MR × Categorical Analysis
MR × Categorical Analysis is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Education Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
MR × Categorical Analysis automatically performs all required assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automatic interpretation in the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Education Sciences domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select MR × Categorical Analysis.
Panel assignments (form fields in the program):
- source_col:
aktiviteler - Group Column:
cinsiyet - mc_metadata:
None
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Education Sciences
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Education Sciences:
Egitim_Bilimleri/30_mr_categorical_activity_sex.xlsx
🎬 Scenario
Suppose we want to know whether the kinds of extracurricular activities students choose differ between boys and girls. We surveyed 220 students who could each pick several activities from a multi-select list, and we also recorded each student’s sex (male/female). The research question is how participation in each activity breaks down by sex. A Multiple Response Crosstab is the right approach because it cross-tabulates a multiple- response set against a single categorical grouping variable, showing counts and percentages of each activity within each sex.
⚙️ Variable Selection
- Multiple-response set (comma-separated): activities
- Crosstab (grouping) variable: sex (male/female)
- Response delimiter: comma
Data Preview (First 5 Rows)
| student_id | sex | activities |
|---|---|---|
| 1 | male | sport |
| 2 | male | sport, computer |
| 3 | female | sport, music |
| 4 | male | sport |
| 5 | female | music, computer |
n = 220 · Columns: student_id, sex, activities
📈 MerQur Output
💬 Interpretation
We cross-tabulated extracurricular activities (multiple-choice) by the student’s sex. With data from 220 students we can see how each activity is distributed across sexes. The multiple-response crosstab answers “which group ticks which options more” — e.g. girls lead in one activity, boys in another. This reveals the sex patterns in activity preference and guides inclusive activity planning.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Education Sciences file.
▶ MR × Categorical Analysis — video walkthrough
This section is part of the Çok Seçimli Yanıtlar video (4 analyses in one video). The link below jumps straight to 1:54, where this analysis begins. Narration is in Turkish.
🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.