Mann-Kendall + Sen’s Slope

🏛 Architecture, Planning & Design · Mann-Kendall + Sen’s Slope

Mann-Kendall + Sen’s Slope

Zaman Serisi · Trend
🆕 New in v1.0.5: A Chart tab was added to this analysis (boxplot, interaction, profile, bar, scatter, biplot, or forecast — depending on analysis type).

Mann-Kendall + Sen’s Slope 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?

Mann-Kendall + Sen’s Slope 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 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

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 Mann-Kendall + Sen’s Slope.

3

Panel assignments (form fields in the program):

  • Value Column: ortalama_sicaklik_C
  • Tarih sutunu: yil
  • Zaman birimi: year
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 — 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/98_mann_kendall_temperature_trend.xlsx

🎬 Scenario

We test the long-term temperature trend (urban heat island/climate) with the nonparametric Mann-Kendall + Sen’s slope.

⚙️ Variable Selection

  • Value: mean_temperature_C

Data Preview (First 5 Rows)

yearmean_temperature_C
1985.015.22
1986.015.78
1987.015.27
1988.015.37
1989.015.98

n = 40 · Columns: year, mean_temperature_C

📈 MerQur Output

MANN-KENDALL + SEN’S SLOPE RESULT ───────────────────────────────────────────── p < .001 *** Trend = INCREASING Mean temperature long-term trend

💬 Interpretation

We tested whether mean temperature shows a significant long-term trend with the Mann-Kendall test — a nonparametric, outlier- and distribution-robust trend test. The result is highly significant (p < .001) and the direction is increasing: temperature is rising consistently over the years. This is a concrete indicator of the urban heat island and climate change. It is critical for landscape architecture: rising temperature makes green infrastructure’s cooling service (shade, evapotranspiration) even more important — the rationale for climate- adaptive design. Mann-Kendall + Sen’s slope is the gold standard for long-term trend detection in climate/ environmental series.

⚠ 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.

▶ Mann-Kendall + Sen’s Slope — video walkthrough

This section is part of the Zaman Serisi Analizi video (6 analyses in one video). The link below jumps straight to 6:24, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (6:24) 📺 All videos

📚 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.