Good research does not rely on hunches; it rests on the measurement that the reader can trust. That’s why every research design must have these two characteristics in it, i.e, reliability (consistency) and validity (accuracy).
Reliability tells us whether a measure gives stable results, whereas validity refers to whether those results reflect what you intend to measure or not. Together, both of these factors determine the quality of your findings from planning to completion.
However, ignoring these factors can lead to misleading data and weak conclusions. So, to avoid these issues, add them to your design choices analysis and sampling.
| Aspect | Reliability | Validity |
|---|---|---|
| What it shows | Consistency of results when repeated under similar conditions | Accuracy in measuring the intended concept |
| How it’s checked | Consistency across time, raters, and test parts | Agreement with theory and other accepted measures |
| Relationship | A measure can be reliable but not valid | Valid measures are generally reliable |
What is Reliability?
Reliability refers to the consistency of the measurement. It shows how trustworthy the score of the test is. 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. → reliable results.
Example:
If a teacher conducts the same math test for students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.
Note: Reliability is not sufficient on its own; that’s why it is necessary for validity.
How to Assess Reliability?
Reliability can be measured by comparing the consistency of the procedure and its results. It can be measured through various statistical methods depending on the types of validity, as explained below:
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Types of Reliability
| Type | What it measures | Example |
|---|---|---|
| Test–retest | Consistency of scores over time. | A group of students takes the same math test today and again in one week. If most students get very similar scores on both occasions, the test has high test-retest reliability. |
| Inter-rater | The degree of agreement between different people (raters/observers/judges) scoring or observing the same thing. | Two different doctors evaluate the severity of a patient’s rash using the same scale. If both doctors assign very similar scores, their evaluations show high inter-rater reliability. |
| Parallel forms | Equivalence between two different versions (forms) of the same test or measure. | A researcher creates Form A and Form B of a history exam, covering the same content and difficulty. A group of students takes both forms. If their scores on Form A are highly correlated with their scores on Form B, the measure has high parallel forms reliability. |
| Internal consistency (Split-Half) | Consistency of measurement across the items within a single test. | A 50-question personality questionnaire is split into two halves (e.g., odd vs. even questions). The scores on the two halves for the same group of people are compared. A high correlation between the scores on the two halves suggests high internal consistency reliability. |
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.
What is Validity?
Validity is the accuracy of a measurement. It 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:
If a scale shows different types of results of weight with no real change, it shows that our method lacks reliability, and the result is not valid.
Example:
If a skincare product questionnaire receives a similar response from different groups, it shows that the real user experience is great and the validity is high.
Types of Validity
| Type | What it measures | Example |
|---|---|---|
| Content validity | The extent to which a measure adequately covers all aspects of the construct being measured. | A test designed to measure knowledge of U.S. History should include questions on all major periods (Colonial, Revolutionary, Civil War, etc.), not just a single period. If it covers all key topics, it has high content validity. |
| Face validity | The extent to which a measure appears to be a plausible measure of the construct. (The least rigorous type of validity.) | A survey asking people about their favorite colors appears to measure color preference. A test labeled “General Knowledge Exam” appears to measure broad knowledge. It is judged by laypersons or participants, not experts. |
| Construct validity | The degree to which a test measures the theoretical construct (e.g., intelligence, anxiety, communication skills) it is intended to measure. | A new “Anxiety Scale” should be highly correlated with existing, validated measures of anxiety (convergent validity) but should have low correlation with a measure of a different construct like intelligence (discriminant validity). |
| Criterion validity | The extent to which a measure is related to an outcome (the criterion) it is supposed to predict. This is split into concurrent and predictive validity. | The score on a college entrance exam (the measure) should correlate well with a student’s first-year GPA (the criterion). If taken before college, this is predictive validity. If scores on a new depression test correlate with a clinical diagnosis given at the same time, this is concurrent validity. |
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.
Example:
Findings from research on pregnant women may not generalize to other normal women or men.
