As you do your research, there are two key ideas to use to build the quality and trustworthiness of your study validity and reliability. Both are necessary for determining the qualitative forecast and dependability of academic research instruments and strategies. Although validity and reliability are related, they represent two separate aspects of the research process. In this blog you are going to learn about what are the differences between Validity vs Reliability, and the types of both and some examples.
What is Validity in Research?
Validity represents how well a research study determines what it intends to do. It guarantees the findings’ reliability truthfulness and knowledge that the findings actually represent the theory of interest. If the conclusions of a study are not valid, they can be inaccurate or misleading.
Types of Validity vs Reliability
1: Internal Validity
The issue of internal validity is concerned with whether the design and research methodology of a study successfully pinpoint the cause-and-effect relationship between variables. Internal validity is high if researchers can confidently attribute changes in the dependent variable to the independent variable, and not some external factor.
For example, if you are studying the effects of a given diet on weight loss, internal validity will make sure things like exercise or taking medicine do not influence results so that only the diet can be blamed.
2: External Validity
The validity of results beyond the study is called external validity. A high external validity study can be confident to apply its results out of the given context in which it was conducted. For example, to generalise the results of research meant to determine the effectiveness of a vaccine in one city, the results should apply to a broader population on a global scale.
3: Construct Validity
Construct validity examines whether it measures what it is supposed to measure. This ensures a perfect alignment between theoretical definitions and what is effectively measured. An example is a test that is intended to measure intelligence in which irrelevant traits, such as memory, are not tested but skills such as problem-solving and reasoning are
4: Content Validity
Content validity guarantees that a measurement tool deals with all aspects of a concept under study. Left out will be critical elements and the results will be biased or incomplete. For example, an English proficiency test should include reading, writing, speaking and listening as part of its components.
5: Criterion Validity
Criterion validity refers to how well a tool reflects an existing benchmark. It comprises concurrent validity (comparison at the same time) and predictive validity (comparison over time). For example, a validated and established performance evaluation tool closely aligns with a new job performance assessment.
In Research, What Does Reliability Mean?
Reliability is the consistency and stability of a measurement tool. Dependable instruments deliver the same results in repeatable situations; that is, from instrument to instrument. Reliability does not concern the reproducibility of findings; it attempts to reduce random errors.
Types of Reliability
1: Test-Retest Reliability
Test-retest reliability measures the measurement stability over time by giving the same test to the same group of people on two occasions. For instance, if you ask a group the same stress level questionnaire a week later as long as conditions do not change you should get the same results.
2: Inter-Rater Reliability
This type refers to the degree of consensus among two or more observers rating the same phenomenon. Providing high inter-rater reliability allows you to have uniformity in subjective assessments. For example, two judges statistically score a dance performance against a rubric if the rubric is clear and standardised, the ratings from both judges will be similar.
3: Parallel-Forms Reliability
Parallel forms reliability is a test of the equivalence of different versions of a test. For example, test takers of a certification exam should receive the same score on alternate forms of the same exam and therefore score reliability.
4: Internal Consistency Reliability
Internal consistency is the analysis of item consistency for participants answering a single set of items. Higher internal consistency means all of the items measure the same construct. For example, a satisfaction survey should have answers that are consistent with one average overall satisfaction number.
Validity vs Reliability
Both validity vs reliability are related measures of measurement quality, although they speak to different aspects of that quality.
Validity is concerned with accuracy; that is, whether a tool measures what it is supposed to. For example, a test of mathematical ability should not inadvertently test reading comprehension as well.
Reliability is the consistency with which a tool will give the same results under the same conditions for validity vs reliability. Even a test that is reliable can be an invalid test, for example, a bathroom scale that always returns a number that is too small or too large. On the other hand, an inherently reliable tool should be valid.
Examples of Validity and Reliability in Practice
Educational Testing
- Validity: A math test should test students’ math skills, not reading skills or ability to follow instructions.
- Reliability: In a given test, scores should be the same if given out to a student twice under similar conditions.
Medical Research
- Validity:
A diagnostic test for COVID-19 should have both high sensitivity and high specificity: that is, it should identify infected individuals but exclude uninfected ones.
- Reliability:
Consistent results should be obtained when the same test is repeated over and over again on the same person.
Market Research
- Validity:
The customer satisfaction survey should be able to see how customers perceived their experiences.
- Reliability:
The survey should give the same results when re-performed with another group of respondents or at another time.
Increasing the validity of a research study; the replicability of its results; the degree to which findings apply across settings, over time, and different population segments; and the precision of data interpretation.
Increasing The Validity & Reliability in Research
Tips for Improving Validity
- Define Constructs Clearly:
Present precise definitions of the concepts to be studied so measurement may be accurate.
- Use Established Instruments:
Use tools known to have been validated in past studies for credibility.
- Pilot Testing:
Estimate and eliminate issues on a small sample before large-scale research by testing test instruments on a small sample.
- Control Extraneous Variables:
Maximum murmur: external factors will not affect the results as much as possible.
- Triangulation:
Combine different methods and data sources to increase the validity of findings.
Tips on How to Improve Reliability
- Standardise Procedures:
Maintain consistency in the administration of tests and data collection protocols.
- Train Observers and Researchers:
Train data collectors extensively to minimize subjectivity and variability.
- Increase Sample Size:
The larger the sample, the more stable the result gets and the less the effect of random errors.
- Repeat Measurements:
Repeated testing should be conducted to confirm the repeatability of results.
- Refine Measurement Tools:
Tools should be reviewed, updated and supported regularly so they remain reliable through time.
CONCLUSION
Research integrity is based on validity vs reliability as foundational notions. Validity guarantees that the study measures what is planned to be measured and provides the right results. Reliability provides that measurements are consistent and stable and thus increases the dependability of findings. This understanding of these concepts helps researchers design robust studies with reliable conclusions, add to the knowledge bank, and influence real-life applications.
