Significant Gender Differences in Work Hours: T-Test Findings

The research hypothesis, or alternative hypothesis, and the null hypothesis can be inferred from the context of the analysis of the Independent Samples T-test (Schnuerch & Erdfelder, 2020). Research Hypothesis (Alternative Hypothesis): There is a significant difference in the mean number of hours worked last week between male and female respondents. Null Hypothesis: Male and female respondents have no significant difference in the mean number of hours worked last week. T-test Findings: Group Means: Male (MALE) group mean: 43.92 hours Female (FEMALE) group mean: 38.92 hours T-test Value: When equal variances are assumed: t-statistic = 5.038 When equal variances are not assumed: t-statistic = 5.048 Significance (P) Level : In both cases, the two-sided p-value is less than 0.001 Group Means: For the variable Number of Hours Worked Last Week, the mean number of hours worked last week for males is 43.92 hours, and for the female group, the mean number of Hours Worked Last Week is 38.92. Conclusion: Do the reported results enable me to reject the null hypothesis and accept the hypothesis?: Based on the findings, one could conclude that the T-test results show a statistically significant difference in the mean “number of hours worked last week” between male and female respondents (Ioannidis, 2019). As the P-values are less than 0.0001, this means that the probability of observing a significant difference is low. With low P-values and the magnitude of the T-statistic, there exists strong enough evidence to reject the null hypothesis which means that the research hypothesis can be accepted since there does exist a significant difference in the mean number of hours worked last week between males and females. Appropriateness of the T-test: One reason this type of test is appropriate is it is designed specifically to compare the means of two independent variables allowing for determining whether there are statistically significant overserved differences (Cesario et al., 2018). The T-test for this dataset analysis is used to compare the continuous variable, the mean number of hours worked, while also assessing if any observed differences were likely to have occurred by random chance. SPSS Output Tables T-Test

Table 1 Number of Hours Worked Last Week One-Sample Statistics N Mean Std. Deviation Std. Error Mean NUMBER OF HOURS WORKED LAST WEEK 895 41.47 15.039 .503 One-Sample Test Test Value = 40 t df Significance Mean Difference One-Sided p Two-Sided p NUMBER OF HOURS WORKED LAST WEEK 2.918 894 .002 .004 1.467 One-Sample Test Test Value = 40 95% Confidence Interval of the Difference Lower Upper NUMBER OF HOURS WORKED LAST WEEK .48 2.45 One-Sample Effect Sizes Standardizer a Point Estimate 95% Confidence Interval Lower Upper NUMBER OF HOURS WORKED LAST WEEK Cohen's d 15.039 .098 .032 .163 Hedges' correction 15.051 .097 .032 .163 a. The denominator used in estimating the effect sizes. Cohen's d uses the sample standard deviation. Hedges' correction uses the sample standard deviation, plus a correction factor.

Note . Adapted from Datasets Codebooks\GSS14SSDS-B.sav by Frankfort-Nachmias & Leon-Guerrero, 2021 (https://edge.sagepub.com/frankfort8e2/student-resources/data-sets-and-codebooks) in conjunction with IBM SPSS Statistics, Ver. 28.0.1, by IBM, n.d. (https://www.ibm.com/products/spss-statistics). Table 2 Respondents Sex T-Test One-Sample Statistics N Mean Std. Deviation Std. Error Mean RESPONDENTS SEX 1500 1.55 .497 .013 One-Sample Test Test Value = 40 t df Significance Mean Difference One-Sided p Two-Sided p RESPONDENTS SEX -2993.409 1499 .000 .000 -38.448 One-Sample Test Test Value = 40 95% Confidence Interval of the Difference Lower Upper RESPONDENTS SEX -38.47 -38.42 One-Sample Effect Sizes Standardizer a Point Estimate 95% Confidence Interval Lower Upper RESPONDENTS SEX Cohen's d .497 -77.289 -80.056 -74.522 Hedges' correction .498 -77.251 -80.016 -74.485

