Calculating Effect Size (Cohen's d) for a Paired-Samples T Test

Summary

Dr. Grande explains how to calculate effect size using a paired samples t-test, demonstrating the process in SPSS and Excel with fictitious data on depression levels from 50 participants. He outlines the steps for conducting the t-test, interpreting the output, and calculating Cohen's D through two methods. The video emphasizes the importance of both p-values and effect sizes in assessing statistical significance and practical relevance, highlighting that a Cohen's D of 0.4 indicates a small effect size. This informative guide equips viewers with essential tools for data analysis in psychological research.

Takeaways

• 😀 The paired samples t-test is used to compare means from two related groups, assessing the impact of a treatment.
• 😀 SPSS can efficiently perform paired samples t-tests, providing vital statistics like means, standard deviations, and p-values.
• 😀 A p-value of 0.0005 indicates a statistically significant difference between pretest and post-test scores.
• 😀 Cohen's D is a measure of effect size that quantifies the magnitude of the difference between two means.
• 😀 Two methods for calculating Cohen's D include using the mean difference over standard deviation and the t-statistic over the square root of sample size.
• 😀 In this example, both methods yielded a Cohen's D value of approximately 0.4373.
• 😀 Cohen's D can be interpreted using general guidelines: small (0.2), medium (0.5), and large (0.8) effect sizes.
• 😀 A Cohen's D of 0.44 suggests a small effect size, indicating that the difference is around 0.44 standard deviation units.
• 😀 Statistical significance does not always imply that the difference is practically important; effect size provides context.
• 😀 It's essential to calculate both the p-value and the effect size when analyzing data with inferential statistics.

Q & A

What is the purpose of the video?

The video demonstrates how to calculate effect size using a paired samples t-test in SPSS and Excel.

What do the pretest and post-test variables measure?

The pretest and post-test variables measure depression levels in participants.

How many participants are involved in the study?

The study involves 50 participants.

What statistical test is conducted in the video?

A paired-samples t-test is conducted to determine if there is a significant difference between pretest and post-test scores.

What is the significance of the p-value mentioned?

The p-value of 0.0005 indicates a statistically significant difference between the pretest and post-test scores.

What are the two methods for calculating Cohen's D presented in the video?

Cohen's D can be calculated using the mean divided by the standard deviation, or by dividing the T statistic by the square root of the sample size.

What was the calculated value of Cohen's D?

The calculated value of Cohen's D was approximately 0.4373.

How is Cohen's D interpreted in terms of effect size?

Cohen's D values are interpreted as small (0.2), medium (0.5), and large (0.8) effects; a value of 0.4 suggests a small effect size.

Why is it important to calculate effect size along with p-values?

Calculating effect size provides insight into the practical significance of the difference, while p-values indicate statistical significance.

What does a Cohen's D value of 0.4 imply about the difference between means?

A Cohen's D value of 0.4 indicates that the difference between the two sample means is 0.4 standard deviation units away from zero, suggesting a moderate effect.