Effect Sizes in Empirical Research: Applications of Cohen's d and Statistical Analysis
Overview
This paper discusses the significance of effect sizes in empirical research, particularly focusing on Cohen's d as a standardized measure of mean differences. The author reflects on the practical application of effect sizes in facilitating cumulative science and determining sample sizes for follow-up studies, while also documenting challenges encountered when learning SPSS software for statistical analysis.
What this reflection paper covers
- Definition and importance of effect sizes in empirical research
- Application of Cohen's d for standardized mean difference across different measurement scales
- Personal reflection on challenges with SPSS software and learning curve
Preview
Discussion
Effect sizes are significant empirical studies results. For instance, they facilitate cumulative science (Lakens, 2013). In most cases, statisticians use effect sizes to identify follow-up studies sample sizes, or evaluate the effects across variables. Cohen’s d helps in describing an effect’s standardized mean difference. The value is often used to examine the effects between variables. For instance, a certain study utilizes 7-point scale to examine the dependent variable, while another utilizes a 9-point scale. The main challenges experienced while completing the exercise included running the exercises through SPSS. Since it is the first time to use the program, I always have to spend much time learning the best approach to make complete the exercise requirements. Understanding how to apply SPSS commands is the key knowledge gap but I am confident that my skills are improving.
References
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4.