Welcome to researchpy’s documentation!

About ResearchPy

ResearchPy is an open-source statistical library focused on clear, reproducible univariate and bivariate analysis for research workflows.

For background on authorship, scope, and design principles, see the About page.

Note

researchpy is only compatible with Python 3.x. Download using either:

• For standard install

  • pip install researchpy

• For installation through conda

  • conda install researchpy::researchpy

Contents:

• About
  • What ResearchPy Is
  • Authorship and Scope
  • Relationship to the Python Statistical Ecosystem
  • Broader Context
• Technical Design Rationale
  • The Gap
  • Design Principles
  • Verification & Rigor: The “Trust Layer”
  • Roadmap & Evolution: Guided by User Needs
    • Current Scope & Stability
  • Relationship to Hearts of Science: The Emerging Container
• Installing researchpy
• fetch_dta()
  • Description
  • Parameters
    • Input
    • Returns
    • Raises
  • Examples
    • Get dataset (default latest version)
    • Specify dataset version
    • Force refresh download from server
    • Fetching Common Datasets
  • References
• estable()
  • Description
  • Parameters
    • Input
    • Returns
  • Examples
    • Loading Packages and Data
    • Single Variable
    • Two Variables
    • Pandas Groupby Objects
• codebook()
  • Arguments
  • Examples
• summarize()
  • Description
  • Parameters
    • Input
    • Returns
  • Examples
    • Loading Packages and Data
    • Single Variable
    • Two Variables
    • Pandas Groupby Objects
• summary_cont()
  • Arguments
  • Examples
• summary_cat()
  • Arguments
  • Examples
• difference_test()
  • Description
  • Parameters
    • Input
    • Returns
  • Effect Size Measures Formulas
    • Cohen’s d_s (between subjects design)
    • Cohen’s d_av (within subject design)
    • Hedges’s g_s (between subjects design)
    • Hedges’s g_av (within subjects design)
    • Glass’s \(\Delta\) (between or within subjects design)
    • Pearson correlation coefficient r (between or within subjects design)
    • Rank-Biserial correlation coefficient r (between or within subjects design)
  • Examples
    • Loading Packages and Data
    • Independent Samples t-test
    • Paired Samples t-test
    • Welch’s t-test
    • Wilcoxon signed-rank Test
  • References
• ttest()
  • Description
  • Parameters
    • Input
    • Returns
  • Welch Degrees of freedom
  • Effect Size Measures Formulas
    • Cohen’s d_s (between subjects design)
    • Hedges’s g_s (between subjects design)
    • Glass’s \(\Delta\) (between or within subjects design)
    • Cohen’s d_av (within subject design)
    • Pearson correlation coefficient r (between or within subjects design)
    • Rank-Biserial correlation coefficient r (between or within subjects design)
  • Examples
    • Loading Packages and Data
    • Independent t-test
    • Paired Sample t-test
    • Welch’s t-test
    • Wilcoxon Signed-Rank Test
  • References
• signrank()
  • Description
  • Parameters
    • Input
    • Returns
  • Effect size measures formulas
    • Rank-Biserial r
    • Pearson r
  • Examples
    • Loading Packages and Data
    • Signrank using Wide Structured Datasets
    • Signrank using Long Structured Datasets
  • References
• crosstab()
  • Description
  • Parameters
    • Input
    • Returns
  • Effect Size Measures Formulas
    • Cramer’s Phi (2x2 table)
    • Cramer’s V (RxC where R or C > 2)
  • Examples
    • Loading Packages and Data
    • Crosstabulation with Frequency
    • Crosstabulation with Cell Percentages
    • Crosstabulation with Row Percentages
    • Crosstabulation with Column Percentages
    • Chi Squared (\(\chi^2\)) Test of Independence
    • G-test
    • Fisher’s Exact test
    • McNemar test
  • References
• corr_case()
  • Description
  • Parameters
    • Input
  • Examples
    • Loading Packages and Data
    • Pearson r
  • References
• corr_pair()
  • Description
  • Parameters
    • Input
  • Examples
    • Loading Packages and Data
    • Pearson r
  • References
• anova()
  • Description
  • Parameters
    • Input
    • Returns
  • Effect Size Measures Formulas
    • Eta-squared (\(\eta^2\)) and \(R^2\)
    • Adjusted \(R^2\)
    • Partial Eta-squared (\(\eta^2_p\))
    • Omega-squared (\(\omega^2\))
    • Partial Omega-squared (\(\omega^2_p\))
  • Examples
  • References
• ols()
  • Description
  • Parameters
    • Input
    • Returns
  • Effect Size Measures Formulas
    • Eta-squared (\(\eta^2\)) and \(R^2\)
    • Adjusted \(R^2\)
    • Omega-squared (\(\omega^2\))
  • Examples
  • References
• predict()
  • Description
    • Classes where predict included
  • Parameters
    • Input
    • Returns
  • Formulas
    • Linear prediction
    • Residuals
    • Standardized residuals
    • Studentized (jackknifed) residuals
    • Leverage
  • Examples
  • References
• Special Thank You