PCA (Principal Components Analysis)

Boyut İndirgeme

PCA (Principal Components 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?

PCA (Principal Components 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 PCA (Principal Components Analysis).

Panel assignments (form fields in the program):

Columns: [yas, BMI, sistolik_KB, ... +3 adet]
Bilesen sayisi: 3
Missing strategy: drop

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/69_pca_clinical_6ozellik.xlsx

🎬 Scenario

We reduce six correlated clinical indicators to a few components with PCA. For dimensionality reduction, PCA is appropriate.

⚙️ Variable Selection

Variables: age
Variables: BMI
Variables: systolic_bp
Variables: glucose
Variables: cholesterol
Variables: HbA1c

Data Preview (First 5 Rows)

patient_id	age	BMI	systolic_bp	glucose	cholesterol	HbA1c
1.0	84.0	36.07	179.0	174.0	272.0	8.71
2.0	20.0	13.88	76.0	25.0	90.0	4.5
3.0	76.0	33.5	145.0	143.0	256.0	8.43
4.0	62.0	26.62	123.0	84.0	184.0	6.91
5.0	55.0	28.83	131.0	105.0	221.0	6.85

n = 180 · Columns: patient_id, age, BMI, systolic_bp, glucose, cholesterol, HbA1c

📈 MerQur Output

PCA (PRINCIPAL COMPONENTS ANALYSIS) RESULT ───────────────────────────────────────────── PC1 explains 91.9% of variance variables: age, BMI, systolic_bp, glucose, cholesterol, HbA1c

💬 Interpretation

We reduced six correlated clinical indicators to a few components with PCA: the first component alone explains 91.9% of variance — so these six measures largely reflect a single latent dimension (general metabolic/cardiovascular risk). PCA transforms correlated variables into mutually independent components; it reduces dimensions, eases visualization and resolves multicollinearity. In medicine it is fundamental for summarizing multi-indicator clinical data and building a “composite risk index”.

⚠ 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.

▶ PCA (Principal Components Analysis) — video walkthrough

A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.

▶ Watch the video 📺 All videos

🔀 The same analysis in other disciplines:
🎓 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 →

Sources:

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.