# Education Quantitative Study Design

> Use for education papers using quantitative survey, correlational, regression, experimental, quasi-experimental, causal-comparative, mediation/moderation, SEM, multilevel, or longitudinal designs. Produces variables, hypotheses, sampling, instruments, power/sample-size guidance, statistical plan, and methods-section structure.

- Skill: `ilog3/education-quantitative-study-design` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ilog3/education-quantitative-study-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ilog3/education-quantitative-study-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ilog3 (https://skillmd.com/u/ilog3)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/ilog3/education-quantitative-study-design

---


# Education Quantitative Study Design

## Goal

Create a defensible quantitative research design aligned with research questions and variables.

## Inputs

- Research questions/hypotheses
- Population and setting
- Available data or planned data collection
- Variable model from `education-variable-identification`
- Desired design: survey, experiment, quasi-experiment, regression, SEM, multilevel, longitudinal

## Workflow

1. Confirm design type and feasibility.
2. Define population, sampling, inclusion/exclusion, and sample size logic.
3. Finalize IV/DV/mediator/moderator/control variables.
4. Identify instruments/scales/tests and reliability/validity evidence.
5. Specify data collection procedure.
6. Specify statistical analysis plan.
7. Produce a method-section skeleton.

## Tool Calls

Sample size/power:

```text
G*Power: https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower
```

Open-source analysis:

```r
install.packages(c("tidyverse", "psych", "car", "lavaan", "lme4", "effectsize", "mediation"))
```

No-code/low-code:

```text
jamovi: https://www.jamovi.org/
JASP: https://jasp-stats.org/
```

## Output Format

| Component | Design Decision | Rationale | Tool/Analysis |
|---|---|---|---|

Include:

- Hypotheses
- Variable table
- Sampling plan
- Instrument table
- Statistical analysis plan
- Method section draft

## Quality Rules

- Causal claims require experimental/quasi-experimental logic.
- For pre/post studies, include baseline equivalence and appropriate controls.
- For nested education data, consider multilevel modeling.