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December 3, 2025
December 3, 2025

Statistical Analyses Plan

Author By Academic Wizard Categories Blog
Statistical Analyses Plan

Explain the statistical analyses that you will use to analyze the data in order to answer each of your evaluation questions.

  • How will you determine which statistical analyses you will use to answer your evaluation question? In your response, explain how you will decide between a paired samples t-test and the Wilcoxon Signed-Rank test. This includes checking whether your pre–post difference scores meet basic assumptions (such as approximate normality) and then selecting the test that best fits your data.
  • Is your dataset in final form? If not, what additional steps still need attention? Consider whether you need to address:
  • Missing data
  • Outliers
  • Variable labeling or recoding
  • What would be the best way to present the results in your report? You may want to consider using some tables or other graphics to help illustrate the results.

Statistical Analyses Plan

This discussion will help you prepare for the week 6 written assignment, where you will formally report and interpret your results.

  • Explain the statistical analyses that you will use to analyze the data in order to answer each of your evaluation questions., How will you determine which statistical analyses you will use to answer your evaluation question?, Explain how you will decide between a paired samples t-test and the Wilcoxon Signed-Rank test., Is your dataset in final form? If not what additional steps still need attention?, What would be the best way to present the results in your report?

Comprehensive General Response

1. Explaining the statistical analyses to answer your evaluation questions

You should select statistical methods that directly align with the type of evaluation questions you are asking and the structure of your data. Common evaluation questions fall into several categories:

a. Change over time (pre–post or baseline–follow-up):
If you are measuring the same participants before and after an intervention, you are analyzing within-person change. Two major analytical approaches apply:

  • Paired samples t-test (parametric): Used to examine whether the mean difference between pre and post scores is statistically significant.

  • Wilcoxon Signed-Rank test (non-parametric): Used when pre–post differences violate the normality assumption or when the measurement scale is ordinal.

b. Group comparisons (e.g., intervention vs. control):
If you compare outcomes between two independent groups:

  • Independent samples t-test (parametric): Compares group means when data is approximately normal and measured at interval/ratio levels.

  • Mann–Whitney U test (non-parametric): Used when normality is violated or data is ordinal.

c. Multiple comparisons (more than two groups OR multiple outcomes at one time):

  • ANOVA / repeated measures ANOVA: Used when comparing more than two conditions or multiple time points.

  • Kruskal–Wallis or Friedman test: Non-parametric equivalents when assumptions are violated.

d. Relationship or association questions (correlations):

  • Pearson correlation: Used when both variables are continuous and normally distributed.

  • Spearman correlation: Used when variables are ordinal or non-normal.

Each question dictates which test is appropriate. For example:

  • “Did participants’ stress levels significantly decrease after using the new wellness app?” → Paired samples t-test or Wilcoxon Signed-Rank.

  • “Do students in Program A perform differently than students in Program B?” → Independent samples t-test or Mann–Whitney U.

  • “Is motivation related to satisfaction?” → Pearson or Spearman correlation.

2. Determining which statistical analysis to use

Your decision flows from a combination of:

  • The question type (change, relationship, comparison).

  • The type and structure of your variables.

  • Whether data meet model assumptions.

A general decision process:

  1. Identify the variables: Are they continuous, ordinal, or categorical?

  2. Determine measurement level: e.g., Likert scores (ordinal), test scores (interval), binary status (categorical).

  3. Check assumptions using exploratory data analysis:

    • Normal distribution (histograms, Q–Q plots, Shapiro–Wilk test)

    • Equality of variances (Levene’s test for group comparisons)

    • Independence (appropriate study design)

  4. Select the analysis that matches both the question and the nature of the data.