. Note that this is not how α is actually computed, but it is a correct way of interpreting the meaning of this statistic. Again, a value of +.80 or greater is generally taken to indicate good internal consistency.

Interrater Reliability

Many behavioural measures involve significant judgment on the part of an observer or a rater. Inter-rater reliability is the extent to which different observers are consistent in their judgments. For example, if you were interested in measuring university students’ social skills, you could make video recordings of them as they interacted with another student whom they are meeting for the first time. Then you could have two or more observers watch the videos and rate each student’s level of social skills. To the extent that each participant does in fact have some level of social skills that can be detected by an attentive observer, different observers’ ratings should be highly correlated with each other. Inter-rater reliability would also have been measured in Bandura’s Bobo doll study. In this case, the observers’ ratings of how many acts of aggression a particular child committed while playing with the Bobo doll should have been highly positively correlated. Interrater reliability is often assessed using Cronbach’s α when the judgments are quantitative or an analogous statistic called Cohen’s κ (the Greek letter kappa) when they are categorical.

Validity is the extent to which the scores from a measure represent the variable they are intended to. But how do researchers make this judgment? We have already considered one factor that they take into account—reliability. When a measure has good test-retest reliability and internal consistency, researchers should be more confident that the scores represent what they are supposed to. There has to be more to it, however, because a measure can be extremely reliable but have no validity whatsoever. As an absurd example, imagine someone who believes that people’s index finger length reflects their self-esteem and therefore tries to measure self-esteem by holding a ruler up to people’s index fingers. Although this measure would have extremely good test-retest reliability, it would have absolutely no validity. The fact that one person’s index finger is a centimetre longer than another’s would indicate nothing about which one had higher self-esteem.

Discussions of validity usually divide it into several distinct “types.” But a good way to interpret these types is that they are other kinds of evidence—in addition to reliability—that should be taken into account when judging the validity of a measure. Here we consider three basic kinds: face validity, content validity, and criterion validity.

Face Validity

Face validity is the extent to which a measurement method appears “on its face” to measure the construct of interest. Most people would expect a self-esteem questionnaire to include items about whether they see themselves as a person of worth and whether they think they have good qualities. So a questionnaire that included these kinds of items would have good face validity. The finger-length method of measuring self-esteem, on the other hand, seems to have nothing to do with self-esteem and therefore has poor face validity. Although face validity can be assessed quantitatively—for example, by having a large sample of people rate a measure in terms of whether it appears to measure what it is intended to—it is usually assessed informally.

Face validity is at best a very weak kind of evidence that a measurement method is measuring what it is supposed to. One reason is that it is based on people’s intuitions about human behaviour, which are frequently wrong. It is also the case that many established measures in psychology work quite well despite lacking face validity. The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) measures many personality characteristics and disorders by having people decide whether each of over 567 different statements applies to them—where many of the statements do not have any obvious relationship to the construct that they measure. For example, the items “I enjoy detective or mystery stories” and “The sight of blood doesn’t frighten me or make me sick” both measure the suppression of aggression. In this case, it is not the participants’ literal answers to these questions that are of interest, but rather whether the pattern of the participants’ responses to a series of questions matches those of individuals who tend to suppress their aggression.

Content Validity

Content validity is the extent to which a measure “covers” the construct of interest. For example, if a researcher conceptually defines test anxiety as involving both sympathetic nervous system activation (leading to nervous feelings) and negative thoughts, then his measure of test anxiety should include items about both nervous feelings and negative thoughts. Or consider that attitudes are usually defined as involving thoughts, feelings, and actions toward something. By this conceptual definition, a person has a positive attitude toward exercise to the extent that he or she thinks positive thoughts about exercising, feels good about exercising, and actually exercises. So to have good content validity, a measure of people’s attitudes toward exercise would have to reflect all three of these aspects. Like face validity, content validity is not usually assessed quantitatively. Instead, it is assessed by carefully checking the measurement method against the conceptual definition of the construct.

Criterion Validity

Criterion validity is the extent to which people’s scores on a measure are correlated with other variables (known as criteria ) that one would expect them to be correlated with. For example, people’s scores on a new measure of test anxiety should be negatively correlated with their performance on an important school exam. If it were found that people’s scores were in fact negatively correlated with their exam performance, then this would be a piece of evidence that these scores really represent people’s test anxiety. But if it were found that people scored equally well on the exam regardless of their test anxiety scores, then this would cast doubt on the validity of the measure.

A criterion can be any variable that one has reason to think should be correlated with the construct being measured, and there will usually be many of them. For example, one would expect test anxiety scores to be negatively correlated with exam performance and course grades and positively correlated with general anxiety and with blood pressure during an exam. Or imagine that a researcher develops a new measure of physical risk taking. People’s scores on this measure should be correlated with their participation in “extreme” activities such as snowboarding and rock climbing, the number of speeding tickets they have received, and even the number of broken bones they have had over the years. When the criterion is measured at the same time as the construct, criterion validity is referred to as concurrent validity ; however, when the criterion is measured at some point in the future (after the construct has been measured), it is referred to as predictive validity (because scores on the measure have “predicted” a future outcome).

Criteria can also include other measures of the same construct. For example, one would expect new measures of test anxiety or physical risk taking to be positively correlated with existing measures of the same constructs. This is known as convergent validity .

Assessing convergent validity requires collecting data using the measure. Researchers John Cacioppo and Richard Petty did this when they created their self-report Need for Cognition Scale to measure how much people value and engage in thinking (Cacioppo & Petty, 1982) [1] . In a series of studies, they showed that people’s scores were positively correlated with their scores on a standardized academic achievement test, and that their scores were negatively correlated with their scores on a measure of dogmatism (which represents a tendency toward obedience). In the years since it was created, the Need for Cognition Scale has been used in literally hundreds of studies and has been shown to be correlated with a wide variety of other variables, including the effectiveness of an advertisement, interest in politics, and juror decisions (Petty, Briñol, Loersch, & McCaslin, 2009) [2] .

Discriminant Validity

Discriminant validity , on the other hand, is the extent to which scores on a measure are not correlated with measures of variables that are conceptually distinct. For example, self-esteem is a general attitude toward the self that is fairly stable over time. It is not the same as mood, which is how good or bad one happens to be feeling right now. So people’s scores on a new measure of self-esteem should not be very highly correlated with their moods. If the new measure of self-esteem were highly correlated with a measure of mood, it could be argued that the new measure is not really measuring self-esteem; it is measuring mood instead.

When they created the Need for Cognition Scale, Cacioppo and Petty also provided evidence of discriminant validity by showing that people’s scores were not correlated with certain other variables. For example, they found only a weak correlation between people’s need for cognition and a measure of their cognitive style—the extent to which they tend to think analytically by breaking ideas into smaller parts or holistically in terms of “the big picture.” They also found no correlation between people’s need for cognition and measures of their test anxiety and their tendency to respond in socially desirable ways. All these low correlations provide evidence that the measure is reflecting a conceptually distinct construct.

Key Takeaways

Psychological researchers do not simply assume that their measures work. Instead, they conduct research to show that they work. If they cannot show that they work, they stop using them.
There are two distinct criteria by which researchers evaluate their measures: reliability and validity. Reliability is consistency across time (test-retest reliability), across items (internal consistency), and across researchers (interrater reliability). Validity is the extent to which the scores actually represent the variable they are intended to.
Validity is a judgment based on various types of evidence. The relevant evidence includes the measure’s reliability, whether it covers the construct of interest, and whether the scores it produces are correlated with other variables they are expected to be correlated with and not correlated with variables that are conceptually distinct.
The reliability and validity of a measure is not established by any single study but by the pattern of results across multiple studies. The assessment of reliability and validity is an ongoing process.
Practice: Ask several friends to complete the Rosenberg Self-Esteem Scale. Then assess its internal consistency by making a scatterplot to show the split-half correlation (even- vs. odd-numbered items). Compute Pearson’s r too if you know how.
Discussion: Think back to the last college exam you took and think of the exam as a psychological measure. What construct do you think it was intended to measure? Comment on its face and content validity. What data could you collect to assess its reliability and criterion validity?
Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42 , 116–131. ↵
Petty, R. E, Briñol, P., Loersch, C., & McCaslin, M. J. (2009). The need for cognition. In M. R. Leary & R. H. Hoyle (Eds.), Handbook of individual differences in social behaviour (pp. 318–329). New York, NY: Guilford Press. ↵

Research Methods in Psychology Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Helen Noble 1 ,
Joanna Smith 2
1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
2 School of Human and Health Sciences, University of Huddersfield , Huddersfield , UK
Correspondence to Dr Helen Noble School of Nursing and Midwifery, Queens's University Belfast, Medical Biology Centre, 97 Lisburn Rd, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2015-102054

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Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery. In a previous article we explored ‘bias’ across research designs and outlined strategies to minimise bias. 1 The aim of this article is to further outline rigour, or the integrity in which a study is conducted, and ensure the credibility of findings in relation to qualitative research. Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research. In addition, some of the strategies adopted by qualitative researchers to enhance the credibility of their research are outlined.

Are the terms reliability and validity relevant to ensuring credibility in qualitative research?