Threats to Internal Validity
| Threat (to Internal Validity) | Definition | Example |
|---|---|---|
| History | Unanticipated external events that occur during the experiment that could affect the dependent variable. | A study on the effectiveness of a new online learning program is interrupted by a sudden, mandatory school closure (e.g., due to a natural disaster). The closure, not the program, may explain changes in student performance. |
| Maturation | Changes in the participants themselves (biological or psychological) that occur naturally over time, independent of the treatment. | A year-long study tracking reading comprehension in first-graders. Any improvement in reading could be due to normal developmental growth over the year (maturation) rather than the special reading intervention. |
| Testing | The effect of taking a pre-test on the scores of a subsequent post-test. The act of measuring can change the characteristic being measured. | Participants in a memory study score higher on the final memory test simply because the pre-test exposed them to the specific types of questions or material, making them more familiar with the task. |
| Instrumentation | Changes in the measurement tool (e.g., equipment, observer training, scoring criteria) over the course of the study. | A study uses human observers to rate aggressive behavior. If the observers become more experienced, tired, or change their standards for what counts as “aggressive” between the start and end of the study, the change in scores is due to instrumentation, not the treatment. |
| Statistical Regression (Regression to the Mean) | The tendency for extreme scores (either very high or very low) on an initial measurement to move closer to the group mean on a subsequent measurement. | A researcher selects the 10 students with the lowest math scores for a remedial program. Even if the program is ineffective, their scores are likely to be higher on the post-test simply because their initial scores were statistically extreme and unstable. |
| Selection Bias | Differences between the experimental and control groups that existed before the treatment was administered, often due to non-random assignment. | In a job training study, volunteers (who may be more motivated) are assigned to the treatment group, while non-volunteers are in the control group. Any difference in job performance afterward could be due to initial motivation, not the training. |
| Mortality (Attrition) | Participants dropping out of the study, particularly if the drop-out rate or reasons differ significantly between the experimental and control groups. | In a study testing a difficult exercise program, the least fit participants drop out of the treatment group. The remaining participants show greater fitness gains, but this is because the fittest people are left, not necessarily because the program was effective for everyone. |
How to Assess Validity?
Validity is assessed by comparing the results with theory and other measures by gathering the evidence given by the test to construct relevant facets.
Threats of External Validity
| Threat (to External Validity) | Definition | Example |
|---|---|---|
| Interaction of Selection and Treatment | The effect of the treatment is specific only to the particular type of participants (e.g., specific demographic or characteristic) included in the study. | A new reading program is tested only on highly gifted students. The program’s effectiveness may not generalize to average or below-average students. |
| Interaction of Setting and Treatment | The treatment effect observed in one setting (e.g., lab, specific school, or environment) cannot be replicated in a different setting. | A team-building intervention is highly successful in a relaxed, non-competitive corporate environment. The results may not generalize to a high-stress, highly competitive workplace. |
| Interaction of History and Treatment | The treatment’s effectiveness is limited to a specific time period or set of historical circumstances during which the study was conducted. | An anti-smoking campaign is highly effective when launched immediately after a major public health scare regarding tobacco. The same campaign may be much less effective when launched during a period of low public concern. |
| Reactive Arrangements / Hawthorne Effect | Participants modify their behavior simply because they know they are being observed or are part of an experiment. | Employees significantly increase their productivity after researchers start observing them and measuring their output, regardless of whether a new incentive system (the treatment) is implemented. |
How to Increase Validity?
- 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.
- Inter-rater reliability should be ensured.
- The control and experimental groups should be matched with each other.
How to Implement Reliability and Validity in a Thesis?
| Section (in a Research Paper) | How to Address Reliability and Validity | Key Focus |
|---|---|---|
| Literature Review | Discuss how previous studies addressed reliability and validity of the constructs and measures you plan to use. | The contributions and limitations of other researchers’ work in establishing the measurement quality of the variables. |
| Methodology | Describe the specific procedures, tests, and calculations you will use to ensure and measure reliability and validity in your study. | Planning and execution: Details on sample selection, instrument development, types of reliability (e.g., Cronbach’s alpha, test-retest) and validity (e.g., content, construct) to be assessed. |
| Results | Present the statistical outcomes of your reliability and validity checks. | Calculations and statistical values: Report the calculated coefficients (e.g., $\alpha$ value, correlation coefficients) for reliability and the empirical evidence for validity. |
| Discussion | Interpret the statistical results for reliability and validity, and explain their implications for the study’s findings. | Interpretation and impact: Comment on the level of reliability and validity achieved, and how these factors influence the confidence in your conclusions and their generalizability. |
| Conclusion | Summarize the overall quality of the measurements and briefly mention any challenges faced or limitations observed. | Overall summary and limitations: Final statement on the rigor of the study’s measurement and any issues faced while ensuring reliability and validity. |
Frequently Asked Questions
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.
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.
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.
