An expert AI co-pilot for psychological researchers that automates the transition from experimental design to APA-formatted statistical results.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install psychology-data-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install psychology-data-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Psychology Data Analysis Assistant?
The Psychology Data Analysis Assistant is a specialized tool within the Openclaw Skills ecosystem designed to streamline the complex lifecycle of psychological research. It serves as a decision-making engine that evaluates research designs—such as between-subjects, within-subjects, or mixed designs—to recommend the most robust statistical tests. By bridging the gap between raw data and academic reporting, it ensures that researchers adhere to rigorous methodology and assumption checking.
This skill provides a dual-track execution model, catering to both traditional SPSS users and modern Python-based data scientists. Whether you are conducting simple t-tests or complex mediation and moderation analyses with Bootstrap methods, the assistant provides step-by-step guidance, automated code execution, and precise result interpretation tailored to the standards of psychological science.
Psychology Data Analysis Assistant Use Cases
- Determining the correct statistical test based on variable types and experimental levels.
- Automating data cleaning and hypothesis testing using Python scripts.
- Generating step-by-step SPSS menu navigation and syntax guides for students and researchers.
- Interpreting complex statistical outputs including p-values, effect sizes (Cohen's d, η²), and 95% confidence intervals.
- Converting raw statistical output into publication-ready APA format text.
How Psychology Data Analysis Assistant Works
- Information Gathering: The skill prompts for research design, variable types (X, Y, covariates), sample size, and software preference.
- Statistical Recommendation: It matches the research architecture against a built-in decision tree to suggest tests like ANOVA, MANOVA, or Regression.
- Assumption Verification: Performs critical pre-test checks including Shapiro-Wilk for normality and Levene’s test for homogeneity of variance.
- Analysis Execution: Executes the analysis via the integrated Python script or provides a detailed manual guide for SPSS users.
- Interpretation and Reporting: Synthesizes the results into a final report, validating original hypotheses and providing APA-formatted summaries.
Psychology Data Analysis Assistant Setup
To integrate this psychology analysis tool into your environment, ensure you have the core Openclaw Skills runner installed and the necessary Python dependencies.
# Install required statistical libraries
pip install scipy statsmodels scikit-learn pandas matplotlib
# Run a specific analysis via the CLI
python scripts/python_analysis.py --data "your_dataset.csv" --analysis "anova" --options "between_subjects"
Psychology Data Analysis Assistant Data Schema & Taxonomy
The skill processes and generates data according to the following taxonomy:
| Component | Format/Description |
|---|---|
| Input Data | Supports .xlsx, .csv, and .sav (SPSS) files. |
| Statistics | Outputs t/F/r values, Degrees of Freedom (df), and p-values. |
| Effect Sizes | Calculates Cohen's d, Partial Eta Squared (η²), and Beta (β) coefficients. |
| Reporting | Generates standardized APA 7th Edition results text blocks. |
| Metadata | Tracks normality (Shapiro-Wilk) and Sphericity (Mauchly) status. |
Psychology Data Analysis Assistant Advanced Features
- Mediation and Moderation analysis support using Bootstrap resampling methods.
- Automated visualization of interactions and descriptive statistics.
- Multi-software support allowing seamless switching between Python scripting and SPSS syntax generation.
- Built-in statistical decision tree to prevent common methodological errors.
- Integration with Openclaw Skills to handle large-scale longitudinal dataset processing.
SKILL.md
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