Is it possible to conduct data analysis on a completely anonymous pre-post Perceived Stress Scale survey?

Conducting data analysis on a completely anonymous pre-post Perceived Stress Scale (PSS) survey is possible, but it does pose some challenges in terms of statistical analysis. Since you are unable to match respondents due to ethical concerns and are treating the pre and post surveys as independent samples, the use of an independent t-test is not the most appropriate method for analyzing the data. Here are some suggestions on how to approach your analysis:

Descriptive Statistics: Start by calculating descriptive statistics for both the pre-intervention and post-intervention PSS scores separately. This will give you a sense of the central tendency and variability of the scores in each group.

Visual Inspection: Create box plots or histograms to visualize the distribution of PSS scores for both pre and post surveys. This can help you see if there are any noticeable changes before and after the intervention.

Paired Difference Analysis (if possible): While you mentioned that you were advised not to match respondents, if it's ethically acceptable and feasible, consider conducting a paired difference analysis. In this approach, you calculate the difference between each participant's pre-intervention and post-intervention PSS scores and then analyze these differences. This would provide a more direct assessment of the intervention's impact on individual participants.

Nonparametric Tests: If you cannot perform paired difference analysis and have completely anonymous, unmatched data, consider using nonparametric tests. Wilcoxon signed-rank test is a nonparametric equivalent of the paired t-test, which doesn't require matching of respondents. You can apply this test to evaluate whether there is a significant difference between the pre and post intervention PSS scores.

Effect Size: Regardless of the statistical test you choose, calculate the effect size to quantify the practical significance of any differences you find. Cohen's d is a commonly used measure for effect size in t-tests and Wilcoxon signed-rank tests.

Subgroup Analysis: If you have any demographic or other relevant data on your participants, consider conducting subgroup analyses to see if the intervention had different effects on different groups (e.g., age, gender, years of experience).

Qualitative Data: If available, consider collecting qualitative data through open-ended survey questions or interviews to complement your quantitative analysis. This can provide valuable insights into how the intervention impacted participants' stress levels.

Consult a Statistician: If you are unsure about the appropriate statistical analysis for your data, it's advisable to consult with a statistician or data analyst who can provide tailored guidance based on the specifics of your dataset.

Ka Rivera

thank you @Hugo Assunção, I appreciate your reply.

Muhamad Uyun

one of the psychological measuring tools is a scale, a scale to reveal a person's psychological aspects, in other words what is subjectively felt by that person, the explanation is neither true nor false, which is true if the subject answers what he experienced. so the answer to the question is very possible.

thank you @Muhamad Uyun. Do you have other statistical analysis recommendations for unmatched pre-post data other than what was already mentioned by @Hugo Assunção?

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