Assignment task:
Valuation Question
Do youth participating in the program demonstrate a significant reduction in perceived stress from intake to discharge? Need Assignment Help?
This evaluation question directly examines change over time within the same group of participants, allowing for a comparison of pre-intervention and post-intervention scores. The primary objective of the analysis is to determine whether program involvement is associated with measurable improvement.
Determining the Appropriate Statistical Test
To analyze pre- and post-intervention scores, the analysis will use either a paired samples t-test or a Wilcoxon Signed-Rank test. Both statistical procedures assess whether there is a meaningful difference between two related sets of scores, but the decision on which test to apply depends on the distributional characteristics of the data.
Assumption Checking
Before selecting the appropriate test, the difference scores (pre minus post) will be evaluated for basic assumptions:
Normality:
The Shapiro-Wilk test will be used to determine whether the distribution of difference scores meets normality assumptions. Visual methods such as histograms and Q-Q plots will provide additional assessment of skewness and kurtosis (Field, 2018).
Outliers:
Boxplots and standardized residual values will be used to identify potential outliers. Any outlier detected will be examined to determine whether it represents a legitimate extreme value or a data entry error.
Criteria for Statistical Procedure Selection
If the normality assumptions are met (i.e., the difference scores are normally distributed), a paired samples t-testwill be conducted to compare the mean pre- and post-intervention scores.
If the normality assumptions are violated (e.g., significant skew indicated by Shapiro-Wilk results), the Wilcoxon Signed-Rank test will be used as a nonparametric alternative because it evaluates median differences and does not require normally distributed scores (Kline, 2015).
This approach ensures that the data analysis aligns with the underlying statistical assumptions, increasing the validity of the findings.
Dataset Readiness
Before running analyses, the dataset must be examined to determine whether it is in final form and free of concerns that may compromise accuracy.
Missing Data
Cases missing either pre-intervention or post-intervention scores will be identified. Because change over time is required to answer the evaluation question, participants missing one of the two scores will likely be removed using listwise deletion. The number of excluded cases will be documented.
Outliers
Any extreme values will be reviewed to determine whether they are true reflections of participant scores or errors in data entry. Decisions regarding retention or removal will be clearly justified in the Method section of the report.
Presentation of Results
The results will be presented in a format that clearly communicates statistical outcomes to program stakeholders. The following presentation methods are recommended:
A descriptive statistics table summarizing pre-intervention and post-intervention scores
The results of the selected inferential test (paired t-test or Wilcoxon Signed-Rank test) with effect size
A graph displaying pre- and post-intervention values, such as a bar graph or line graph
Including a visual representation of findings will support interpretation and improve clarity, especially for audiences less familiar with statistical terminology.
Results will not be reported in this discussion, as they will be included in the formal results section in Week 6.
References:
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.