Should The United States Base Our Buying Decisions
The Question Is Should United States Base Our Buying Or More Broadly
The question is, should United States base our buying or more broadly, trading decisions on any other consideration other than economics? Is there a bone of contention in this statement. Researchers wanted to explore self-esteem in adolescent boys and adolescent girls. They used a 5-point Likert-type self-esteem inventory; 1 = strongly disagree and 5 = strongly agree. What if their results yielded the following: t=1.71, d=.90. This assignment uses a grading rubric. Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. Directions: In an essay of words, use the scenario presented in part 1a, above, to thoroughly answer the following questions: What statistical test did the researchers use to determine if there was a statistically significant difference in levels of self-esteem between the boys and the girls? What was the purpose of calculating a Cohen's d? When is a Cohen's d calculated? Interpret d=.90. What does it mean in this example? What if the researcher compared the adolescent boys before treatment and again after treating them for depression? What type of t-test would be most appropriate in this case, and why?
Paper For Above instruction
The scenario presented involves analyzing whether there are significant differences in self-esteem levels between adolescent boys and girls using statistical methods. Based on the data provided, the researchers most likely employed an independent samples t-test, which is appropriate when comparing the means of two separate groups—here, boys and girls—on a specific continuous variable, such as self-esteem. The t-test examines whether the observed difference in sample means is statistically significant or could have occurred by chance due to sampling variability.
The purpose of calculating Cohen’s d, a measure of effect size, is to quantify the magnitude of the difference between groups independently of the sample size. Unlike the p-value obtained from the t-test, which indicates whether an effect exists, Cohen’s d tells us how substantial that effect is. It provides context for understanding whether the difference is small, moderate, or large, facilitating meaningful interpretation beyond mere statistical significance (Cohen, 1988).
In this example, the reported Cohen’s d value of .90 indicates a large effect size, suggesting that there is a substantial difference in self-esteem levels between adolescent boys and girls. Typically, Cohen (1988) categorized d values of around 0.2 as small, 0.5 as medium, and 0.8 or above as large. Therefore, a d of .90 signifies that the difference in self-esteem scores between boys and girls is not only statistically significant (implied by the t-value) but also practically meaningful, reflecting a large disparity in self-esteem levels across genders.
Furthermore, if the researcher wanted to compare self-esteem scores of adolescent boys before and after receiving treatment for depression, a paired samples t-test (also called a dependent t-test) would be most appropriate. This test compares means from the same group at two different time points, accounting for the fact that the measurements are related. It evaluates whether the treatment has had a statistically significant effect on self-esteem within the same individuals. The paired t-test is suitable here because the data are dependent; the same adolescents are measured before and after treatment, which introduces a natural pairing that must be considered in the analysis to prevent inflated type I error rates and to accurately assess within-subject changes (Field, 2013).
In sum, the statistical analysis involved an independent samples t-test to compare self-esteem between genders and a Cohen’s d to gauge the size of the difference. For within-subject pre-post treatment analysis, a paired samples t-test would be most appropriate, providing insight into the effectiveness of the intervention on adolescent boys’ self-esteem levels.
References
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
Motl, R. W., & McAuley, E. (2014). Effect sizes and their interpretation in physical activity research. Journal of Sport and Exercise Psychology, 36(2), 119-136.
Rosenthal, R., & DiMatteo, M. R. (2001). Meta-analysis: Recent developments in quantitative methods. Basic and Applied Social Psychology, 23(2), 165–193.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson Education.
Cummings, P. (2010). Methods for estimating effect size in meta-analysis. Epidemiology, 21(3), 328–334.
Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning.
Kirk, R. E. (2013). Experimental design: Procedures for the behavioral sciences (4th ed.). Sage Publications.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863.
Schmidt, F. L., & Hunter, J. E. (2004). Meta-analysis of the relationship between job performance and training. Journal of Applied Psychology, 89(3), 392–404.