Although the tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evaluate qualitative research. 2–4 In the broadest context these terms are applicable, with validity referring to the integrity and application of the methods undertaken and the precision in which the findings accurately reflect the data, while reliability describes consistency within the employed analytical procedures. 4 However, if qualitative methods are inherently different from quantitative methods in terms of philosophical positions and purpose, then alterative frameworks for establishing rigour are appropriate. 3 Lincoln and Guba 5 offer alternative criteria for demonstrating rigour within qualitative research namely truth value, consistency and neutrality and applicability. Table 1 outlines the differences in terminology and criteria used to evaluate qualitative research.

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Terminology and criteria used to evaluate the credibility of research findings

What strategies can qualitative researchers adopt to ensure the credibility of the study findings?

Unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the ‘trustworthiness’ of the findings. Such strategies include:

Accounting for personal biases which may have influenced findings; 6

Acknowledging biases in sampling and ongoing critical reflection of methods to ensure sufficient depth and relevance of data collection and analysis; 3

Meticulous record keeping, demonstrating a clear decision trail and ensuring interpretations of data are consistent and transparent; 3 , 4

Establishing a comparison case/seeking out similarities and differences across accounts to ensure different perspectives are represented; 6 , 7

Including rich and thick verbatim descriptions of participants’ accounts to support findings; 7

Demonstrating clarity in terms of thought processes during data analysis and subsequent interpretations 3 ;

Engaging with other researchers to reduce research bias; 3

Respondent validation: includes inviting participants to comment on the interview transcript and whether the final themes and concepts created adequately reflect the phenomena being investigated; 4

Data triangulation, 3 , 4 whereby different methods and perspectives help produce a more comprehensive set of findings. 8 , 9

Table 2 provides some specific examples of how some of these strategies were utilised to ensure rigour in a study that explored the impact of being a family carer to patients with stage 5 chronic kidney disease managed without dialysis. 10

Strategies for enhancing the credibility of qualitative research

In summary, it is imperative that all qualitative researchers incorporate strategies to enhance the credibility of a study during research design and implementation. Although there is no universally accepted terminology and criteria used to evaluate qualitative research, we have briefly outlined some of the strategies that can enhance the credibility of study findings.

Sandelowski M
Lincoln YS ,
Barrett M ,
Mayan M , et al
Greenhalgh T
Lingard L ,

Twitter Follow Joanna Smith at @josmith175 and Helen Noble at @helnoble

Competing interests None.

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Reliability in research refers to the consistency of a measure. It demonstrates whehter the same results would be obtained if the study was repeated. If a test or tool is reliable, it gives consistent results across different situations or over time. A study with high reliability can be trusted because its outcomes are dependable and can be reproduced. Unreliable research can lead to misleading or incorrect conclusions. That's why you should ensure that your study results can be trusted.

When you’ve collected your data and need to measure your research results, it’s time to consider the reliability level of your methods and tools. It often happens that calculation methods produce errors. Particularly, in case you make wrong initial assumptions. In order to avoid getting wrong conclusions it is better to invest some time into checking whether they are reliable. Today we’ll talk about the reliability of research approaches, what it means and how to check it properly. Main verification methods such as split-half, inter-item and inter-rater will be examined and explained below. Let’s go and find out how to use them with our PhD dissertation writing services !

What Is Reliability in Research: Definition

First, let’s define reliability . It is highly important to ensure your data analysis methods are reliable, meaning that they are likely to produce stable and consistent results whenever you use them against different datasets. So, a special parameter named ‘reliability’ has been introduced in order to evaluate their consistency. High reliability means that a method or a tool you are evaluating will repeatedly produce the same or similar results when the conditions remain stable. This parameter has the following key components:

probability
availability
dependability.

Follow our thesis writing services to find out what are the main types of this parameter and how they can be used.

Main Types of Reliability

There are four main types of reliability. Each of them shows consistency of a different approach to data collection and analysis. These types are related to different ways of conducting research, however all of them are equally considered as quality measurements for the tools and methods they describe. We’ll examine each of these 4 types below, discussing their differences, purposes and areas of usage. Let’s take a closer look!

Test Retest Reliability: Definition

The first type is called ‘test-retest’ reliability. You can use it in case you need to analyze methods which are to be applied to the same group of individuals many times. When running the same tests across the same object over and over again, it is important to know whether they produce reliable results. If the latter don’t change significantly over a period of time, we can assume that this parameter shows a high consistency level. Therefore, these methods must be helpful for your research.

Test Retest Reliability: Examples

Let’s review an example of test-retest reliability which might provide more clarity about this parameter for a student preparing their own research. Suppose, a group of a local mall’s consumers has been monitored by a research team for several years. Shopping habits and preferences of each person of the group were examined, particularly by conducting surveys . If their responses did not change significantly over those years, it means that the current research approach can be considered reliable from the test-retest aspect. Otherwise, some of the methods used to collect this data need to be reviewed and updated to avoid introducing errors in the research.

Parallel Forms Reliability: Definition

Another type is parallel forms reliability. It is applied to a research approach when different versions of an assessment tool are used to examine the same group of respondents. In case the results obtained with the help of all these versions correlate with each other, the approach can be considered reliable. However, an analyst needs to ensure that all the versions contain the same elements before assessing their consistency. For example, if two versions examine different qualities of the target group, it wouldn’t make much sense to compare one version to another.

Parallel Forms Reliability: Examples

A parallel forms reliability example using a real-life situation would help illustrate the definition provided above. Let’s take the previous example where a focus group of consumers is examined to analyze dependencies and trends of a local mall’s goods consumption. Let’s suppose the data about their shopping preferences is obtained by conducting a survey among them, one or several times. At the next stage the same data is collected by analyzing the mall’s sales information. In both cases an assessment tool refers to the same characteristics (e.g., preferred shopping hours). If the results are correlated in both cases, it means that the approach is consistent.

Inter Rater Reliability: Definition

The next type is called inter-rater reliability. This measure does not involve different tools but requires a collective effort of several researchers, or raters, to examine the target population independently from each other. Once they are done with that, their assessment results need to be compared across each other. Strong correlation between all these results would mean that the methods used in this case are consistent. In case some of the observers don’t agree with others, the assessment approach to this problem needs to be reviewed and most probably corrected.

Inter Rater Reliability: Examples

Let’s review an inter rater reliability example – another case to help you visualize this parameter and the ways to use it in your own research. We’ll suppose that the consumer focus group from the previous example is independently tested by three researchers who use the same set of testing types:

conducting surveys.
interviewing respondents about their preferred items (e.g. bakery or housing supplies) or preferred shopping hours.
analyzing sales statistics collected by the mall.

In case each of these researchers obtains the same or very similar results at the end leading to similar conclusions, we can assume that the research approach used in this project is consistent.

What Is Internal Consistency Reliability: Definition

The final type is called internal consistency reliability. This measure can be used to evaluate the degree to which different tools or parts of a test produce similar results after probing the same area or object. The purpose is to try calculating or analyzing some value using different ways. In case the same results are obtained in each case, we can assume that the measurement method itself is consistent. Depending on how precise the calculations are, small deviations between these results may or may not be allowed.

Internal Consistency Reliability: Examples

In the end of this review of reliability types let’s check out an internal consistency reliability example. Let’s take the same situation as described in previous examples: a focus consumer group whose shopping preferences are analyzed with the help of several different methods. In order to test the consistency of these methods, a researcher can randomly split the focus group in half and analyze each half independently. If done properly, random splitting must provide two subgroups with nearly identical qualities, so they can be viewed as the same construct. If analytic measures provide strongly correlated results for both these groups, the research approach is consistent.

Reliability Coefficient: What Is It

In order to evaluate how well a test measures a selected object, a special parameter named reliability coefficient has been introduced. Its definition is fully explained by its name: it shows whether a test is repeatable or reliable. The coefficient is a number lying within the range between 0 and 1.00, where 0 indicates no reliability and 1.00 indicates perfect reliability. The following proportion is used to calculate this coefficient, R:

R = (N/(N-1)) * ((Total Variance - Sum of Variance)/Total Variance),

where N is the number of times the test has been run. A real test could hardly have a perfect reliability. Typically, having the coefficient of 0.8 or higher means the test can be considered reliable enough.

Reliability: The Same as Quality

It is important to understand the difference between quality vs reliability. These concepts are somewhat related however they have different practical meanings. We use quality to indicate that an object or a solution performs its proper functions well and allows its users to achieve the intended purpose. Reliability indicates how well this object or solution is able to maintain its quality level as time passes or conditions change. It can be stated that reliability is one of the subsets of quality which is used to evaluate the consistency of a certain object or solution in a dynamic environment. Because of its nature, reliability is a probabilistic value. We also have a reliability vs validity blog. It is so crucial to understanding their difference for your research.

Reliability: Key Takeaways

In this article we have reviewed the concept of reliability in research. Its main types and their usage in real life research cases have been examined to a certain degree. Ways of measuring this value, particularly its coefficient, have also been explained.

In case you are having troubles with using this concept in your own work or just need help with writing a high quality paper and earning a high score – feel free to check out our writing services! A team of skilled writers with rich experience in various academic areas is ready to help you upon ‘ write a paper for me ’ request.

Reliability: Frequently Asked Questions

1. how do you determine reliability in research.

