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 Dependency policy: inspect the current environment first. Do not install packages, clone repositories, or change the user's environment without explicit approval. Prefer a project-local Python virtual environment or R library, and record package versions used for reproducibility. 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.