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research-design-social

Convert social science research concepts into study designs with hypotheses and pre-analysis plans.

Updated Aug 23, 2026
Search Tags:#research design#mixed methods#causal identification#hypothesis formulation#causal DAG#pre-analysis plan
One-click install
npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill research-design-social
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-design-social
Source: https://github.com/Yuuqq/claude-social-science-skills/tree/main/social-science-skills/research-design-social
Command: npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill research-design-social

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

It solves the problem of turning an abstract research idea into a rigorous social science study design with clear hypotheses, defensible identification strategy, operationalized variables, and a pre-analysis plan.

Core Features & Use Cases

  • Theory → Hypotheses pipeline: Converts theoretical traditions into testable, falsifiable, directional hypotheses and scope conditions.
  • Identification strategy selection: Guides the choice among RCT, DiD, RDD, IV, panel fixed effects, PSM, SCM, and DML based on feasibility and assumptions.
  • Causal DAG and operationalization: Helps construct causal DAGs and translate constructs into measurable variable dictionaries.
  • Pre-analysis plan support: Produces a structured plan to reduce HARKing/p-hacking (with a template referenced in assets).

Quick Start

Use this skill to design a new mixed-methods social science study by providing your research question, target population, candidate theory, and what causal claim you want to make.

Frequently Asked Questions about research-design-social

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert a research concept into a defensible social science study design?▼

A pre-analysis plan structures your empirical tests before data collection to reduce HARKing and p-hacking. It formalizes directional hypotheses, operationalized variable dictionaries, and identification strategies, ensuring your social science research design remains transparent and defensible.

What is the best way to select an identification strategy for causal inference?▼

Constructing causal DAGs visually maps your theoretical causal assumptions, clarifying directional hypotheses and scope conditions. This graphical mapping directly informs the operationalization of constructs into measurable variable dictionaries for rigorous social science studies.

Can I use this approach for both new and existing mixed-methods research designs?▼

Yes, this approach works for starting new quantitative, qualitative, or mixed-methods projects and for refining existing designs. It systematically evaluates identification strategies and validity threats, requiring only your research question, target population, and candidate theory as input.

Do I need Python dependencies like pandas and scipy to formulate a pre-analysis plan?▼

Python dependencies including numpy, pandas, and scipy are required to support the underlying workflow. These libraries facilitate the structured theory-to-hypotheses pipeline, variable dictionary construction, and causal identification decisioning for your social science study design.

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