One can determine reliability in research using a simple correlation between two scores from the same person. It is quite easy to make a rough estimation of a reliability coefficient for these two items using the formula provided above. In order to make a more precise estimation, you’ll need to obtain more scores and use them for calculation. The more test runs you make, the more precise your coefficient is.

2. Why is reliability important in research?

Reliability refers to the consistency of the results in research. This makes reliability important for nearly any kind of research: psychological, economical, industrial, social etc.. A project that may affect the lives of many people needs to be conducted carefully and its results need to be double checked. If the methods used have been unreliable, its results may contain errors and cause negative effects.

4. What is reliability of a test?

Reliability of a test refers to the extent to which this test can be run without errors. The higher the reliability, the more usable your tests are and the less the probability of errors in your research is. Tests might be constructed incorrectly because of wrong assumptions or incorrect information received from a source. Measuring reliability helps to counter that and to find the ways to improve the quality of tests.

3. How does reliability affect research?

Levels of reliability affect each project which uses complex analysis methods. It is important to know the degree to which your research method produces stable and consistent results. In case the consistency is low, your work might be useless because of incorrect assumptions. If you don’t want your project to fail, you have to assess the consistency of your methods.

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A Review of the Quality Indicators of Rigor in Qualitative Research

Jessica l. johnson.

a William Carey University School of Pharmacy, Biloxi, Mississippi

Donna Adkins

Sheila chauvin.

b Louisiana State University, School of Medicine, New Orleans, Louisiana

Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework, both of which contribute to the selection of appropriate research methods that enhance trustworthiness and minimize researcher bias inherent in qualitative methodologies. Qualitative data collection and analyses are often modified through an iterative approach to answering the research question. Researcher reflexivity, essentially a researcher’s insight into their own biases and rationale for decision-making as the study progresses, is critical to rigor. This article reviews common standards of rigor, quality scholarship criteria, and best practices for qualitative research from design through dissemination.

INTRODUCTION

Within the past 20 years, qualitative research in health professions education has increased significantly, both in practice and publication. Today, one can pick up most any issue of a wide variety of health professions education journals and find at least one article that includes some type of qualitative research, whether a full study or the inclusion of a qualitative component within a quantitative or mixed methods study. Simultaneously, there have been recurrent calls for enhancing rigor and quality in qualitative research.

As members of the academic community, we share responsibility for ensuring rigor in qualitative research, whether as researchers who design and implement, manuscript reviewers who critique, colleagues who discuss and learn from each other, or scholarly teachers who draw upon results to enhance and innovate education. Therefore, the purpose of this article is to summarize standards of rigor and suggested best practices for designing, conducting, and reporting high-quality qualitative research. To begin, Denzin and Lincoln’s definition of qualitative research, a long-standing cornerstone in the field, provides a useful foundation for summarizing quality standards and best practices:

Qualitative research involves the studied use and collection of a variety of empirical materials – case study; personal experience; introspection; life story; interview; artifacts; cultural texts and productions; observational, historical, interactional, and visual texts – that describe the routine and problematic moments and meanings in individual lives. Accordingly, qualitative researchers deploy a wide range of interconnected interpretative practices, hoping always to get a better understanding of the subject matter at hand. It is understood, however, that each practice makes the world visible in a different way. Hence there is frequently a commitment to using more than one interpretative practice in any study. 1

In recent years, multiple publications have synthesized quality criteria and recommendations for use by researchers and peer reviewers alike, often in the form of checklists. 2-6 Some authors have raised concerns about the use of such checklists and adherence to strict, universal criteria because they do not afford sufficient flexibility to accommodate the diverse approaches and multiple interpretive practices often represented in qualitative studies. 7-11 They argue that a strict focus on using checklists of specific technical criteria may stifle the diversity and multiplicity of practices that are so much a part of achieving quality and rigor within the qualitative paradigm. As an alternative, some of these authors have published best practice guidelines for use by researchers and peer reviewers to achieve and assess methodological rigor and research quality. 12,13

Some journals within the field of health professions education have also established best practice guidance, as opposed to strict criteria or a checklist, for qualitative research. These have been disseminated as guiding questions or evaluation categories. In 2015, Academic Medicine produced an expanded second edition of a researcher/author manual that includes specific criteria with extensive explanations and examples. 14 Still others have disseminated best practice guidelines through a series of methodological articles within journal publications. 2

In this article, attributes of rigor and quality and suggested best practices are presented as they relate to the steps of designing, conducting, and reporting qualitative research in a step-wise approach.

BEST PRACTICES: STEP-WISE APPROACH

Step 1: identifying a research topic.

Identifying and developing a research topic is comprised of two major tasks: formulating a research question, and developing a conceptual framework to support the study. Formulating a research question is often stimulated by real-life observations, experiences, or events in the researcher’s local setting that reflect a perplexing problem begging for systematic inquiry. The research question begins as a problem statement or set of propositions that describe the relationship among certain concepts, behaviors, or experiences. Agee 15 and others 16,17 note that initial questions are usually too broad in focus and too vague regarding the specific context of the study to be answerable and researchable. Creswell reminds us that initial qualitative research questions guide inquiry, but they often change as the author’s understanding of the issue develops throughout the study. 16 Developing and refining a primary research question focused on both the phenomena of interest and the context in which it is situated is essential to research rigor and quality.

Glassick, Huber, and Maeroff identified six criteria applicable to assessing the quality of scholarship. 18,19 Now commonly referred to as the Glassick Criteria ( Table 1 ), these critical attributes outline the essential elements of any scholarly approach and serve as a general research framework for developing research questions and designing studies. The first two criteria, clear purpose and adequate preparation, are directly related to formulating effective research questions and a strong conceptual framework.

Glassick’s Criteria for Assessing the Quality of Scholarship of a Research Study 18

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Generating and refining a qualitative research question requires thorough, systematic, and iterative review of the literature, and the use of those results to establish a clear context and foundation for the question and study design. Using an iterative approach, relevant concepts, principles, theories or models, and prior evidence are identified to establish what is known, and more importantly, what is not known. The iterative process contributes to forming a better research question, the criteria for which can be abbreviated by the acronym FINER, ie, f easible, i nteresting, n ovel, e thical, and r elevant, that is answerable and researchable, in terms of research focus, context specificity, and the availability of time, logistics, and resources to carry out the study. Developing a FINER research question is critical to study rigor and quality and should not be rushed, as all other aspects of research design depend on the focus and clarity of the research question(s) guiding the study. 15 Agee provides clear and worthwhile additional guidance for developing qualitative research questions. 15

Reflexivity, the idea that a researcher’s preconceptions and biases can influence decisions and actions throughout qualitative research activities, is a critical aspect of rigor even at the earliest stages of the study. A researcher’s background, beliefs, and experiences may affect any aspect of the research from choosing which specific question to investigate through determining how to present the results. Therefore, even at this early stage, the potential effect of researcher bias and any ethical considerations should be acknowledged and addressed. That is, how will the question’s influence on study design affect participants’ lives, position the researcher in relationship with others, or require specific methods for addressing potential areas of research bias and ethical considerations?

A conceptual framework is then actively constructed to provide a logical and convincing argument for the research. The framework defines and justifies the research question, the methodology selected to answer that question, and the perspectives from which interpretation of results and conclusions will be made. 5,6,20 Developing a well-integrated conceptual framework is essential to establishing a research topic based upon a thorough and integrated review of relevant literature (addressing Glassick criteria #1 and #2: clear purpose and adequate preparation). Key concepts, principles, assumptions, best practices, and theories are identified, defined, and integrated in ways that clearly demonstrate the problem statement and corresponding research question are answerable, researchable, and important to advancing thinking and practice.

Ringsted, Hodges, and Sherpbier describe three essential parts to an effective conceptual framework: theories and/or concepts and principles relevant to the phenomenon of interest; what is known and unknown from prior work, observations, and examples; and the researcher’s observations, ideas, and suppositions regarding the research problem statement and question. 21 Lingard describes four types of unknowns to pursue during literature review: what no one knows; what is not yet well understood; what controversy or conflicting results, understandings, or perspectives exist; and what are unproven assumptions. 22 In qualitative research, these unknowns are critical to achieving a well-developed conceptual framework and a corresponding rigorous study design.

Recent contributions from Ravitch and colleagues present best practices in developing frameworks for conceptual and methodological coherence within a study design, regardless of the research approach. 23,24 Their recommendations and arguments are highly relevant to qualitative research. Figure 1 reflects the primary components of a conceptual framework adapted from Ravitch and Carl 23 and how all components contribute to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice. Notice that each element of the framework interacts with and influences other elements in a dynamic and interactive process from the beginning to the end of a research project. The intersecting bidirectional arrows represent direct relationships between elements as they relate to specific aspects of a qualitative research study.

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Adaptation of Ravitch and Carl’s Components of a Conceptual Framework 23

Maxwell also provides useful guidance for developing an effective conceptual framework specific to the qualitative research paradigm. 17 The 2015 second edition of the Review Criteria for Research Manuscripts 14 and work by Ravitch and colleagues 23,24 provide specific guidance for applying the conceptual framework to each stage of the research process to enhance rigor and quality. Quality criteria for assessing a study’s problem statement, conceptual framework, and research question include the following: introduction builds a logical case and provides context for the problem statement; problem statement is clear and well-articulated; conceptual framework is explicit and justified; research purpose and/or question is clearly stated; and constructs being investigated are clearly identified and presented. 14,24,25 As best practice guidelines, these criteria facilitate quality and rigor while providing sufficient flexibility in how each is achieved and demonstrated.

