PLS Regression
PLS Regression is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Education 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, 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 and 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 Education 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_col:
ogrenci_id - x_cols:
[davranis_01, davranis_02, davranis_03, +9 daha] - n_components:
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 — Education 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 Education Sciences:
Egitim_Bilimleri/44_pls_behavior_achievement.xlsx
🎬 Scenario
Suppose we want to predict academic achievement from a battery of twelve correlated classroom behavior indicators (behavior_01 through behavior_12) measured on 200 students. Because these behavior items are highly intercorrelated and there are many of them relative to a single outcome, ordinary regression would be unstable. We use PLS Regression because it extracts latent components from the predictor block that best explain academic_achievement, handling the multicollinearity among the behavior items.
⚙️ Variable Selection
- Predictors (X block): behavior_01, behavior_02, behavior_03, behavior_04, behavior_05, behavior_06, behavior_07, behavior_08, behavior_09, behavior_10, behavior_11, behavior_12
- Target (Y): academic_achievement
Data Preview (First 5 Rows)
| student_id | behavior_01 | behavior_02 | behavior_03 | behavior_04 | behavior_05 | behavior_06 | behavior_07 | behavior_08 | behavior_09 | behavior_10 | behavior_11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 0.132 | 0.388 | -0.665 | -1.046 | 0.965 | -1.207 | -0.574 | 1.847 | -0.169 | 0.177 | -0.125 |
| 2.0 | -0.483 | 0.018 | 1.739 | 2.137 | -1.326 | 0.855 | -1.007 | -2.324 | 1.442 | -0.318 | 0.22 |
| 3.0 | 1.058 | -0.516 | -0.46 | 0.013 | -0.746 | 0.643 | 0.93 | 0.534 | -0.349 | -0.972 | 0.422 |
| 4.0 | 1.56 | -0.134 | -1.36 | -1.465 | 0.545 | 1.194 | 2.086 | 0.426 | -1.181 | -0.207 | -0.603 |
| 5.0 | 0.591 | 0.163 | -0.943 | 0.06 | 0.469 | -0.356 | 0.495 | -0.3 | -0.698 | -0.256 | 0.098 |
n = 200 · Columns (first 12 columns): student_id, behavior_01, behavior_02, behavior_03, behavior_04, behavior_05, behavior_06, behavior_07, behavior_08, behavior_09, behavior_10, behavior_11
📈 MerQur Output
💬 Interpretation
We predicted academic achievement from 12 inter-correlated behavior items with PLS (Partial Least Squares) regression. When the behavior items are correlated, ordinary regression becomes unstable; PLS solves this by reducing the items to a few orthogonal components. The model is very strong both in training (R2 = 0.91) and cross-validation (R2_CV = 0.90) — no overfitting, excellent generalisation. When there are many correlated items/behaviors (observation scales, survey batteries), PLS is the preferred method that keeps both predictive power and interpretability.
⚠ 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 Education Sciences file.
▶ PLS Regression — video walkthrough
This section is part of the İleri Düzey I — Gelişmiş Regresyon Modelleri video (8 analyses in one video). The link below jumps straight to 9:15, where this analysis begins. Narration is in Turkish.
🧪 Natural Sciences & Mathematics · 🏛 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.