Correlation (Pearson / Spearman / Kendall)
Correlation (Pearson / Spearman / Kendall) is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Natural Sciences & Mathematics 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?
Correlation (Pearson / Spearman / Kendall) 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 Natural Sciences & Mathematics 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 Correlation (Pearson / Spearman / Kendall).
Panel assignments (form fields in the program):
- Columns:
[ornek_id, pH, sicaklik_C, +3 daha] - method:
pearson
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 — Natural Sciences & Mathematics
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 Natural Sciences & Mathematics:
Fen_Matematik/33_correlation_5fizikokimyasal.xlsx
🎬 Scenario
We examine all pairwise correlations among five physicochemical variables. For relationship direction and strength, the correlation matrix is appropriate.
⚙️ Variable Selection
- Variables: pH
- Variables: temperature_C
- Variables: concentration_mM
- Variables: activity
- Variables: yield_pct
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- p-values are now produced for ALL methods (Pearson/Spearman/Kendall), not only Pearson.
- Hypothesis direction (two-sided / right / left).
- Multiple-comparison p-adjustment across pairs: Bonferroni / Holm / FDR (Benjamini-Hochberg).
- Confidence interval for r via Fisher z (Pearson/Spearman) or Kendall SE.
Data Preview (First 5 Rows)
| sample_id | pH | temperature_C | concentration_mM | activity | yield_pct |
|---|---|---|---|---|---|
| 1.0 | 8.2 | 59.6 | 2.17 | 0.977 | 100.0 |
| 2.0 | 4.8 | 42.8 | 6.05 | 0.866 | 100.0 |
| 3.0 | 7.25 | 56.4 | 22.37 | 1.0 | 100.0 |
| 4.0 | 6.79 | 58.0 | 41.81 | 1.0 | 100.0 |
| 5.0 | 8.55 | 60.0 | 5.35 | 1.0 | 100.0 |
n = 180 · Columns: sample_id, pH, temperature_C, concentration_mM, activity, yield_pct
📈 MerQur Output
💬 Interpretation
We computed all pairwise Pearson correlations among five physicochemical variables: only pH and temperature were strongly positively related (r = 0.79, p < .001); the other pairs were weaker. So pH and temperature rise together, while the remaining measures carry largely separate information. The correlation matrix summarizes the direction and strength of relationships at a glance; in the lab it is the first step in spotting overlap among indicators (multicollinearity risk) and seeing which variables truly give separate signals. >> ADVANCED PARAMETERS (optional in the form — what they do): – p-values are now produced for ALL methods (Pearson/Spearman/Kendall), not only Pearson. – Hypothesis direction (two-sided / right / left). – Multiple-comparison p-adjustment across pairs: Bonferroni / Holm / FDR (Benjamini-Hochberg). – Confidence interval for r via Fisher z (Pearson/Spearman) or Kendall SE.
⚠ 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 Natural Sciences & Mathematics file.
▶ Correlation (Pearson / Spearman / Kendall) — video walkthrough
This section is part of the İlişki ve Korelasyon video (6 analyses in one video). The link below jumps straight to 0:00, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🏛 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.