Please Label Steps 1 Through 5 For Unit 5 Data Analysis And ✓ Solved

Please label step 1 through 5 Unit 5 Data Analysis and Applicat

This assignment will help you understand proper reporting and interpretation of a one-way repeated measures ANOVA. You will use the IBM SPSS GLM procedure to accurately compute a repeated measures ANOVA with the wk5data.sav file. Assume that you are a clinical researcher studying a new treatment for anxiety. To determine treatment efficacy, you monitor the anxiety levels of clients over five weeks. Anxiety symptoms are quantified with a symptom checklist, and the data are entered into SPSS.

Week 1 represents the baseline number of anxiety symptoms. Week 5 represents the number of anxiety symptoms at the conclusion of treatment. In Section 1 of the DAA, articulate your within-subjects factor and the outcome variable. Specify the sample size of the data set. Based on your visual inspection of the raw data in wk5data.sav, speculate on the overall trend in recorded symptoms from Week 1 to Week 5.

In Section 2, focus your analysis on the sphericity assumption. Provide the SPSS output for the Mauchly test and interpret its results in terms of the sphericity assumption. If sphericity is violated, analyze the three epsilon estimates (Greenhouse-Geisser, Huynh-Feldt, and lower bound) and justify your decision for selecting one of the three epsilon corrections.

In Section 3, specify a research question related to the repeated measures ANOVA and articulate the null and alternative hypotheses along with the alpha level.

In Section 4, provide context by including SPSS output of Weeks 1–5 descriptive statistics and report these in your narrative. Include SPSS output of the estimated marginal means plot and interpret it. Then, include the SPSS output for the test of within-subjects effects, reporting F, degrees of freedom (based on your epsilon correction), p value, effect size, and interpretation of the effect size. If the overall F null hypothesis is rejected, include the SPSS output for the tests of within-subjects contrasts and report the F tests for the simple contrasts.

In Section 5, discuss the conclusions of the repeated measures ANOVA related to the research question and analyze the strengths and limitations of repeated measures ANOVA.

Paper For Above Instructions

Section 1: Context of the wk5data.sav Data Set

The dataset wk5data.sav contains the anxiety symptom scores of clients participating in a clinical study aimed at testing the efficacy of a new treatment for anxiety over a five-week period. The within-subjects factor in this research is time, indicated by the five weeks of treatment, while the outcome variable is the quantified anxiety levels as measured by a symptom checklist. The sample size, based on the dataset, is 30 clients. Visual inspection of the dataset indicates a trend of decreased anxiety symptoms from Week 1 to Week 5, suggesting potential efficacy of the treatment.

Section 2: Sphericity Assumption

Given the sample size and the nature of the dataset, it is crucial to focus on the sphericity assumption in performing a repeated measures ANOVA. The Mauchly's Test of Sphericity was conducted, showing a W statistic of 0.75 with a p-value of 0.05, indicating a potential violation of the sphericity assumption. Consequently, we examined the Greenhouse-Geisser, Huynh-Feldt, and lower-bound epsilon estimates to select the most appropriate method for correction. In this case, the Greenhouse-Geisser epsilon of 0.8 appears the most conservative, thus it was chosen for further analysis.

Section 3: Research Question and Hypotheses

The central research question is: “Does the new anxiety treatment significantly reduce symptom levels over five weeks?” The null hypothesis (H0) states that there are no significant differences in anxiety symptom levels across the five weeks. The alternative hypothesis (H1) posits that at least one week demonstrates a significantly different anxiety symptom level. An alpha level of 0.05 was established for our tests.

Section 4: Descriptive Statistics and Results

The descriptive statistics for Weeks 1–5 revealed the following mean anxiety scores: Week 1: 22 (SD = 5.3), Week 2: 19 (SD = 4.8), Week 3: 16 (SD = 4.5), Week 4: 12 (SD = 4.2), and Week 5: 10 (SD = 3.9). These results highlight a clear declining trend in anxiety scores. The estimated marginal means plot visually represents this trend, indicating a significant reduction in anxiety over time.

The SPSS output for the within-subjects effects test resulted in an F value of 15.67 (df = 4, 116) and a p-value of

Section 5: Conclusions and Analysis

The analysis demonstrated that the new anxiety treatment was effective in significantly reducing symptoms over the five-week period. The strengths of repeated measures ANOVA include its ability to control for participant variability by using the same individuals throughout the study, which enhances statistical power. However, limitations such as the potential violation of sphericity, as seen in this analysis, necessitate careful interpretation of results. Future studies could benefit from larger sample sizes or adjustments to address sphericity violations more robustly.

References

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  • Park, J. H., & Kim, J. H. (2019). Repeated measures ANOVA: A review. Journal of Educational Evaluation for Health Professions, 16, 12.
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