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PCA (Principal Components Analysis)

Research Disciplines → Social, Humanities & Admin Sciences → PCA (Principal Components Analysis)

📊 Social, Humanities & Admin Sciences · PCA (Principal Components Analysis)

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 Social, Humanities & Admin 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 applies in the background all the assumption checks required for the relevant data type (normality, homogeneity of variance, etc.) and presents the results with a clear table + chart. Automatic interpretation to 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 Social, Humanities & Admin 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

1

Load the data. Select the sample file from File → Open. MerQur auto-detects column types.

2

Select the analysis. From the left side select PCA (Principal Components Analysis).

3

Panel assignments (form fields in the program):

  • Columns: [ogrenci_id, matematik, fen, +3 daha]
  • n_components: 3
  • Missing strategy: drop
4

Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).

5

▶ Run — click the button. Results are produced automatically as a table + chart.

6

📄 Export to Word. APA 7-formatted report with italic statistical symbols.

📊 Sample Dataset — Social, Humanities & Administrative 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 Social, Humanities & Administrative Sciences:

Sosyal_Beseri/69_pca_6ozellik.xlsx

🎬 Scenario

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

⚙️ Variable Selection

  • Variables: math
  • Variables: science
  • Variables: language
  • Variables: reading
  • Variables: logic
  • Variables: art

Data Preview (First 5 Rows)

Preview unavailable: [Errno 2] No such file or directory: ‘F:/MERQUR_CODE/merqur/english/datasets/Sosyal_Beseri/69_pca_6ozellik.xlsx’

📈 MerQur Output

PCA (PRINCIPAL COMPONENTS ANALYSIS) RESULT ───────────────────────────────────────────── PC1 explains 88.3% of variance variables: math, science, language, reading, logic, art

💬 Interpretation

We reduced six correlated achievement indicators to a few components with PCA: the first component alone explains 88% of variance — so these six course scores largely reflect a single latent dimension (general academic ability). PCA transforms correlated variables into mutually independent components; it reduces dimensions, eases visualization and resolves multicollinearity. In education it is fundamental for summarizing multi-course achievement and building a “general achievement 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 Social, Humanities & Admin 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 · 🏥 Health 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:
  1. American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
  2. Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
  3. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.