Cross-Tabulation Analysis
Cross-Tabulation 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?
Cross-Tabulation 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 Cross-Tabulation Analysis.
Panel assignments (form fields in the program):
- error:
cats() got an unexpected keyword argument 'max_unique'
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/28_cross_age_political.xlsx
🎬 Scenario
Suppose we want to describe how political attitudes vary across age groups in our sample of 350 respondents. Each person is classified into an age group (18-30, 31-50, or 51+) and reports a political attitude (conservative, liberal, soloist, or uncertain). Here we are not testing a formal hypothesis so much as building a clear cross-tabulated picture of how attitudes are distributed within each age band, with counts and row percentages. Crosstabulation is exactly the tool for laying out the joint frequency distribution of these two categorical variables.
⚙️ Variable Selection
- Row variable: age_group (18-30/31-50/51+)
- Column variable: political_attitude (conservative/liberal/soloist/uncertain)
Data Preview (First 5 Rows)
| subject_id | age_group | political_attitude |
|---|---|---|
| 1 | 31-50 | uncertain |
| 2 | 18-30 | conservative |
| 3 | 31-50 | soloist |
| 4 | 31-50 | conservative |
| 5 | 51+ | uncertain |
n = 350 · Columns: subject_id, age_group, political_attitude
📈 MerQur Output
💬 Interpretation
We examined the association between age group and attitude/opinion with a crosstab and chi-square. The association is significant but weak (chi-square(6) = 15.81, p = 0.015, Cramer V = 0.15): age is related to attitude, but not strongly — other factors (education, environment) are probably more determining. Reading significance together with effect size matters: in a large sample even a weak association can be significant. The crosstab is the most basic and readable way to see the co-occurrence of two categorical variables; Cramer V gives the true strength.
⚠ 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.
▶ Cross-Tabulation Analysis — video walkthrough
This section is part of the Kategorik Veri Analizleri video (7 analyses in one video). The link below jumps straight to 11:23, where this analysis begins. Narration is in Turkish.
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📚 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.