Conjoint Survey Design Tool
Anton Strezhnev, Jens Hainmueller, Daniel J. Hopkins, and Teppei Yamamoto
The Conjoint Survey Design Tool assists researchers in creating multi-dimensional choice experiments that can be readily incorporated into any pre-existing web survey software (such as Qualtrics). Conjoint analysis is a type of survey experiment often used by market researchers to measure consumer preferences over a variety of product attributes. Hainmueller, Hopkins and Yamamoto (2014) demonstrate the value of this design for political science applications. Conjoint experiments present respondents with a choice among set of profiles composed of multiple randomly assigned attributes. This approach allows researchers to estimate the effect of each individual component on the probability that the respondent will choose a profile. This software tool is designed as a companion to Hainmueller, Hopkins and Yamamoto (2014), providing a graphical user interface for generating conjoint experiments.
Hainmueller, Jens., Hopkins, Daniel J., Yamamoto, Teppei. (2014). Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices Via Stated Preference Experiments. Political Analysis. 22(1), 1-30
cjoint: AMCE Estimator for Conjoint Experiments
Anton Strezhnev, Elissa Berwick, Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto
An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Install via CRAN. (CRAN project page)
Marginal Effects Plots for Interaction Effects in R
R code to generate marginal effect plots for GLMs that include interaction terms (similar to Stata's "marginsplot" command).