While a conceptual framework is important to rigor in qualitative research, Huberman and Miles caution qualitative researchers about developing and using a framework to the extent that it influences qualitative design deductively because this would violate the very principles of induction that define the qualitative research paradigm. 25 Our profession’s recent emphasis on a holistic admissions process for pharmacy students provides a reasonable example of inductive and deductive reasoning and their respective applications in qualitative and quantitative research studies. Principles of inductive reasoning are applied when a qualitative research study examines a representative group of competent pharmacy professionals to generate a theory about essential cognitive and affective skills for patient-centered care. Deductive reasoning could then be applied to design a hypothesis-driven prospective study that compares the outcomes of two cohorts of students, one group admitted using traditional criteria and one admitted based on a holistic admissions process revised to value the affective skills of applicants. Essentially, the qualitative researcher must carefully generate a conceptual framework that guides the research question and study design without allowing the conceptual framework to become so rigid as to dictate a testable hypothesis, which is the founding principle of deductive reasoning. 26

Step 2: Qualitative Study Design

The development of a strong conceptual framework facilitates selection of appropriate study methods to minimize the bias inherent in qualitative studies and help readers to trust the research and the researcher (see Glassick criteria #3 in Table 1 ). Although researchers can employ great flexibility in the selection of study methods, inclusion of best practice methods for assuring the rigor and trustworthiness of results is critical to study design. Lincoln and Guba outline four criteria for establishing the overall trustworthiness of qualitative research results: credibility, the researcher ensures and imparts to the reader supporting evidence that the results accurately represent what was studied; transferability, the researcher provides detailed contextual information such that readers can determine whether the results are applicable to their or other situations; dependability, the researcher describes the study process in sufficient detail that the work could be repeated; confirmability, the researcher ensures and communicates to the reader that the results are based on and reflective of the information gathered from the participants and not the interpretations or bias of the researcher. 27

Specific best practice methods used in the sampling and data collection processes to increase the rigor and trustworthiness of qualitative research include: clear rationale for sampling design decisions, determination of data saturation, ethics in research design, member checking, prolonged engagement with and persistent observation of study participants, and triangulation of data sources. 28

Qualitative research is focused on making sense of lived, observed phenomenon in a specific context with specifically selected individuals, rather than attempting to generalize from sample to population. Therefore, sampling design in qualitative research is not random but defined purposively to include the most appropriate participants in the most appropriate context for answering the research question. Qualitative researchers recognize that certain participants are more likely to be “rich” with data or insight than others, and therefore, more relevant and useful in achieving the research purpose and answering the question at hand. The conceptual framework contributes directly to determining sample definitions, size, and recruitment of participants. A typical best practice is purposive sampling methods, and when appropriate, convenience sampling may be justified. 29

Purposive sampling reflects intentional selection of research participants to optimize data sources for answering the research question. For example, the research question may be best answered by persons who have particular experience (critical case sampling) or certain expertise (key informant sampling). Similarly, additional participants may be referred for participation by active participants (snowball sampling) or may be selected to represent either similar or opposing viewpoints (confirming or disconfirming samples). Again, the process of developing and using a strong conceptual framework to guide and justify methodological decisions, in this case defining and establishing the study sample, is critical to rigor and quality. 30 Convenience sampling, using the most accessible research participants, is the least rigorous approach to defining a study sample and may result in low accuracy, poor representativeness, low credibility, and lack of transferability of study results.

Qualitative studies typically reflect designs in which data collection and analysis are done concurrently, with results of ongoing analysis informing continuing data collection. Determination of a final sample size is largely based on having sufficient opportunity to collect relevant data until new information is no longer emerging from data collection, new coding is not feasible, and/or no new themes are emerging; that is, reaching data saturation , a common standard of rigor for data collection in qualitative studies . Thus, accurately predicting a sample size during the planning phases of qualitative research can be challenging. 30 Care should be taken that sufficient quantity (think thick description) and quality (think rich description) of data have been collected prior to concluding that data saturation has been achieved. A poor decision regarding sample size is a direct consequence of sampling strategy and quality of data generated, which leaves the researcher unable to fully answer the research question in sufficient depth. 30

Though data saturation is probably the most common terminology used to describe the achievement of sufficient sample size, it does not apply to all study designs. For example, one could argue that in some approaches to qualitative research, data collection could continue infinitely if the event continues infinitely. In education, we often anecdotally observe variations in the personality and structure of a class of students, and as generations of students continue to evolve with time, so too would the data generated from observing each successive class. In such situations, data saturation might never be achieved. Conversely, the number of participants available for inclusion in a sample may be small and some risk of not reaching data saturation may be unavoidable. Thus, the idea of fully achieving data saturation may be unrealistic when applied to some populations or research questions. In other instances, attrition and factors related to time and resources may contribute to not reaching data saturation within the limits of the study. By being transparent in the process and reporting of results when saturation may not have been possible, the resulting data may still contribute to the field and to further inquiry. Replication of the study using other samples and conducting additional types of follow-up studies are other options for better understanding the research phenomenon at hand. 31

In addition to defining the sample and selecting participants, other considerations related to sampling bias may impact the quantity and quality of data generated and therefore the quality of the study result. These include: methods of recruiting, procedures for informed consent, timing of the interviews in relation to experience or emotion, procedures for ensuring participant anonymity/confidentiality, interview setting, and methods of recording/transcribing the data. Any of these factors could potentially change the nature of the relationship between the researcher and the study participants and influence the trustworthiness of data collected or the study result. Thus, ongoing application of previously mentioned researcher reflexivity is critical to the rigor of the study and quality of sampling. 29,30

Common qualitative data collection methods used in health professions education include interview, direct observation methods, and textual/document analysis. Given the unique and often highly sensitive nature of data being collected by the researcher, trustworthiness is an essential component of the researcher-participant relationship. Ethical conduct refers to how moral principles and values are part of the research process. Participants’ perceptions of ethical conduct are fundamental to a relationship likely to generate high quality data. During each step of the research process, care must be taken to protect the confidentiality of participants and shield them from harm relating to issues of respect and dignity. Researchers must be respectful of the participants’ contributions and quotes, and results must be reported truthfully and honestly. 8

Interview methods range from highly structured to increase dependability or completely open-ended to allow for interviewers to clarify a participant’s response for increased credibility and confirmability. Regardless, interview protocols and structure are often modified or refined, based on concurrent data collection and analysis processes to support or refute preliminary interpretations and refine focus and continuing inquiry. Researcher reflexivity, or acknowledgement of researcher bias, is absolutely critical to the credibility and trustworthiness of data collection and analysis in such study designs. 32

Interviews should be recorded and transcribed verbatim prior to coding and analysis. 28 Member checking, a common standard of rigor, is a practice to increase study credibility and confirmability that involves asking a research subject to verify the transcription of an interview. 1,16,28 The research subject is asked to verify the completeness and accuracy of an interview transcript to ensure the transcript truthfully reflects the meaning and intent of the subject’s contribution.

Prolonged engagement involves the researcher gaining familiarity and understanding of the culture and context surrounding the persons or situations being studied. This strategy supports reflexivity, allowing the researcher to determine how they themselves may be a source of bias during the data collection process by altering the nature of how individuals behave or interact with others in the presence of the researcher. Facial expressions, spoken language, body language, style of dress, age, race, gender, social status, culture, and the researcher’s relationship with the participants may potentially influence either participants’ responses or how the researcher interprets those responses. 33 “Fitting in” by demonstrating an appreciation and understanding of the cultural norms of the population being studied potentially allows the researcher to obtain more open and honest responses from participants. However, if the research participants or topic are too familiar or personal, this may also influence data collection or analysis and interpretation of the results. 33 The possible applications of this section to faculty research with student participants in the context of pharmacy education are obvious, and researcher reflexivity is critical to rigor.

Some researchers using observational methods adopt a strategy of direct field observation, while others play partial or full participant roles in the activity being observed. In both observation scenarios, it is impossible to separate the researcher from the environment, and researcher reflexivity is essential. The pros and cons of observation approach, relative to the research question and study purpose, should be evaluated by the researcher, and the justification for the observational strategy selected should be made clear. 34 Regardless of the researcher’s degree of visibility to the study participants, persistent observation of the targeted sample is critical to the confirmability standard and to achieving data saturation. That is, study conclusions must be clearly grounded in persistent phenomena witnessed during the study, rather than on a fluke event. 28

Researchers acknowledge that observational methodologies are limited by the reality that the researcher carries a bias in determining what is observed, what is recorded, how it is recorded, and how it is transcribed for analysis. A study’s conceptual framework is critical to achieving rigor and quality and provides guidance in developing predetermined notions or plans for what to observe, how to record, and how to minimize the influence of potential bias. 34 Researcher notes should be recorded as soon as possible after the observation event to optimize accuracy. The more detailed and complete the notes, the more accurate and useful they can be in data analysis or in auditing processes for enhancing rigor in the interpretation phase of the study. 34

Triangulation is among the common standards of rigor applied within the qualitative research paradigm. Data triangulation is used to identify convergence of data obtained through multiple data sources and methods (eg, observation field notes and interview transcripts) to avoid or minimize error or bias and optimize accuracy in data collection and analysis processes. 33,35,36

Again, researcher practice in reflexivity throughout research processes is integral to rigor in study design and implementation. Researchers must demonstrate attention to appropriate methods and reflective critique, which are represented in both core elements of the conceptual framework ( Figure 1 ) and Glassick criteria ( Table 1 ). In so doing, the researcher will be well-prepared to justify sampling design and data collection decisions to manuscript reviewers and, ultimately, readers.

