Which parametric test is appropriate for assessing changes in scores from the same participants at two different time points?

Which parametric test is appropriate for assessing changes in scores from the same participants at two different time points?

• A. Independent t-test
• B. Matched t-test
• C. Paired T test
• D. ANOVA

Answer: (C)

The paired t-test is the appropriate parametric test for assessing changes in scores from the same participants at two different time points because it specifically addresses situations where you have matched or paired observations. In this context, the same individuals are measured twice, and the paired t-test analyzes the mean difference between these two related groups. This test accounts for the fact that the scores are not independent, as they come from the same participants, thereby increasing the power of the test to detect a significant difference. It operates on the assumption that the differences between the pairs are normally distributed, making it suitable for scenarios where this assumption holds true. In contrast, alternatives such as the independent t-test require two different groups of participants, which does not apply here. ANOVA evaluates differences among means across three or more groups or levels of a factor, making it unnecessary when comparing just two time points within the same group. The matched t-test, while it acknowledges pairs, is not the standard terminology used; the paired t-test is the correct term in this context. Thus, the paired t-test is the best fit for the scenario of measuring within-subject changes over time.

The paired t-test is the appropriate parametric test for assessing changes in scores from the same participants at two different time points because it specifically addresses situations where you have matched or paired observations. In this context, the same individuals are measured twice, and the paired t-test analyzes the mean difference between these two related groups.

This test accounts for the fact that the scores are not independent, as they come from the same participants, thereby increasing the power of the test to detect a significant difference. It operates on the assumption that the differences between the pairs are normally distributed, making it suitable for scenarios where this assumption holds true.

In contrast, alternatives such as the independent t-test require two different groups of participants, which does not apply here. ANOVA evaluates differences among means across three or more groups or levels of a factor, making it unnecessary when comparing just two time points within the same group. The matched t-test, while it acknowledges pairs, is not the standard terminology used; the paired t-test is the correct term in this context. Thus, the paired t-test is the best fit for the scenario of measuring within-subject changes over time.