Research

Research

Validity

Validity refers to the extent to which the results obtained from data analysis accurately represent the phenomenon under study (Mugenda & Mugenda, 2003). It denotes the degree to which a concept, conclusion, or measurement is well-grounded and corresponds correctly to reality.

The term “valid” originates from the Latin word “validus,” meaning strong. In measurement contexts, validity signifies how well a tool, such as an educational test, measures what it is intended to measure. In this sense, validity is synonymous with accuracy.

In psychometrics, validity has a specialized application known as test validity, which pertains to the extent to which evidence and theoretical support justify interpretations of test scores for their intended purposes.

Scientific validity is considered a fundamental aspect of understanding reality, making it both a philosophical and epistemological issue, as well as a measurement concern. In logic, validity is more narrowly defined, referring to the truthfulness of inferences derived from premises.

Validity is crucial in research as it helps determine appropriate testing methods, ensuring that researchers employ ethical, cost-effective, and accurate techniques that truly measure the intended concept or construct.

Assessment validity refers to how well an evaluation measures what it is designed to assess. Unlike reliability, which focuses on consistency, validity does not require consistent results but rather the correct measurement of the intended variable. A measurement can be reliable without being valid; for example, a scale that consistently shows a weight five pounds off is reliable but not valid. However, a test cannot be valid unless it is also reliable.

Validity is a relative concept, meaning it is not an all-or-nothing characteristic. Various types of validity exist, each assessing different aspects of measurement accuracy.

Reliability

Reliability in psychological research refers to the consistency of a study or measurement tool. For instance, if a person weighs themselves multiple times during a day, they should expect relatively consistent readings. A scale producing different weights each time would be considered unreliable.

Reliability pertains to the quality of measurement, emphasizing the repeatability and consistency of results. To understand reliability, it is essential to grasp the true score theory of measurement and the impact of measurement errors, which can degrade reliability.

Types of Reliability

1. Test-Retest Reliability: This type is determined by administering the same test to a group at two different points in time and correlating the results to assess stability.
2. Parallel Forms Reliability: This is established by using different versions of an assessment that test the same construct. The results are compared to evaluate consistency across versions.
3. Inter-Rater Reliability: This assesses the agreement among different evaluators or raters, ensuring that subjective judgments are consistent.
4. Internal Consistency Reliability: This evaluates whether different items within a test that measure the same concept produce similar results.
   • Average Inter-Item Correlation: Determines the consistency of test items by calculating correlations between them and averaging the results.
   • Split-Half Reliability: Splits test items into two sets and compares the correlation between scores on both halves.

Measurement Scales in Quantitative Research

Quantitative research requires measurements to be both accurate and reliable. Researchers assign values to attributes of individuals, objects, or concepts through measurement. These variables are classified into four levels:

1. Nominal Scale: Categorizes data without implying order or value. Examples include gender (male/female) or smoker/non-smoker classifications.
2. Ordinal Scale: Ranks data but does not indicate the magnitude of differences between ranks. For instance, levels of agreement (low, medium, high) in a survey.
3. Interval Scale: Has equal distances between data points but lacks a true zero. An example is the Fahrenheit temperature scale.
4. Ratio Scale: Contains all characteristics of other scales and includes an absolute zero, allowing for meaningful ratio comparisons (e.g., time in minutes).

Types of Validity

1. Concurrent Validity: Assesses how well a measurement correlates with other established measures of the same construct when tested simultaneously.
2. Predictive Validity: Measures how well a test predicts future performance. For instance, an employment selection test should correlate with future job performance.
3. Statistical Conclusion Validity: Determines whether conclusions drawn about variable relationships are accurate and reasonable based on statistical evidence.
4. Internal Validity: Evaluates whether causal conclusions drawn from a study are justified based on its design, setting, and methodology.

Threats to Validity

1. Maturation: Changes in participants over time can affect study results. This is a threat in single-group studies but less so in two-group designs.
2. History: External events occurring during a study can influence results. This is mitigated in comparison group designs.
3. Statistical Regression: Extreme scores tend to move toward the mean upon retesting, potentially affecting study outcomes.
4. Testing Effects: A pre-test may influence participant responses on a post-test, affecting results.
5. Compensatory Rivalry: When participants in a control group become aware of the experimental treatment and alter their behavior, affecting study result