Assignment 2: Tests Of Significance Throughout This A 401229

Assignment 2 Tests Of Significancethroughout This Assignment You Will

Throughout this assignment, you will review five mock studies involving hypothesis testing. For each study, you will create a data set from scratch, perform the five steps of hypothesis testing using SPSS, and interpret the results. The analyses include t-tests (single sample, dependent, and independent), ANOVA, and chi-square tests. All calculations must be performed in SPSS, and the output file (.spv) must be submitted with your assignment. The assignment is due by Sunday of Week 6 at 11:55 pm ET, and the file should be named in the format [your last name]_SOCI332_A2.docx.

Paper For Above instruction

Hypothesis testing is a core method in research for determining the significance of observed differences or associations within data. This paper examines five mock studies, each illustrating different statistical tests—t-tests, ANOVA, and chi-square—using SPSS software. The goal is to demonstrate how to manage data, execute analyses, and interpret results in a systematic manner aligned with hypothesis testing principles.

Mock Study 1: t-Test for a Single Sample

In the first mock study, researchers investigate whether depressed clients undergoing group therapy perform a different number of daily activities compared to a typical mean of 17 activities. Twelve clients' post-therapy activities are recorded, and a one-sample t-test is planned to compare the sample mean to the population mean of 17. Data entry involves creating a variable named "ADL" and inputting provided scores. Using SPSS, a one-sample t-test is conducted with a test value of 17. The significance of the p-value (“Sig 2-tail”) determines whether to reject the null hypothesis that the population mean equals 17. If the p-value is less than or equal to the significance level (0.05 or 0.01), the null hypothesis is rejected, suggesting that group therapy influences daily activity performance. Based on the decision, behavioral scientists will recommend or not recommend group therapy for depressed individuals.

Mock Study 2: Paired-Samples t-Test

The second study examines whether depression-related activities change pre- and post-therapy within the same group of clients. Eight clients' activity counts before and after therapy are recorded, and a paired-samples t-test is performed. Data management involves creating variables "ADLPRE" and "ADLPOST," inputting data accordingly. The test compares the means of the two related groups, testing for significant differences at the 0.05 level. A significant result indicates that the therapy has a measurable effect on daily activities, leading to a recommendation for therapy as an effective intervention.

Mock Study 3: Independent Samples t-Test

In this scenario, the researcher compares job satisfaction between employees who participated in counseling and those who did not, six months after an industrial accident. Data for two independent groups are entered into SPSS with variables "ADL" and "group" (coded 1 for counseling, 0 for no counseling). An independent-samples t-test assesses whether the two groups differ significantly in their scores. If the p-value ≤ 0.01, the null hypothesis that both groups have equal mean satisfaction scores is rejected, suggesting counseling may improve job satisfaction. Otherwise, counseling may not have a significant effect.

Mock Study 4: One-Way ANOVA

Fifteen clients are assigned to three different therapy frequency groups: biweekly, weekly, and twice-weekly sessions. The number of daily activities performed is recorded. Data management entails creating a variable "ADL" with all scores and a grouping variable "THERAPY" with codes 1, 2, or 3. Using SPSS, a one-way ANOVA tests whether the mean activities differ across groups at the 0.05 significance level. Descriptive statistics are obtained for each group. If the F-test is significant, post-hoc tests can identify specific group differences. A significant ANOVA indicates that therapy frequency impacts daily activity levels, which supports tailored therapy scheduling.

Mock Study 5-1: Chi-Square Goodness of Fit

This study evaluates whether the observed distribution of conflict resolution styles among 20 students differs significantly from an equal distribution across four categories. Data entry involves creating a variable "STYLE" with codes 1-4, each representing a conflict style. The chi-square test compares observed frequencies to expected frequencies assuming equal proportions, using SPSS’s Non-Parametric Tests. A significant p-value (≤ 0.05) indicates that conflict resolution styles are not uniformly distributed among students, suggesting certain styles are more prevalent.

Mock Study 5-2: Chi-Square Test of Independence

The second chi-square study examines the relationship between conflict style and suspension status. Data include conflict style ("STYLE") and suspension ("SUSPEND" – coded 1 for yes, 2 for no). A crosstab analysis with chi-square tests assesses if these variables are associated. A significant result at p ≤ 0.05 suggests that conflict resolution style and suspension status are related, possibly implying certain styles are linked to misbehavior.

Interpreting Results and When to Use Tests

Selection of the appropriate statistical test depends on the research design and data type. T-tests are suitable when comparing means between one or two groups, with the single-sample t-test comparing a sample to a known value, the dependent t-test comparing paired scores, and the independent t-test comparing two independent groups. ANOVA extends this comparison to three or more groups, evaluating whether at least one group mean differs significantly. Chi-square tests are designed for categorical data: goodness of fit assesses whether observed frequencies fit an expected distribution, while tests of independence examine relationships between categorical variables.

In circumstances where multiple groups are compared in terms of means, and the data are continuous, ANOVA avoids the increased risk of Type I error associated with multiple t-tests. T-tests become inappropriate when multiple groups are involved because performing multiple pairwise comparisons inflates Type I error risk unless adjustments are made. Additionally, t-tests are unsuitable for categorical data, where chi-square tests are more appropriate.

Conclusion

This comprehensive examination of hypothesis testing in various mock studies illustrates the importance of selecting the correct statistical method based on data structure and research questions. Proper data management, execution in SPSS, and accurate interpretation of results are essential for making valid conclusions that can inform practice and policy decisions. Behavioral scientists and researchers must understand the underlying assumptions and correct applications of each test to avoid erroneous findings and ensure research validity.

References

• Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage Publications.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences (10th ed.). Cengage Learning.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.
• Spiegel, M. R., et al. (2019). Theory and Problems of Probability and Statistics (6th ed.). McGraw-Hill Education.
• George, D., & Mallery, P. (2019). IBM SPSS Statistics 26 Step by Step. Routledge.
• Peng, C., Lee, K., & Ingersoll, G. (2002). An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1), 3-14.
• McHugh, M. L. (2013). The Chi-Square Test of Independence. Biochemia Medica, 23(2), 143-149.
• Willard, K. W., et al. (2018). Applied Multivariate Statistical Concepts (5th ed.). Cengage Learning.
• Levin, J., & Rubin, D. S. (2004). Statistics for Management (7th ed.). Pearson.