PaluckMetaSOP

This package documents the Paluck lab’s approach to meta-analysis, focusing on two papers in particular:

• “Preventing Sexual Violence —A Behavioral Problem Without a Behaviorally-Informed Solution"
• “The Contact Hypothesis Re-evaluated.”

Installation

Run the following R command:

remotes::install_github('setgree/PaluckMetaSOP', build_vignettes = TRUE)

What you'll find

This package contains:

• Functions that implement the Paluck lab approach to meta-analysis
• Vignettes that walk through how to use the functions
• Datasets (sv_data and contact_data) that accompany the two example metas and are used in the vignettes

Getting started

After installation, run:

browseVignettes(package = "PaluckMetaSOP")

We recommend going through the vignettes in order, but you can also pick and choose based on what seems relevant.

The functions

For calculating effect size and variance:

• d_calc: a function for calculating standardized mean differences (SMDs) -- Cohen's D/Glass's ∆/etc.
• var_d_calc: a function for calculating the variance of your SMDs.

For meta-analyzing studies:

• map_robust: This function is a tidyverse-friendly wrapper around metafor::robust(), which is the flavor of meta-analysis we used in the two metas listed above

Helper functions

• study_count: tidyverse-friendly function for counting studies in a grouping (the function assumes that each row in a dataset corresponds to a point estimate from a study, and studies are grouped by a variable called unique_study_id)
• sum_lm: a tidyverse-friendly wrapper around summary(lm()).
• sum_tab: a tidyverse-friendly version of R's built-in table function.

For more documentation, either see the vignettes or add a ? before any of the functions (e.g., ?PaluckMetaSOP::sum_tab).

For Paluck Lab members extending this package

To contribute:

1. Clone this package:

   git clone https://github.com/setgree/PaluckMetaSOP.git

2. Add or amend functions and vignettes
3. Open a pull request or create your own fork

The instructions in the first chapter of R packages should get you pretty far on the "building a package" side of things.