How to Use New Conjoint Analysis Tools
We published a paper on how to use our conjoint analysis tools in The Political Methodologist
The paper below published in The Political Methodologist outlines how to use new tools we developed, based on our experience in Tanzania, for implementing conjoint experiments in developing countries. The first tool produces conjoint profiles in the Qualtrics offline app. The second is an app allows researchers to produce PDFs of conjoint profiles using images to represent attribute-levels. Read more on how to use these tools below.
Conjoint Analysis Tools for Developing Country Contexts
by Alexander Meyer and Leah R. Rosenzweig (Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA 02139. firstname.lastname@example.org, corresponding author.)*
Conjoint analysis has long been used in marketing research, but has recently become popular in political science. Originally developed by Luce and Tukey in 1964, conjoint analysis serves as a useful tool for understanding preferences over multidimensional alternatives. This method presents respondents with profiles — for example of candidates (Carlson, 2015; Rosenzweig and Tsai, N.d.) or immigrants (Hainmueller and Hopkins, 2014; Berinsky et al., 2015) — that have randomly assigned attributes and asks respondents to evaluate and choose between them. The random assignment of profile characteristics allows researchers to identify the causal influence of attributes on a person’s decision to vote for a candidate or allow an immigrant into the country.
Conjoint analysis is advantageous for researchers interested in observing respondents’ choice-making behaviors and attitudes. Using this method, researchers can identify interaction effects as well as analyze particular aspects of treatments. For example, not only can it be used to identify the effect of a candidate’s past performance on the probability that respondents will vote for her, but we can also analyze the influence of past performance with respect to the candidate’s ethnic identity (Carlson, 2015). In addition, conjoint analysis allows us to investigate subgroup effects based on shared attributes between profiles and respondents, which can influence respondent attitudes (Berinsky et al., 2015). Thus, we are able to implement more realistic ‘bundled’ treatments, testing multiple hypotheses simultaneously (Hainmueller, Hopkins and Yamamoto, 2014).
As with all survey experiments, external validity is always a concern. Hainmueller, Hangartner and Yamamoto (2015) test the external validity of conjoint analysis by comparing results to a real-world behavioral benchmark in Switzerland. The authors find strong evidence that conjoint experiments can help to explain the preferences and behaviors of people in the real-world. From a paired conjoint design “estimates are within 2% percentage points of the effects in the behavioral benchmark” (Hainmueller, Hangartner and Yamamoto, 2015, p. 2395). Not only is conjoint analysis useful for investigating multiple hypotheses at once, but it can also achieve reliable results.
Until very recently, conjoint analysis had been relegated to online surveys. However, this method presents an excellent opportunity for researchers to understand preferences and behaviors across a host of different contexts. Researchers have begun to take advantage of this method in developing countries (Carlson, 2015; Hartman and Morse, 2015) but lack widely available resources for easy implementation and standardized best practices. Here we present the tools we developed to help researchers conduct conjoint experiments offline among respondents with little or no education.
Download the full publication to read more.