CFA (Confirmatory Factor Analysis)
CFA (Confirmatory Factor Analysis) is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Health 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?
CFA (Confirmatory Factor Analysis) automatically performs all assumption checks required for the data type (normality, homogeneity of variance, etc.) in the background and presents the results in a clear table + chart. Automatic APA 7-formatted interpretation, effect sizes (Cohen’s d, η², R²) and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Health 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 CFA (Confirmatory Factor Analysis).
Panel assignments (form fields in the program):
- Random faktorler:
{...kompleks yapilandirma...} - Yapisal yollar:
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 — Health 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 Health Sciences:
Tip/77_cfa_DASS21_12.xlsx
🎬 Scenario
We test whether a predefined three-factor structure (depression/anxiety/stress — DASS) fits the data. For construct validity, CFA is appropriate.
⚙️ Variable Selection
- Value: depression: depression_1..4
- Value: anxiety: anxiety_1..4
- Value: stress: stress_1..4
Data Preview (First 5 Rows)
| patient_id | depression_1 | depression_2 | depression_3 | depression_4 | anxiety_1 | anxiety_2 | anxiety_3 | anxiety_4 | stress_1 | stress_2 | stress_3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 2 | 3 | 3 | 4 | 4 | 5 | 4 | 2 | 2 | 3 |
| 2 | 3 | 3 | 2 | 3 | 1 | 2 | 2 | 2 | 3 | 4 | 4 |
| 3 | 2 | 3 | 2 | 2 | 4 | 4 | 4 | 4 | 3 | 2 | 2 |
| 4 | 3 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 2 | 2 |
| 5 | 3 | 1 | 2 | 2 | 3 | 3 | 3 | 2 | 3 | 2 | 3 |
n = 280 · Columns (first 12 columns): patient_id, depression_1, depression_2, depression_3, depression_4, anxiety_1, anxiety_2, anxiety_3, anxiety_4, stress_1, stress_2, stress_3
📈 MerQur Output
💬 Interpretation
Unlike EFA, we TESTED whether a pre-defined three-factor structure (depression/anxiety/stress — DASS-style) fits the data with CFA: the fit indices are excellent (CFI = 1.00, RMSEA = 0.00). CFA tests a theoretical scale model — which item loads on which factor is fixed in advance, and the question is “does the model fit the data?”. In medicine it is a mandatory step for confirming the construct validity of a developed mental-health scale (do the DASS dimensions match theory).
⚠ 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 Health Sciences file.
▶ CFA (Confirmatory Factor Analysis) — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 📊 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.