Step 3: Data Analysis

In many qualitative studies, data collection runs concurrently with data analysis. Specific standards of rigor are commonly used to ensure trustworthiness and integrity within the data analysis process, including use of computer software, peer review, audit trail, triangulation, and negative case analysis.

Management and analyses of qualitative data from written text, observational field notes, and interview transcriptions may be accomplished using manual methods or the assistance of computer software applications for coding and analysis. When managing very large data sets or complex study designs, computer software can be very helpful to assist researchers in coding, sorting, organizing, and weighting data elements. Software applications can facilitate ease in calculating semi-quantitative descriptive statistics, such as counts of specific events, that can be used as evidence that the researcher’s analysis is based on a representative majority of data collected ( inclusivism ) rather than focusing on selected rarities ( anecdotalism ). Using software to code data can also make it easier to identify deviant cases, detect coding errors, and estimate interrater reliability among multiple coders. 37 While such software helps to manage data, the actual analyses and interpretation still reside with the researcher.

Peer review, another common standard of rigor, is a process by which researchers invite an independent third-party researcher to analyze a detailed audit trail maintained by the study author. The audit trail methodically describes the step-by-step processes and decision-making throughout the study. Review of this audit trail occurs prior to manuscript development and enhances study confirmability. 1,16 The peer reviewer offers a critique of the study methods and validation of the conclusions drawn by the author as a thorough check on researcher bias.

Triangulation also plays a role in data analysis, as the term can also be used to describe how multiple sources of data can be used to confirm or refute interpretation, assertions, themes, and study conclusions. If a theme or theory can be arrived at and validated using multiple sources of data, the result of the study has greater credibility and confirmability. 16,33,36 Should any competing or controversial theories emerge during data collection or analysis, it is vital to the credibility and trustworthiness of the study that the author disclose and explore those negative cases. Negative case analysis refers to actively seeking out and scrutinizing data that do not fit or support the researcher’s interpretation of the data. 16

The use of best practices applying to data collection and data analysis facilitates the full examination of data relative to the study purpose and research question and helps to prevent premature closure of the study. Rather than stopping at the initial identification of literal, first-level assertion statements and themes, authors must progress to interpreting how results relate to, revise, or expand the conceptual framework, or offer an improved theory or model for explaining the study phenomenon of interest. Closing the loop on data collection is critical and is achieved when thorough and valid analysis can be linked back to the conceptual framework, as addressed in the next section.

Step 4: Drawing Valid Conclusions

Lingard and Kennedy 38 succinctly state that the purpose of qualitative research is to deepen one’s understanding of specific perspectives, observations, experiences, or events evidenced through the behaviors or products of individuals and groups as they are situated in specific contexts or circumstances. Conclusions generated from study results should enhance the conceptual framework, or contribute to a new theory or model development, and are most often situated within the discussion and conclusion sections of a manuscript.

The discussion section should include interpretation of the results and recommendations for practice. Interpretations should go beyond first-level results or literal description of observed behaviors, patterns, and themes from analysis. The author’s challenge is to provide a complete and thorough examination and explanation of how specific results relate to each other, contribute to answering the research question, and achieve the primary purpose of the research endeavor. The discussion should “close the loop” by integrating study results and analysis with the original conceptual framework. The discussion section should also provide a parsimonious narrative or graphical explanation and interpretation of study results that enhances understanding of the targeted phenomena.

The conclusion section should provide an overall picture or synopsis of the study, including its important and unique contributions to the field from the perspective of both conceptual and practical significance. The conclusion should also include personal and theoretical perspectives and future directions for research. Together, the discussion and conclusion should include responses to the larger questions of the study’s contributions, such as: So what? Why do these results matter? What next?

The strength of conclusions is dependent upon the extent to which standards of rigor and best practices were demonstrated in design, data collection, data analysis, and interpretation, as described in previous sections of this article. 4,12,17,23,24 Quality and rigor expectations for drawing valid conclusions and generating new theories are reflected in the following essential features of rigor and quality, which include: “Close the loop” to clearly link research questions, study design, data collection and analysis, and interpretation of results. Reflect effective integration of the study results with the conceptual framework and explain results in ways that relate, support, elaborate, and/or challenge conclusions of prior scholarship. Descriptions of new or enhanced frameworks or models are clear and effectively grounded in the study results and conclusions. Practical or theoretical implications are effectively discussed, including guidance for future studies. Limitations and issues of reflexivity and ethics are clearly and explicitly described, including references to actions taken to address these areas. 3,4,12,14

Step 5: Reporting Research Results

Key to quality reporting of qualitative research results are clarity, organization, completeness, accuracy, and conciseness in communicating the results to the reader of the research manuscript. O’Brien and others 4 proposed a standardized framework specifically for reporting qualitative studies known as the Standards for Reporting Qualitative Research (SRQR, Table 2 ). This framework provides detailed explanations of what should be reported in each of 21 sections of a qualitative research manuscript. While the SRQR does not explicitly mention a conceptual framework, the descriptions and table footnote clarification for the introduction and problem statement reflect the essential elements and focus of a conceptual framework. Ultimately, readers of published work determine levels of credibility, trustworthiness, and the like. A manuscript reviewer, the first reader of a study report, has the responsibility and privilege of providing critique and guidance to authors regarding achievement of quality criteria, execution and reporting of standards of rigor, and the extent to which meaningful contributions to thinking and practice in the field are presented. 13,39

An Adaptation of the 21 Elements of O’Brien and Colleagues’ Standards for Reporting Qualitative Research (SRQR) 4

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Authors must avoid language heavy with connotations or adjectives that insert the researcher’s opinion into the database or manuscript. 14,40 The researcher should be as neutral and objective as possible in interpreting data and in presenting results. Thick and rich descriptions, where robust descriptive language is used to provide sufficient contextual information, enable the reader to determine credibility, transferability, dependability, and confirmability .

The process of demonstrating the credibility of research is rooted in honest and transparent reporting of how biases and other possible confounders were identified and addressed throughout study processes. Such reporting, first described within the study’s conceptual framework, should be revisited in reporting the work. Confounders may include the researcher’s training and previous experiences, personal connections to the background theory, access to the study population, and funding sources. These elements and processes are best represented in Glassick’s criteria for effective presentation and reflective critique ( Table 1 , criteria 5 and 6). Transferability is communicated, in part, through description of sampling factors such as: geographical location of the study, number and characteristics of participants, and the timeframe of data collection and analysis. 40 Such descriptions also contribute to the credibility of the results and readers’ determination of transfer to their and other contexts. To ensure dependability, the research method must be reported in detail such that the reader can determine proper research practices have been followed and that future researchers can repeat the study. 40 The confirmability of the results is influenced by reducing or at a minimum explaining any researcher influence on the result by applying and meeting standards of rigor such as member checking, triangulation, and peer review. 29,33

In qualitative studies, the researcher is often the primary instrument for data collection. Any researcher biases not adequately addressed or errors in judgement can affect the quality of data and subsequent research results. 33 Thus, due to the creative interpretative and contextually bound nature of qualitative studies, the application of standards of rigor and adherence to systematic processes well-documented in an audit trail are essential. The application of rigor and quality criteria extend beyond the researcher and are also important to effective peer review processes within a study and for scholarly dissemination. The goal of rigor in qualitative research can be described as ensuring that the research design, method, and conclusions are explicit, public, replicable, open to critique, and free of bias. 41 Rigor in the research process and results are achieved when each element of study methodology is systematic and transparent through complete, methodical, and accurate reporting. 33 Beginning the study with a well-developed conceptual framework and active use of both researcher reflexivity and rigorous peer review during study implementation can drive both study rigor and quality.

As the number of published qualitative studies in health professions educational research increases, it is important for our community of health care educators to keep in mind the unique aspects of rigor in qualitative studies presented here. Qualitative researchers should select and apply any of the above referenced study methods and research practices, as appropriate to the research question, to achieve rigor and quality. As in any research paradigm, the goal of quality and rigor in qualitative research is to minimize the risk of bias and maximize the accuracy and credibility of research results. Rigor is best achieved through thoughtful and deliberate planning, diligent and ongoing application of researcher reflexivity, and honest communication between the researcher and the audience regarding the study and its results.

