Understanding Reliability in Classical Test Theory (CTT): Types and Measurement

1. Test-Retest Reliability

Test-retest reliability assesses the stability of test scores over time. It measures whether a test yields consistent results when administered to the same group at two different times under similar conditions. The assumption here is that the construct being measured remains stable.

To calculate test-retest reliability, the correlation between the scores from both tests is computed. A high correlation suggests strong reliability, indicating that external factors like random errors or time-related influences do not significantly affect results.

Key Considerations:

• The time interval between administrations must be sufficient to prevent memory effects or practice effects.
• External conditions, such as environmental changes, could influence reliability.

2. Parallel-Forms Reliability

Parallel-forms reliability, also called alternate-forms reliability, examines the equivalence of two test versions designed to measure the same construct. These versions contain different items but aim to produce comparable results.

To assess this type of reliability, the correlation between the scores from both versions is calculated. A high correlation indicates that the forms are equivalent.

Key Considerations:

• Designing truly equivalent test forms can be challenging.
• Both forms should be administered under identical conditions to minimize variability.

3. Internal Consistency Reliability

Internal consistency reliability assesses how well the items within a test correlate with each other, ensuring that they all measure the same underlying construct. This is especially relevant for tests with multiple items, such as questionnaires.

Common methods for evaluating internal consistency include:

• Cronbach’s Alpha: The most widely used method, it calculates the average correlation among test items. A value above 0.70 generally indicates good reliability.
• Split-Half Reliability: The test is split into two halves, and the scores from each half are correlated. A correction is applied to estimate the full test's reliability.
• Kuder-Richardson Formula 20 (KR-20): This is a specific measure for dichotomous items, like true/false questions.

Key Considerations:

• High values may suggest redundancy, while low values indicate that the items may not measure the same construct.

4. Inter-Rater Reliability

Inter-rater reliability focuses on the consistency of scores assigned by different raters, especially when the assessment involves subjective judgment. Examples include grading essays or performance evaluations.

Common methods include:

• Cohen’s Kappa: This measures the agreement between two raters while accounting for chance. Values above 0.75 indicate good reliability.
• Intraclass Correlation Coefficient (ICC): Used for more than two raters, this provides an estimate of the variance attributable to raters versus individuals being rated.

Key Considerations:

• Training raters minimizes subjectivity.
• Consistency is essential in subjective evaluations.

1. Correlation Coefficients

Correlation coefficients, such as Pearson’s or Spearman-Brown Prophecy Formula, are used to measure the relationship between two sets of scores. These are common in test-retest and parallel-forms reliability assessments.

2. Cronbach’s Alpha

Cronbach’s alpha is the go-to measure for internal consistency. It evaluates how well the test items correlate with one another, with values above 0.70 generally considered reliable.

3. Intraclass Correlation Coefficient (ICC)

ICC is commonly used for inter-rater reliability, providing a more detailed measure than simple correlation, accounting for systematic rater differences.