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MR × MR Analysis

Research Disciplines → Agriculture, Forestry & Aquatic → MR × MR Analysis

🌾 Agriculture, Forestry & Aquatic · MR × MR Analysis

MR × MR Analysis

Kategorik · MR

MR × MR Analysis is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Agriculture, Forestry & Aquatic 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?

MR × MR 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 and chart. Automatic interpretation in 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 Agriculture, Forestry & Aquatic 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 MR × MR Analysis.

3

Panel assignments (form fields in the program):

  • source_col1: None
  • source_col2: None
  • mc_metadata: None
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 — Agriculture, Forestry & Aquatic

ℹ Note: The MerQur output and interpretation tables on this page are illustrative, intended to help readers understand the analysis. Numeric results from your own data may differ; this page does not need to match the YouTube video walkthrough exactly.

🎬 File Used in the Video

The YouTube video for this page was recorded for the Agriculture, Forestry & Aquatic domain using the sample file below:

31_MR_×_MR_Analiz / Ziraat_Orman_Muhendislik__31_mr_mr.xlsx

Topic: MR × MR Analysis analysis applied to a real Agriculture, Forestry & Aquatic sample dataset.

Data Preview (First 5 Rows)

ornek_idgrup_agrup_b
1A, C, DZ, W
2BX, W
3B, CZ, W
4DZ
5B, CX

n = 180 · Columns: ornek_id, grup_a, grup_b

MerQur Output (Illustrative)

MR × MR ANALYSIS RESULT ───────────────────────────────────────────── Veri: n = 180, 3 columns Test statistic: 12.43 p-value: < .001 Effect size: medium-large Detailed results are produced automatically by MerQur’s ▶ Run command.

MerQur Narrative Interpretation

MR × MR Analysis revealed a statistically significant finding among the variables (p < .001). Detailed results and visualizations are produced automatically in the MerQur output panel.

APA 7 Interpretation (Academic Format · Illustrative)

MR × MR Analysis results indicated that grup_a showed a statistically significant effect/association (p < .001). The effect size was medium-to-large, suggesting the finding has practical importance. For the detailed report, use MerQur’s APA 7 Word export.

⚠ 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 Agriculture, Forestry & Aquatic file.

▶

The video will be uploaded to the MerQur YouTube channel soon

📺 MerQur YouTube Channel

🔀 The same analysis in other disciplines:
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences

📚 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.