Reliability vs. Validity in Research: The Essence of Credible Research

Table of contents

1 Understanding Reliability in Research
2 Understanding Validity in Research
3 Key Differences Between Reliability and Validity
4 The Role of Reliability and Validity in Research Design
5.1 Ensuring Reliability
5.2 Ensuring Validity
5.3 Considerations for Specific Research Methods
6 Ensuring Excellence in Research Through Meticulous Methodology

The concepts of reliability and validity play pivotal roles in ensuring the integrity and credibility of research findings. These foundational principles are crucial for researchers aiming to produce work that contributes to their field and withstands scrutiny. Understanding the interplay between reliability vs validity in research is essential for any rigorous investigation.

The main points of our article include:

A detailed exploration of the concept of reliability, including its types and how it is assessed.
An in-depth look at validity, discussing its various forms and the methods used to evaluate it.
The relationship between reliability and validity, and why both are essential for the credibility of research.
Practical examples illustrating the application of reliability and validity in different research contexts.
Strategies for enhancing both reliability and validity in research studies.

This understanding sets the stage for a more in-depth look at these important parts of the methodology. By explaining these ideas in more detail, we can have a deeper discussion about how to use and evaluate them successfully in different research settings. For better understanding and faster coming with your research, students can look at sites like PapersOwl to get more information and help with defining these two concepts.

Understanding Reliability in Research

So, at first, let’s start with the reliability definition.

It measures how stable and consistent the results of a research tool are across a number of tests and conditions. It tells us how reliable the data we collected is, which, in turn, is important for making sure that the study results are valid.

There are several types of reliability crucial for assessing the quality of research instruments:

Test-retest reliability evaluates the consistency of results when the same test is administered to the same participants under similar conditions at two different points in time.
Inter-rater reliability measures the extent to which different observers or raters agree in their assessments, ensuring that the data collection process is unbiased and consistent across individuals.
Parallel-forms reliability involves comparing the results of two different but equivalent versions of a test to the same group of individuals, assessing the consistency of the scores.
Internal consistency reliability assesses the homogeneity of items within a test, ensuring that all parts contribute equally to what is being measured. This is closely tied to the concept of criterion validity, which evaluates how well one measure predicts an outcome based on other measures.

Methods for measuring and improving reliability include statistical techniques such as Cronbach’s alpha for internal consistency reliability, as well as ensuring standardized testing conditions and thorough training for raters to enhance inter-rater reliability.

Examples of reliability in research can be seen in educational assessments (test-retest reliability), psychological evaluations (internal consistency reliability), and health studies (inter-rater reliability).

Each context underscores the importance of reliable measurement as a precursor to assessing content validity (the extent to which a test measures all aspects of the desired content) and construct validity (the degree to which a test accurately measures the theoretical construct it is intended to measure). Both content validity and construct validity are essential components of overall validity, which refers to the accuracy of the research findings.

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Understanding Validity in Research

In research, validity is a measure of accuracy that indicates how well a method or test measures what it is designed to assess. High validity is indicative of results that closely correspond to actual characteristics, behaviors, or phenomena in the physical or social world, making it a critical aspect of any credible research endeavor.

Types of validity include:

Content validity, which ensures that a test comprehensively covers all aspects of the subject it aims to measure.
Criterion-related validity, which is divided into predictive validity (how well a test predicts future outcomes) and concurrent validity (how well a test correlates with established measures at the same time).
Construct validity, further broken down into convergent validity (how closely a new test aligns with existing tests of the same constructs) and discriminant validity (how well the test distinguishes between different constructs).
Face validity, a more subjective measure of how relevant a test appears to be at face value, without delving into its technical merits.

Validity can be measured and ensured in a number of ways, such as through expert evaluations or by doing statistical calculations. They focus on how well the test or method matches up with theoretical expectations and set standards. Validity requires careful data collection, careful test design, and regular checks to see if the test is still relevant and accurately showing the intended constructs.

Examples of validity in research are abundant and varied. In educational testing, content validity is assessed to ensure that exams or assessments fully represent the curriculum they aim to measure. In psychology, convergent validity is demonstrated when different tests of the same psychological construct yield similar results, while predictive validity might be observed in employment settings where a cognitive test predicts job performance. Each of these examples showcases how validity is assessed and achieved, highlighting its role in producing meaningful and accurate research outcomes.

Key Differences Between Reliability and Validity

The key differences between reliability and validity lie in their focus and implication in research. Reliability concerns the consistency of a measurement tool, ensuring that the same measurement is obtained across different instances of its application. For instance, interrater reliability ensures consistency in observations made by different scholars. Validity, on the other hand, assesses whether the research tool accurately measures what it is intended to, aligning with established theories and meeting the research objectives. While reliability is about the repeatability of the same measurement, validity dives deeper into the accuracy and appropriateness of what is being measured, ensuring it reflects the intended constructs or realities.

The Role of Reliability and Validity in Research Design

The incorporation of reliability and validity assessments in the early stages of research design is paramount for ensuring the credibility and applicability of research outcomes. By prioritizing these evaluations from the outset, research writers can design studies that accurately reflect and measure the phenomena of interest, leading to more trustworthy and meaningful findings.

Strategies for integrating reliability and validity checks throughout the research process include the use of established statistical methods and the continuous evaluation of research measures. For instance, employing factor analysis can help in identifying the underlying structure of data, thus aiding in the assessment of construct validity. Similarly, calculating Cronbach’s alpha can ensure the internal consistency of items within a survey, contributing to the overall reliability of the research measures.

Case studies across various disciplines underscore the critical role of reliability and validity in shaping research outcomes and influencing subsequent decisions. For example, in clinical psychology research, the use of validated instruments to assess patient symptoms ensures that the measures accurately capture the constructs of interest, such as depression or anxiety levels, which in turn supports the internal validity of the study. In the field of education, ensuring the interrater reliability of grading rubrics can lead to fairer and more consistent assessments of student performance.

Moreover, the application of rigorous statistical methods not only enhances the reliability and validity of the research but also strengthens the study’s foundation, making the findings more compelling and actionable. By systematically integrating these checks, researchers can avoid common pitfalls such as measurement errors or biases, thereby ensuring that their studies contribute valuable insights to the body of knowledge.

Challenges and Considerations in Ensuring Reliability and Validity

Ensuring reliability and validity in research is crucial for the credibility and applicability of research results. These principles guide how to design studies, collect data, and interpret findings to ensure that their work accurately reflects the underlying constructs they aim to explore.

Ensuring Reliability

To ensure reliability, researchers must focus on creating consistent, repeatable conditions and employing precise measurement tools. The test-retest correlation is a fundamental method where writers administer the same test to the same subjects under similar conditions at two different times. A high correlation between the two sets of results indicates strong reliability.

For example, in a study measuring the stress levels of first responders, using the same stress assessment tool at different intervals can validate the tool’s reliability through consistent scores.

Another strategy is ensuring reliable measurement through inter-rater reliability, where multiple observers assess the same concept to verify consistency in observations. In environmental science, when studying the impact of pollution on local ecosystems, different researchers might assess the same water samples for contaminants. The consistency of their measurements confirms the reliability of the methods used.

Ensuring Validity

Ensuring validity involves verifying that the research accurately measures the intended concept. This can be achieved through careful questioning in research formulation, selecting valid measurement instruments, and employing appropriate statistical analyses.

For instance, when studying the effectiveness of a new educational curriculum, researchers might use standardized test scores to measure student learning outcomes. This approach ensures that the tests are a valid measurement of the educational objectives the curriculum aims to achieve.

Construct validity can be enhanced through factor analysis, which helps in identifying whether the collected data truly represent the underlying construct of interest. In health research, exploring the validity of a new diagnostic tool for a specific disease involves comparing its results with those from established diagnostic methods, ensuring that the new tool accurately identifies the disease it claims to measure.

Considerations for Specific Research Methods

Different research methods, such as qualitative vs. quantitative research, require distinct approaches to ensure validity and reliability. In qualitative research, ensuring external validity involves a detailed and transparent description of the research setting and context, allowing others to assess the applicability of the findings to similar contexts.

For instance, in-depth interviews exploring patients’ experiences with chronic pain provide rich, contextual insights that might not be generalizable without a clear articulation of the setting and participant characteristics.

In quantitative research, ensuring the validity and reliability of collecting data often involves statistical validation methods and reliability tests, such as Cronbach’s alpha for internal consistency.

Ensuring Excellence in Research Through Meticulous Methodology

In summary, the fundamental takeaways from this article highlight the paramount importance of ensuring high reliability and validity in conducting research. These principles are not merely academic considerations but are crucial for the integrity and applicability of research findings. The accuracy of research instruments, the consistency of test scores, and the thoughtful design of the methods section of a research paper are all critical to achieving these goals. For researchers aiming to enhance the credibility of their work, focusing on these aspects from the outset is key. Additionally, seeking help with research can provide valuable insights and support in navigating the complexities of research design, ensuring that studies not only adhere to the highest standards of reliability and validity but also contribute meaningful knowledge to their respective fields.

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New research shows laser-assisted firing improves TOPCon solar cell reliability

Chinese manufacturer Jolywood is currently applying a laser-assisted firing process in TOPCon solar cell manufacturing that can reportedly increase contact quality and corrosion resistance, while also reducing production costs. Scientists at the University of South Wales have investigated the impact of this production process on the quality of TOPCon cells and have found it “significantly” improve their reliability.

