Survey Frequencies

🏛 Architecture, Planning & Design · Survey Frequencies

Survey Frequencies

Survey · Frekans

Survey Frequencies 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?

Survey Frequencies 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 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 Survey Frequencies.

3

Panel assignments (form fields in the program):

  • Agirlik sutunu: agirlik
  • Degisken: kullanim_sikligi
  • Strat sutun: bolge
  • Kume sutunu: None
  • FPC: None
  • By kolonu: 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 — 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/79_survey_freq_use.xlsx

🎬 Scenario

We estimate the proportions of park-use-frequency categories accounting for design weights and stratification.

⚙️ Variable Selection

  • Variable: use_frequency | Weight: weight | Stratum: region

Data Preview (First 5 Rows)

participant_idregionuse_frequencyweight
1centerper_month single1.2
2environmentnone0.9
3environmentnone0.9
4suburbnone1.1
5suburbnone1.1

n = 400 · Columns: participant_id, region, use_frequency, weight

📈 MerQur Output

SURVEY FREQUENCIES RESULT ───────────────────────────────────────────── “every day” proportion = 0.083 (SE 0.014) | “none” proportion = 0.440 (SE 0.025) Park-use frequency — stratified + weighted design

💬 Interpretation

We estimated the proportion of park-use-frequency categories among users, accounting for the complex sample design. Daily users are 8%, never-users 44% (with design-based standard errors). This is striking — nearly half the population never uses parks; this may point to an access/attractiveness problem. A simple percentage would give the wrong standard error by ignoring stratification and weights; survey frequency analysis produces a confidence interval reflecting the true uncertainty. It is the correct way to estimate use proportions (park use, green-space access) in a population in a design-consistent manner — the basis of green-space policy monitoring.

⚠ 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.

▶ Survey Frequencies — video walkthrough

This section is part of the Karmaşık Anket Tasarımı video (5 analyses in one video). The link below jumps straight to 1:44, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (1:44) 📺 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.