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a. The denominator used in estimating the effect sizes. Cohen's d uses the sample standard deviation. Hedges' correction uses the sample standard deviation, plus a correction factor. Note . Adapted from Datasets Codebooks\GSS14SSDS-B.sav by Frankfort-Nachmias & Leon-Guerrero, 2023 ( https://edge.sagepub.com/frankfort8e2/student-resources/data-sets-and-codebooks) in conjunction with IBM SPSS Statistics, Ver. 28.0.1, by IBM, n.d. (https://www.ibm.com/products/spss-statistics). Table 3 Independent Samples Test T-Test [DataSet1] C:\Users\SnS_M\OneDrive\Desktop\CRJ_575_Analytical Methods\Datasets Codebooks\GSS14SSDS-B.sav Group Statistics RESPONDENTS SEX N Mean Std. Deviation Std. Error Mean NUMBER OF HOURS WORKED LAST WEEK MALE 456 43.92 15.528 .727 FEMALE 439 38.92 14.085 .672 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t NUMBER OF HOURS WORKED LAST WEEK Equal variances assumed 4.931 .027 5.038 Equal variances not assumed 5.048 Independent Samples Test t-test for Equality of Means df Significance One-Sided p Two-Sided p

NUMBER OF HOURS WORKED LAST WEEK Equal variances assumed 893 <.001 <.001 Equal variances not assumed 889.868 <.001 <.001 Independent Samples Test t-test for Equality of Means Mean Difference Std. Error Difference NUMBER OF HOURS WORKED LAST WEEK Equal variances assumed 4.999 .992 Equal variances not assumed 4.999 .990 Independent Samples Test t-test for Equality of Means 95% Confidence Interval of the Difference Lower Upper NUMBER OF HOURS WORKED LAST WEEK Equal variances assumed 3.051 6.946 Equal variances not assumed 3.055 6.942 Independent Samples Effect Sizes Standardizer a Point Estimate 95% Confidence Interval Lower Upper NUMBER OF HOURS WORKED LAST WEEK Cohen's d 14.838 .337 .205 .469 Hedges' correction 14.850 .337 .205 .468 Glass's delta 14.085 .355 .222 .488 a. The denominator used in estimating the effect sizes. Cohen's d uses the pooled standard deviation. Hedges' correction uses the pooled standard deviation, plus a correction factor. Glass's delta uses the sample standard deviation of the control group. Note . Adapted from Datasets Codebooks\GSS14SSDS-B.sav by Frankfort-Nachmias & Leon-Guerrero, 2023 (https://edge.sagepub.com/frankfort8e2/student-resources/data-sets-and-codebooks) in conjunction with IBM SPSS Statistics, Ver. 28.0.1, by IBM, n.d. (https://www.ibm.com/products/spss-statistics).

References Cesario, K. A., Dulla, J., Good, A. B., Moreno, M. R., Dawes, J., & Lockie, R. G. (2018). Relationships between assessments in a physical ability test for law enforcement: Is there redundancy in certain assessments? International Journal of Exercise Science , 11 (4). https://digitalcommons.wku.edu/cgi/viewcontent.cgi?article=2308&context=ijes IBM. (n.d.). IBM SPSS statistics - Gradpack and faculty packs . IBM. https://www.ibm.com/products/spss- statistics/gradpack Ioannidis, J. P. A. (2019). What have we (not) learnt from millions of scientific papers with p values? The American Statistician , 73 (sup1), 20–25. https://doi.org/10.1080/00031305.2018.1447512 Frankfort-Nachmias, C., & Leon-Guerrero, A. (2023). Social statistics for a diverse society: Data sets and codebooks (8th ed.). https://edge.sagepub.com/frankfort8e2/student-resources/data-sets-and- codebooks Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t-test. Psychological Methods , 25 (2), 206–226. https://doi.org/10.1037/met0000234

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