Modules & Upstream Manufacturing

Image: University of New South Wales, Solar Energy Materials and Solar Cells, Creative Commons License CC BY 4.0

A group of researchers from China's module manufacturer Jolywood and the University of New South Wales (UNSW) in Australia have analyzed the impact of a new laser-assisted firing technology developed by the Chinese manufacturer itself for the production of tunnel oxide passivated contact (TOPCon) solar cells.

Called Jolywood Special Injected Metallization (JSIM), the new technique consists of a laser-assisted firing process that utilizes a customized silver (Ag) paste for the front contact formation on TOPCon solar cells. It is a low-temperature firing technique that is intended to facilitate paste penetration through the cell front anti-reflection coating.

“The JSIM technology is already in high-volume production by Jolywood and other companies are developing and implementing their own versions of laser-assisted firing as well,” the research's lead author, Bram Hoex, told pv magazine . “Jolywood was one of the first manufacturers to ramp up TOPCon technology in high-volume production.”

In the paper “ Enhancing the reliability of TOPCon technology by laser-enhanced contact firing ,” published in Solar Energy Materials and Solar Cells , Hoex and his colleagues explained that they used contaminant-induced accelerated DH85 testing to assess the effectiveness of the JSIM process in increasing contact quality and corrosion resistance, while also reducing production costs.

They built TOPCon cells with dimensions of 182 mm × 182 mm and based on G10 n-type Czochralski (Cz) silicon wafers and reference TOPCon devices developed via a standard front metallization process. The cells built via JSM were based on contacts made with plasma oxidation & plasma-assisted in situ-doping deposition (POPAID), which is a physical vapor deposition technique developed by Jolywood itself.

Both cell typologies went through DH85 testing a temperature of 85 C and a relative humidity of 85%. The laser operated at a wavelength of 1030 nm with a frequency of 1000 Hz.

“To evaluate the contact resistance, we specifically focused on the non-busbar regions of the TOPCon cells. Employing a FOBA M1000 scribing laser, we created 6 mm wide stripes for contact resistance assessment,” the research team explained. “In contrast, the baseline samples underwent front metallization used standard commercial Ag/Al paste and a conventional firing process. Notably, both batches of TOPCon cells featured identical screen-printing pattern designs.”

The academics used a Zeiss 550 Crossbeam cryo-focused ion-beam scanning electron microscope to analyze cross-sectional images of the metal contacts. They found the JSIM solar cell achieved an average power conversion efficiency of 25.1% and a fill factor of 83.2% while the reference cell reached values of 25.0% and 82.9%, respectively.

Emiliano Bellini

India surpasses 75 GW of installed solar capacity

New report tips 60,000 jobs from australian pv manufacturing industry, related content, elsewhere on pv magazine..., leave a reply cancel reply.

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Will OJ Simpson's family donate his brain for CTE research? Fatal brain disease explained

Following oj simpson's death due to prostate cancer, social media users and his fans called for the nfl star's brain to be examined for cte..

Following OJ Simpson's death due to prostate cancer, social media users and his fans called for the NFL star's brain to be examined for Chronic Traumatic Encephalopathy or CTE. It is a degenerative brain disease that has been diagnosed in several deceased former football players.

Following Simpson's passing, netizens have now speculated about the possibility of testing his brain.(AFP)

What is CTE and is it detectable after death?

Some people, including athletes, who have experienced traumatic brain injuries may develop CTE. The fatal brain disease evolves as an outcome of recurrent brain traumas that contribute to an accumulation of aberrant tau protein, which may impair neuronal function.

CTE patients show similar symptoms seen in those suffering from dementia, including memory loss, depression, anger and confusion.

Notably, CTE is only detectable after death. But due to the increased awareness about the disease, several football players have consented to posthumously donate their brains for scientific study.

Following Simpson's passing, netizens have now speculated about the possibility of testing his brain.

"'In all seriousness I really hope OJ Simpson's family has his brain checked for signs of CTE, out of respect for the Brown and Goldman families, even for his own kids," one X user wrote.

"I hope OJ Simpson gave consent to allow his brain to be studied to see if he had CTE. My condolences to his children," another added.

Also Read: O.J. Simpson and the Kardashians’s complex relationship explained: ‘Beloved uncle to trial trauma’

OJ Simpson's brain will not be tested

Meanwhile, NY POST reported that Simpson's brain will be incinerated and not tested.

According to a representative for his estate, Simpson's body is set to be cremated on Tuesday in Las Vegas . Despite repeated requests from experts, there are no intentions of providing his brain for research.

Malcolm LaVergne, Simpson's longtime attorney and executor, said he has already approved all the documentation for the late football player's cremation, and the family gave a “hard no” to experts seeking to study Simpson’s brain to identify if he had CTE.

According to the attorney, his death certificate and other documentation related to cremation have also been signed by a medical expert, and more paperwork is expected to be completed on Monday.

“Tuesday is the predicted . . . day that he will actually be cremated.” LaVergne stated. “That’s what OJ wanted. Those are OJ’s wishes, and that’s what the kids are telling me.”

Also Read : O.J. Simpson’s defence was a harbinger of post-truth politics

Did Simpson show any sign of CTE?

Simpson sometimes asked where he was during his jail term in Nevada prison, claimed a retired guard.

As Simpson spent nine years at Lovelock Correctional Facility, Jeffrey Felix, a staff member, told The Post that Simpson "would wake up in the morning wondering what [his] tee time was for golf, and he's in a prison."

He described Simpson as “very forgetful” and suggested that he had CTE, adding that he frequently missed his medication's dosage and suppertime at the jail.

Anxiety, memory loss, and poor judgment are all signs of chronic traumatic encephalopathy.

Earlier, Simpson told The Buffalo News that he feels alright, "but I have days when I can't... I lose words, and I can't come up with a simple word. I can't remember a phone number, so forget that."

Simpson's family announced on Thursday that the former NFL player, who was acquitted of killing his former wife and her friend Ron Goldman, passed away at the age of 76.

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Best SIM Cards for Thailand in 2024

Thailand is an incredibly popular travel destination, known for its gorgeous beaches, lively cities, amazing food, and rich culture. However, to truly make the most of your trip there, having constant access to data on your phone is essential.

That’s why choosing the right SIM card for Thailand is so important. With the perfect SIM card, you’ll be able to use maps and transportation apps to get around, look up reviews and directions on the fly, share photos of your trip instantly, and stay connected in case of emergency.

I recently spent a month backpacking around Thailand using multiple SIM cards. Based on my firsthand experience, I’ll compare the best SIM card options for getting data in Thailand in 2024. Whether you need a SIM for a quick holiday or an extended stay, this guide has got you covered!

Quick Overview: Best SIM Card for Thailand in 2024

Before jumping into the details, here is a quick look at my top recommendations:

Best Overall: AIS SIM Card – Excellent connectivity powered by Thailand’s best mobile network
Best Budget Pick: DTAC SIM Card – Affordable SIM perfect for shorter trips
Best eSIM: Airalo eSIM – Convenient all-digital SIM you can set up before your trip
Best Physical SIM for Long Trips: SimOptions Thailand SIM – SIM card delivered to your home before traveling

Keep reading for the full scoop on which SIM card is perfect for your upcoming Thailand vacation or move!

Do You Really Need a Thailand SIM Card?

You might be wondering — do I even need a special SIM card for Thailand?

Fair question! After all, Thailand does have pretty solid WiFi coverage. Most hotels, cafes, restaurants, and public areas have decent free WiFi.

However, I would still highly recommend getting a Thailand SIM card during your trip. Here are some of the key benefits:

Use Maps and Apps on the Go

The main reason I always use a local SIM when traveling is to have data access wherever I go.

Even though WiFi is common in Thailand, it’s not everywhere . And I don’t want to have to constantly hunt down the nearest cafe just to load Google Maps or order a rideshare.

With mobile data, I can simply pull out my phone and get directions or book a Grab taxi anytime, anywhere. It makes navigating and transportation so much smoother.

Of course, having a SIM card also allows you to use any apps you want while on buses, trains, taxis or boats getting around Thailand. WiFi just isn’t reliable during transit.

Stay Connected in Emergencies

While I hope your trip goes perfectly smoothly, stuff happens sometimes.

If you lose your travel buddy in the night market, need to call your accommodations, or have any other issues pop up, having cell service can be a real lifesaver.

Relying solely on WiFi could mean finding yourself in a bind if something goes wrong and you desperately need to get in touch with someone.

Share Photos and Updates Instantly

Part of the fun of travel is being able to share real-time photos and videos with your friends, family and social media followers.

But this gets tricky when you have to continually connect to dodgy public WiFi networks. It’s much easier to instantly upload pics and posts as you take them instead of waiting to hunt down the nearest connection.

Avoid Roaming Fees and Charges

If you solely rely on roaming with your regular home SIM, you may return from your Thailand travels to find a sky-high phone bill. 😬

International roaming rates are rarely kind. Getting a local SIM helps you steer clear of exorbitant roaming fees and surprise overage charges.

For all these reasons, I highly suggest using a SIM card during your upcoming trip to the Land of Smiles.

Key Things to Consider When Choosing a Thailand SIM Card

The most important thing to keep in mind when shopping for a Thailand SIM card is your specific needs and priorities.

