PLS Regression

Regression · Multivariate

PLS Regression 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?

PLS Regression 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 PLS Regression.

Panel assignments (form fields in the program):

Y Sutunu: progresyon_skor
Bagimsiz (X): [gen_01, gen_02, gen_03, ... +9 adet]
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/44_pls_gene_progression.xlsx

🎬 Scenario

We predict a disease progression score from 12 gene expressions. For many highly correlated predictors, PLS is appropriate.

⚙️ Variable Selection

Dependent variable: progression_score
Predictor(s): gene_01..12

Data Preview (First 5 Rows)

patient_id	gene_01	gene_02	gene_03	gene_04	gene_05	gene_06	gene_07	gene_08	gene_09	gene_10	gene_11
1.0	1.302	-0.649	0.454	0.046	0.477	-0.319	-0.659	0.923	0.223	0.361	-0.361
2.0	1.101	-2.145	-0.06	-0.178	1.986	-1.169	-1.268	0.729	-1.647	-0.457	0.933
3.0	0.303	0.148	-0.078	-0.013	-0.258	-0.167	-0.105	0.007	0.387	-0.115	-0.022
4.0	-0.435	0.022	-0.369	0.859	0.661	-0.346	-0.504	-0.247	-0.285	-1.047	0.147
5.0	0.124	-0.29	0.245	1.094	0.67	0.089	-1.163	-0.025	-0.414	-0.95	0.765

n = 200 · Columns (first 12 columns): patient_id, gene_01, gene_02, gene_03, gene_04, gene_05, gene_06, gene_07, gene_08, gene_09, gene_10, gene_11

📈 MerQur Output

PLS REGRESSION RESULT ───────────────────────────────────────────── R^2 (train) = 0.894 R^2 (5-fold CV) = 0.880 outcome: progression_score predictors: gene_01..12 (12 genes)

💬 Interpretation

We predicted a disease progression score from 12 gene expressions. Because the gene expressions are highly correlated (multicollinearity), classic regression becomes unstable; PLS reduces them to a few latent components and regresses on those. With cross-validated R^2 = 0.88, the model is both strong and generalizable. PLS is ideal when predictors are numerous or highly correlated; in genomic/omics data (predicting an outcome from many genes) it is widely used.

⚠ 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.

▶ PLS Regression — 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.