Time-Series Descriptive
Time-Series Descriptive 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?
Time-Series Descriptive 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 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 Time-Series Descriptive.
Panel assignments (form fields in the program):
- date_col:
tarih - Value Column:
aylik_ziyaret
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/94_ts_monthly_kutuphane.xlsx
🎬 Scenario
Let’s say our university library wants a clear summary of how monthly visits have behaved over the past five years. We have 60 consecutive monthly observations, with each month’s date and the corresponding monthly_visit count. Before fitting any forecasting model, it is good practice to first describe the series: its overall level, range, whether it trends up or down, and whether it shows obvious seasonal swings around exam periods. We use Time Series Description to compute these summary characteristics and visualize the monthly_visit series indexed by date.
⚙️ Variable Selection
- Time index (date): date
- Series (value): monthly_visit
Data Preview (First 5 Rows)
| date | monthly_visit |
|---|---|
| 2020-01-01 00:00:00 | 1388 |
| 2020-02-01 00:00:00 | 1256 |
| 2020-03-01 00:00:00 | 1341 |
| 2020-04-01 00:00:00 | 1695 |
| 2020-05-01 00:00:00 | 1702 |
n = 60 · Columns: date, monthly_visit
📈 MerQur Output
💬 Interpretation
We examined a monthly library-visit series. The ADF test does not find it stationary (p = 0.996) — the mean changes over time, there is a trend (increasing) and seasonality is present (fluctuation tied to academic terms). This pre-diagnosis is critical: most time-series models (like ARIMA) require stationarity, so differencing/ transformation may be needed first. Time-series analysis reveals the structure of long monitoring data — trend, seasonality, stationarity — and tells which modelling steps are required. It is the foundation for analysing educational usage/demand data (library, applications, absenteeism).
⚠ 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.
▶ Time-Series Descriptive — video walkthrough
This section is part of the Zaman Serisi Analizi video (6 analyses in one video). The link below jumps straight to 0:00, where this analysis begins. Narration is in Turkish.
🧪 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 →
- 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.