Code Monkey home page Code Monkey logo

multilevelmediation's Introduction

multilevelmediation

Overview

multilevelmediation contains functions for computing indirect effects with multilevel models and obtaining confidence intervals for various effects using bootstrapping. The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models, the option of a moderating variable at level 1 or level 2 for either the a, b, or both paths, and a wide array of random effects for any coefficients represented in level 1 equations (including those for the 1-1-1 model and random effects for the moderator). Currently the 1-1-1 model is supported and has been evaluated in simulations (see Falk et al in references). Support for Bayesian estimation and the inclusion of covariates comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors (e.g., for X) must be numerically represented.

Installation

# From GitHub:
# install.packages("devtools")
devtools::install_github("falkcarl/multilevelmediation")

Some relevant references

Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11(2), 142–163. https://doi.org/10.1037/1082-989X.11.2.142

Carpenter, J. R., Goldstein, H., & Rasbash, J. (2003). A novel bootstrap procedure for assessing the relationship between class size and achievement. Applied Statistics, 52(4), 431-443.

Falk, C. F., Vogel, T., Hammami, S., & Miočević, M. (2022). Multilevel mediation analysis in R: A comparison of bootstrap and Bayesian approaches. Preprint: https://doi.org/10.31234/osf.io/ync34

Hox, J., & van de Schoot, R. (2013). Robust methods for multilevel analysis. In M. A. Scott, J. S. Simonoff & B. D. Marx (Eds.), The SAGE Handbook of Multilevel Modeling (pp. 387-402). SAGE Publications Ltd. doi: 10.4135/9781446247600.n22

Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate behavioral research, 36(2), 249-277. doi: 10.1207/S15327906MBR3602_06

van der Leeden, R., Meijer, E., & Busing, F. M. T. A. (2008). Resampling multilevel models. In J. de Leeuw & E. Meijer (Eds.), Handbook of Multilevel Analysis (pp. 401-433). Springer.

multilevelmediation's People

Contributors

falkcarl avatar toddvogel1628 avatar sarahhammami avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.