MANOVA
MANOVA is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design 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?
MANOVA automatically applies all necessary assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automated 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 Architecture, Planning & Design 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 MANOVA.
Panel assignments (form fields in the program):
- Group Column:
tasarim_stili - Bagimli degiskenler:
['memnuniyet', 'kalis_dk', 'tekrar_ziyaret_oran']
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 — Architecture, Planning & Design
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 Architecture, Planning & Design:
Peyzaj_Mimarligi/09_manova_style_3DV.xlsx
🎬 Scenario
We test design style’s effect on three correlated user-experience variables (satisfaction, stay duration, repeat-visit ratio) at once. Multiple related DVs call for MANOVA.
⚙️ Variable Selection
- Factor: design_style
- Dependent variables: satisfaction, stay_min, repeat_visit_ratio
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Reference test for the overall decision: Wilks / Pillai (most robust) / Hotelling-Lawley / Roy.
- Box’s M: equality of covariance matrices across groups.
- Univariate follow-up ANOVAs (one per dependent variable).
- Multivariate partial eta-squared; per-group descriptive means.
Data Preview (First 5 Rows)
| park_id | design_style | satisfaction | stay_min | repeat_visit_ratio |
|---|---|---|---|---|
| 1 | modern | 3.29 | 26.8 | 0.588 |
| 2 | modern | 3.27 | 42.3 | 0.523 |
| 3 | modern | 4.21 | 41.7 | 0.472 |
| 4 | modern | 3.81 | 25.1 | 0.485 |
| 5 | modern | 2.83 | 24.4 | 0.56 |
n = 120 · Columns: park_id, design_style, satisfaction, stay_min, repeat_visit_ratio
📈 MerQur Output
💬 Interpretation
Here we have three correlated user-experience variables — satisfaction, stay duration, repeat-visit ratio — and one design-style factor. Running three separate ANOVAs would inflate the error rate and ignore the correlations among variables. MANOVA tests all three jointly: Wilks’ Lambda F = 23.33, p < .001, so design style strongly shifts the multivariate user-experience profile. MANOVA is the right approach when a design intervention affects several related outcomes at once. After a significant MANOVA, follow-up univariate tests show which experience dimension drives the effect. >> ADVANCED PARAMETERS (optional in the form — what they do): – Reference test for the overall decision: Wilks / Pillai (most robust) / Hotelling-Lawley / Roy. – Box’s M: equality of covariance matrices across groups. – Univariate follow-up ANOVAs (one per dependent variable). – Multivariate partial eta-squared; per-group descriptive means.
⚠ 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 Architecture, Planning & Design file.
▶ MANOVA — video walkthrough
This section is part of the Veriye İlk Bakış ve Parametrik Testler video (14 analyses in one video). The link below jumps straight to 18:36, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ 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.