What is conjoint analysis?
Bio: "Todd Hartman is Professor of Quantitative Social Science in the Department of Social Statistics at the University of Manchester. His research explores the psychological underpinnings of public opinion and behaviour using cutting-edge research methods and statistical techniques. His work has been published in prestigious peer-reviewed academic journals such as Nature Communications, Nature: Scientific Reports, Psychological Medicine, Big Data & Society, British Journal of Political Science, The Journal of Quantitative Criminology, Social Psychological and Personality Science, Political Psychology, Political Communication, and The Geographical Journal. Professor Hartman has been working with an interdisciplinary team to study the impact of COVID-19 on the public. This project secured early funding from the UK’s Economic and Social Research Council (ESRC) and has collected nationally representative panel data in multiple countries (e.g., UK, Ireland, Spain, and Italy) from multiple survey waves of respondents beginning when the first UK Lockdown was announced (on 23 March 2020). This unique collaboration is only one of two social science research teams to receive ESRC funding to collect new longitudinal survey data since the start of the pandemic to study the implications of COVID-19 on adults living in the UK (e.g., see this funding announcement). While this project has been immensely challenging, given the speed with which things have changed locally, nationally, and internationally, it has also been a once-in-a-lifetime opportunity (hopefully!) to study a global health crisis which has wrought about such societal upheaval."
Conjoint analysis is a method used in the social sciences to identify what individuals value when making choices involving trade-offs. A conjoint experiment works by presenting respondents with profiles containing options randomly generated from a list. Its forced choice design simplifies the decision task facing respondents and allows researchers to test huge combinations of features -- on the order of tens of thousands -- which would be impractical using a typical experimental design.
You will learn about the underlying principles of this approach; as well as how to conduct conjoint analysis; test interactions and/or subgroup effects; and visualise the results in R.