Bland-Altman Analysis

Korelasyon · Uyum

Bland-Altman Analysis 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?

Bland-Altman Analysis automatically performs all required 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 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 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 Bland-Altman Analysis.

Panel assignments (form fields in the program):

method1_col: ogrenci_id
method2_col: IQ_WISC

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/34_bland_altman_IQ_test.xlsx

🎬 Scenario

Suppose a school psychology unit wants to know whether two intelligence tests can be used interchangeably. The same 60 students were assessed with both the WISC and the Stanford-Binet, giving us two IQ scores per child that should, in principle, measure the same construct. The question is not whether the scores correlate but whether they actually agree. We use a Bland-Altman analysis because it plots the difference between the two methods against their average and reports the mean bias and limits of agreement, which is exactly how method comparison should be evaluated.

⚙️ Variable Selection

Measurement 1 (M1): IQ_WISC
Measurement 2 (M2): IQ_Stanford_Binet

Data Preview (First 5 Rows)

student_id	IQ_WISC	IQ_Stanford_Binet
1	92	97
2	114	118
3	97	84
4	99	90
5	112	115

n = 60 · Columns: student_id, IQ_WISC, IQ_Stanford_Binet

📈 MerQur Output

BLAND-ALTMAN ANALYSIS RESULT ───────────────────────────────────────────── WISC mean = 100.18 | Stanford-Binet mean = 97.98 | Bias ~ +2.2 (methods agree largely)

💬 Interpretation

We applied two different IQ tests — WISC and Stanford-Binet — to the same students and compared them with Bland-Altman. This method does not look at correlation (high correlation does not guarantee agreement); it measures the difference between the two measurements. The means are very close (100.2 vs 98.0), the bias about 2 points — the two tests give almost the same result systematically and are practically interchangeable. Bland-Altman is the standard way to assess the agreement of two measurement tools (two tests, two raters, old/new method); it is ideal for showing test equivalence in educational measurement.

⚠ 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.

▶ Bland-Altman Analysis — video walkthrough

This section is part of the İlişki ve Korelasyon video (6 analyses in one video). The link below jumps straight to 2:17, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (2:17) 📺 All videos

🔀 The same analysis in other disciplines:
🧪 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 →

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.