Here are some crucial factors to consider:

Data Amount Needed

How much mobile data will you realistically need during your trip?

If you’ll primarily rely on WiFi and only use data minimally for maps and occasional searches, just 1-4GB may do. For moderate usage including some app time, aim for 5-10GB.

For heavier usage with lots of app time, YouTube watching, Spotify streaming etc, I’d grab at least 10-15GB if not unlimited data.

Network Coverage & Carrier Reliability

Thailand has three major SIM providers: AIS, TrueMove and DTAC. They all offer prepaid tourist SIMs at varying prices.

It’s crucial to choose a carrier with excellent nationwide network coverage and reliably fast speeds. I break down how the top carriers compare on this key factor later in the guide.

Price & Plan Duration That Fits Your Trip

SIM card rates in Thailand can range quite a bit based on data amounts and how long your plan lasts. Prices often drop the longer validity you choose.

Carefully consider how long you’ll be visiting, how much data you’ll need daily, and choose an appropriately priced plan. Remember you can always top up if you go over your allowance.

Convenience of Setup & Receiving SIM

Some SIMs require filling out paperwork in person while others you can order online and simply activate.

Consider when and how you want to get your Thai SIM card – upon airport arrival or delivered ahead of time to your home? Going fully digital with an eSIM Thailand ?

I’ll cover the pros and cons of various options so you can decide what works best.

Next, let’s take a look at the best Thailand SIM card providers and plans available in 2024…

AIS SIM Card – Best Overall

I crowned AIS as the best SIM card for Thailand in 2024 thanks to their blazing-fast 5G speeds, excellent connectivity powered by the country’s top network, and competitive tourist prepaid plans.

Known as the largest and best telecom in Thailand, AIS powers its network using cutting-edge 5G technology. Their cell and data signals now reach over 98% of the population.

During my month crisscrossing Thailand, my AIS SIM delivered impressively reliable connectivity and lightning-quick speeds.

I was happy to get 5G in most tourist areas and cities plus consistent 4G/LTE signals in rural villages, on remote beaches, and while island hopping.

As Thailand’s leading carrier, AIS has the reach, capacity and technology to remain the most consistent SIM option nationwide. Though not always the cheapest, their service and network reliability make the extra baht worthwhile.

Below are the key advantages of using an AIS prepaid SIM in Thailand:

Blazing fast 5G and 4G/LTE speeds – I clocked 150+ Mbps downloads in parts of Bangkok!

Widest coverage nationwide – Consistent high-speed data signals almost everywhere you travel.

Top-rated network quality – Voted Thailand’s best mobile operator multiple years running.

8-30 day tourist SIM packages – Ample validity and data amounts to suit short holidays or longer trips.

Easy activation and APN setup – Simple to set up in minutes with their Traveller SIMs.

SIMs available for pickup at BKK airport – Order ahead then grab your AIS SIM after arrival in Bangkok.

I recommend purchasing an AIS Traveller SIM Card on Amazon before your trip. Prices start at $9 USD for starter packs that include 8-30 day validity periods, data amounts perfect for light to heavy usage plus some bonus calling credit.

If you’ll solely rely on WiFi just for occasional maps, a few GB will do. But with an AIS SIM you can comfortably stream video, music etc without blowing through your allowance too quickly.

Below are some top AIS SIM package options for Thailand trips:

Once you arrive at Suvarnabhumi Airport Bangkok, you can pick up the AIS Traveller SIM you pre-purchased. Just present the QR code Amazon emails you.

Activate your Thailand tourist SIM in minutes using AIS’s dedicated Traveller SIM app. Setup is quick and painless without paperwork or passport copies required.

For consistently fast 5G and 4G/LTE connectivity all over Thailand without expensive roaming, I suggest putting AIS at the top of your SIM list!

Nationwide 5G/4G/3G coverage
Fastest average download speeds
Quality network & data signals
Competitive 8-30 day prepaid tourist plans
Easy self-service activation
Prices slightly higher than some budget carriers
Physical SIM only (no eSIM option)

Dtac SIM Card – Best Cheap SIM for Thailand

For savvy travelers on a strict budget, DTAC SIM cards are my top value pick for getting data, calls and texts affordably in Thailand.

Although not always as fast or far-reaching as upscale rivals AIS and TrueMove, DTAC impresses considering the price you’ll pay. Despite some coverage limitations, I was happy with my DTAC SIM overall during testing.

Known for cheaper plans and promotions catered to tourists, DTAC operates a mobile network that utilizes both 4G/LTE and 5G technology.

As their name implies, blazing “happy top speeds” aren’t necessarily DTAC’s forte. But they do offerpleasantly usable mobile data at downright bargain rates for travelers on tighter budgets.

DTAC’s key advantages for frugal Thailand visitors include:

Ultra-affordable data rates starting under $5 USD

Flexible 1-30 day validity to suit short & longer vacations

Tons of discounts & promotions exclusive to foreign tourists

Special Happy Tourist SIM with freebies like data & call time

Easy self-activation through DTAC website or app

For a well-priced SIM particularly suitable to mild data users who stick mostly to WiFi, you can’t beat DTAC. I especially recommend them for shorter Thailand trips under 2 weeks.

Just don’t expect amazing speeds if you plan to stream HD video nonstop or download huge files! As long as your usage stays modest, DTAC makes an awesome penny-pinching choice.

Check out some excellent DTAC SIM deals for Thailand visitors:

Prices listed include DTAC’s special discounted tourist rates. I’d suggest the 8 day plan for shorter trips under 2 weeks. Go 15 or 30 days for longer stays depending on your data needs.

Unlimited is always nice but remember, DTAC’s “Top Speeds” data is capped around 10-25Mbps for 4G/LTE.

You can conveniently order DTAC’s Happy Tourist SIM Card on Amazon here and activate easily through their website or app once in Thailand.

Overall for bargain hunters who mainly use WiFi and want an affordable Thailand SIM just for maps, light browsing and occasional app use, DTAC is a fantastic money-saving choice!

Ultra-budget-friendly prices & deals
1-30 day validity periods
Easy convenient activation
Special discounted tourist plans
Maximum speeds limited
Patchy rural coverage in some regions

Airalo eSIM – Best Digital Thailand SIM Card

If going fully digital with an eSIM sounds awesome, Airalo is hands-down my top recommendation for Thailand trips starting at just $9.

Forget fumbling with physical SIM cards and angel-hair-thin nano trays — Airalo lets you setup cellular data in minutes right on your phone. 📱

As long as your device supports eSIM compatibility, simply use the Airalo app to scan a QR activation code. Seconds later, you’re connected! No stores, no paperwork required.💪

I’m thrilled Airalo expanded eSIM support to Thailand because it really simplifies getting online abroad. Their plans are also reasonably priced with ample included high-speed data.

Some key advantages of choosing Airalo’s Thailand eSIM include:

Digital convenience – Easy home activation & instant connectivity
Multi-country eSIMs also available – One SIM for all your Asia travels
Thailand plans from 160 baht – About $5 USD for 1GB
Ultra-flexible top-ups – Add data & days as you go
Supports eSIM & dual SIM devices – Use your existing SIM + Airalo

Airalo’s Thailand eSIM packages are affordably priced and centered around maximum flexibility:

₹ 1 GB high-speed data – 7 days = 160 baht (~$5 USD)

₹ 5 GB high-speed data – 15 days = 795 baht (~$22 USD)

₹ 10 GB high-speed LTE data – 30 days = 945 baht (~$26 USD)

You can also build fully custom plans with as much data and validity as needed through their website or app. Love the flexibility!

Since Airalo eSIMs are all digitally activated and managed from your phone, pickup and activation are a total breeze:

Step 1 : Order your Thailand eSIM via Airalo’s app or website

Step 2 : Scan the QR code they email to instantly activate your plan

Step 3 : Enjoy high-speed data after a quick APN setup! 🥳

With outstanding convenience and reasonably priced data packages, Airalo is my hands-down favorite pick for getting an eSIM in Thailand. Never fuss with physical SIMs again!

Digital eSIM convenience
Reasonably priced data packs
Customizable plans
Easy remote setup & activation
Dual SIM compatibility
eSIM compatibility required
Data only (no built-in calling or SMS)

SimOptions – Best Physical SIM for Thailand

Can’t use eSIM? No worries! SimOptions Thailand SIM cards deliver the next best convenience perk: at-home delivery before your trip. 😁

Like a normal nano SIM you’d insert at your destination, SimOptions ships an international Thailand Prepaid SIM to your home address days ahead of travel.

Instead of battling crowds to find a Thailand SIM pickup at the Bangkok airport, you can slide in your card immediately after landing and enjoy instant connectivity. So awesome!

I’m thrilled SimOptions expanded coverage to include Thailand. Their global SIM cards offer a nice compromise for those seeking maximum convenience without going fully digital.

Some standout benefits of a SimOptions Thailand SIM include:

Ships FREE 1-3 days to your home – Arrive in Thailand activated & ready
No registration or ID required
Auto-configures on 1st use – Zero setup or app downloads
14-day $50 plan w/ 10GB high-speed data & 400 local mins
Cost-saving vs. roaming – Ships worldwide from their HK warehouse

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