Bland-Altman Analizi

🌾 Ziraat, Orman ve Su Ürünleri · Bland-Altman Analizi

Bland-Altman Analizi

Korelasyon · Uyum

Bland-Altman Analysis is one of the statistical analyses automatically performed in MerQur. This page illustratively demonstrates how the analysis is applied to a sample dataset from the field of Agriculture, Forestry & Fisheries, how the MerQur output appears, and how it is reported in APA 7 format.

🎯 What is it for?

Bland-Altman Analysis 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. 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?

  • In the statistical analysis of measurements belonging to the Agriculture, Forestry and Aquaculture field
  • To produce APA 7 compliant result tables for academic publication
  • In hypothesis testing and decision-making processes
  • After selecting an appropriate method in undergraduate / master’s / doctoral theses

📐 Assumptions

  • Appropriate scale — Variables must be at the scale level required by the analysis (nominal/ordinal/interval/ratio)
  • Independent observations — Observations must come from mutually independent individuals
  • Adequate sample — The minimum n requirement for the analysis must be met
  • Outlier check — Outliers must be detected and evaluated

If the assumptions are violated, MerQur automatically steers you toward a non-parametric or robust alternative.

🛠 How to do it in MerQur

1

Load the data. Select the sample file from the File → Open menu. MerQur automatically detects the column types.

2

Select the analysis. From the left side, select Bland-Altman Analysis.

3

Panel assignments (form fields in the program):

  • method1_col: agac_id
  • method2_col: klinometre_m
4

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

5

Press the ▶ Run button. The results are automatically produced as a table + chart.

6

📄 Export to Word. A report in APA 7 format with italic statistical symbols.

📊 Sample Dataset — Ziraat, Orman ve Su Ürünleri

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

🎬 Example File Used in the Video

The YouTube video narration for this page was recorded using the following sample file for the Agriculture, Forestry and Aquaculture field:

MerQur_Hoca_Tanitim/Ziraat_Orman_Su/34_bland_altman_klinometre_lazer.xlsx

Topic: The application of Bland-Altman Analysis on a real sample data set belonging to the Agriculture, Forestry and Aquaculture field.

Data Preview (First 5 Rows)

agac_idDBH_klinometreDBH_lazer
1.0030.2527.37
2.0024.1220.98
3.0023.6623.78
4.0022.9723.16
5.0020.4221.41

n = 60 · Sütunlar: agac_id, DBH_klinometre, DBH_lazer

MerQur Output (Illustrative)

KORELASYON MATRİSİ (Pearson) ───────────────────────────────────────────── agac_id DBH_klinometre agac_id 1.000 0.647*** DBH_klinometre 0.647*** 1.000 *** p < .001 · n = 60

MerQur Interpretation (Plain Language)

A positive, moderate-to-strong relationship was found between agac_id and DBH_klinometre (r = .65, p < .001). An increase in one variable is observed together with an increase in the other.

APA 7 Interpretation (Academic Format · Illustrative)

The Pearson correlation analysis revealed a statistically significant, positive, and moderate-to-strong relationship between the agac_id and DBH_klinometre variables, r(58) = .65, p < .001. The coefficient of determination (r² = .42) indicates that approximately 42% of the variance in the DBH_klinometre variable is explained by agac_id.

⚠ Common Mistakes

  • Specifying the data type incorrectly (e.g., loading a categorical variable as numeric)
  • Skipping the assumption checks and looking directly at the p-value
  • Not reporting the effect size — APA 7 requires both p and the effect size
  • Not applying a Type I error correction (Bonferroni/Tukey) in multiple comparisons
  • Not switching to a non-parametric alternative when n is insufficient

📹 Video Walkthrough

You can watch the video below for an end-to-end application of this analysis using the Agriculture, Forestry and Aquaculture file.

▶

The video will soon be uploaded to the MerQur YouTube channel

📺 MerQur YouTube Channel

📚 If You Used This Analysis, Cite MerQur

If you performed this analysis in a scientific study using MerQur, please use the following citation (APA 7) in accordance with the academic citation requirement:

Örücü, Ö. K. (2026). MerQur: An Integrated Academic Data Analysis and Reporting Platform [Computer Software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001

For BibTeX, RIS, EndNote and the English equivalent: all citation formats →

References:
  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.