Canonical Correlation (CCA)
Canonical Correlation (CCA) 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?
Canonical Correlation (CCA) 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 Canonical Correlation (CCA).
Panel assignments (form fields in the program):
- X seti:
['WBC_K_uL', 'CRP_mg_L', 'kreatinin_mg_dL'] - Y seti:
['AST_U_L', 'Hb_g_dL'] - Bilesen sayisi:
2
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/36_cca_lab_clinical.xlsx
🎬 Scenario
We examine the joint structure between the laboratory-findings set and the ICU clinical-scores set. For the relationship between two multivariate sets, CCA is appropriate.
⚙️ Variable Selection
- X variables: WBC_K_uL
- X variables: CRP_mg_L
- X variables: creatinine_mg_dL
- X variables: AST_U_L
- X variables: Hb_g_dL
- Y variables: APACHE_score
- Y variables: SOFA_score
- Y variables: SAPS_score
- Y variables: GCS
Data Preview (First 5 Rows)
| patient_id | WBC_K_uL | CRP_mg_L | creatinine_mg_dL | AST_U_L | Hb_g_dL | APACHE_score | SOFA_score | SAPS_score | GCS |
|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 6.54 | 1.94 | 0.96 | 53.0 | 10.31 | 0.0 | 11.0 | 20.0 | 12.0 |
| 2.0 | 7.51 | 2.17 | 0.5 | 34.0 | 12.73 | 9.0 | 12.0 | 49.0 | 9.0 |
| 3.0 | 6.78 | 3.03 | 1.22 | 29.0 | 13.56 | 13.0 | 3.0 | 35.0 | 14.0 |
| 4.0 | 5.91 | 1.92 | 1.65 | 35.0 | 13.19 | 7.0 | 2.0 | 27.0 | 15.0 |
| 5.0 | 6.06 | 2.72 | 1.35 | 34.0 | 11.64 | 10.0 | 2.0 | 23.0 | 15.0 |
n = 150 · Columns: patient_id, WBC_K_uL, CRP_mg_L, creatinine_mg_dL, AST_U_L, Hb_g_dL, APACHE_score, SOFA_score, SAPS_score, GCS
📈 MerQur Output
💬 Interpretation
We resolved the joint structure between two multivariate measure sets — laboratory findings (WBC, CRP, creatinine, AST, Hb) and ICU clinical scores (APACHE, SOFA, SAPS, GCS) — with canonical correlation. The first two canonical functions are very strong (r = 0.970 and 0.936, p < .001): the two sets are intensely related. CCA answers “how is one variable set related to another?” in a single step — it is the multivariate-on-both-sides version of multiple regression. In medicine it reveals the latent relationship structure between the laboratory bundle and the clinical score bundle.
⚠ 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.
▶ Canonical Correlation (CCA) — 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.