scholarly journals Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices Via Stated Preference Experiments

Author(s):  
Jens Hainmueller ◽  
Daniel J. Hopkins ◽  
Teppei Yamamoto

2014 ◽  
Vol 22 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Jens Hainmueller ◽  
Daniel J. Hopkins ◽  
Teppei Yamamoto

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.



2005 ◽  
Vol 37 (1) ◽  
pp. 237-248 ◽  
Author(s):  
F. Bailey Norwood

Hypothetical bias is a pervasive problem in stated-preference experiments. Recent research has developed two empirically successful calibrations to remove hypothetical bias, though the calibrations have not been tested using the same data or in a conjoint analysis. This study compares the two calibrations in a conjoint analysis involving donations to a public good. Results find the calibrations are biased predictors of true donations but that calibrated and uncalibrated models together provide upper and lower bounds to true donations.



2018 ◽  
Vol 3 (1) ◽  
pp. 238146831774617 ◽  
Author(s):  
Stuart James Wright ◽  
Fiona Ulph ◽  
Tina Lavender ◽  
Nimarta Dharni ◽  
Katherine Payne

Background: Understanding preferences for information provision in the context of health care service provision is challenging because of the number of potential attributes that may influence preferences. This study aimed to identify midwives’ preferences for the process and outcomes of information provision in an expanded national newborn bloodspot screening program. Design: A sample of practicing midwives completed a hybrid-stated preference survey including a conjoint analysis (CA) and discrete choice experiment to quantify preferences for the types of, and way in which, information should be provided in a newborn bloodspot screening program. Six conjoint analysis questions captured the impact of different types of information on parents’ ability to make a decision, and 10 discrete choice experiment questions identified preferences for four process attributes (including parents’ ability to make a decision). Results: Midwives employed by the UK National Health Service (n = 134) completed the survey. All types of information content were perceived to improve parents’ ability to make a decision except for the possibility of false-positive results. Late pregnancy was seen to be the best time to provide information, followed by day 3 postbirth. Information before 20 weeks of pregnancy was viewed as reducing parents’ ability to make a decision. Midwives preferred information to be provided by an individual discussion and did not think parents should receive information on the Internet. Conclusion: A hybrid stated preference survey design identified that a wide variety of information should be provided to maximize parents’ ability to make a decision ideally provided late in pregnancy or on day 3 postbirth.





Author(s):  
Manuel J. Martínez ◽  
Javier Cornejo

Preferences of heavy rail (HR) system users are studied in relation to the system’s alignment and bus connections in the context of a developing city. Stated preferences techniques are applied to estimate the monetary value of a long set of attributes of a new mass transit service: HR connected to bus rapid transit (BRT). Attributes include time, fare, bicycle storage at stations, stairways, feeder bus integration, integration with BRT, type of bus service, bus itinerary, and quality of buses. The long set of attributes deserved three stated preference experiments grouped by time and fare, characteristics of HR, and characteristics of BRT. They were linked by the common attribute of the fare. Results of the values of the attributes are presented. The value of the preference for HR is reduced to 8% when a feeder bus is not offered and the HR route does not reach downtown. The value of a feeder bus using small vehicles is higher than the value of BRT even if BRT operates with new buses and express service to downtown. Bicycle storage or escalators have no value for the prospective passenger. After the response of users to the new services is analyzed, conclusions for the operational design of the system are presented.



Author(s):  
Bilal Farooq ◽  
Elisabetta Cherchi ◽  
Anae Sobhani

Stated preference experiments have been criticized for lack of realism. This issue is particularly visible when the scenario does not have a well understood prior reference, as in the case of research into demand for autonomous vehicles. The paper presents Virtual Immersive Reality Environment (VIRE), which is capable of developing highly realistic, immersive, and interactive choice scenarios. We demonstrate the use of VIRE in researching pedestrian preferences related to autonomous vehicles and associated infrastructure changes on urban streets in Montréal, Canada. The results are compared with predominantly used approaches: text-only and visual aid. We show that VIRE results in respondents having better understanding of the scenario and it yields more consistent results.



2019 ◽  
Vol 10 (1-2) ◽  
pp. 1-144 ◽  
Author(s):  
Moshe Ben-Akiva ◽  
Daniel McFadden ◽  
Kenneth Train


2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1160-1160
Author(s):  
Winnie Bell ◽  
Jennifer Coates ◽  
William Masters ◽  
Norbert Wilson

Abstract Objectives Measuring consumer preferences for different food quality attributes in low- and middle-income countries (LMICs) is increasingly important for interventions and policies to better address poor nutrition and health outcomes in the context of rapidly changing food environments. Despite the importance of measuring preferences, limited research has been conducted in LMICs to develop a better understanding of what matters most to consumers. This study reviews existing methods for measuring preferences and proposes a way forward for the nutrition public health community to address this important gap. Methods Relevant papers were identified in PubMed using pre-selected Mesh terms and by searching reference lists of key review articles. Approaches identified span the fields of marketing, economics, psychology, and nutrition public health. The papers reviewed used different methods to measure preferences of various types of food attributes. Results In marketing and economics, the term conjoint analysis is used to describe a category of methods that measure the stated preference of respondents by asking them to rate, rank, or choose between competing alternatives. Within conjoint analysis, several different methods exist including discrete choice experiences, ranking conjoint analysis, and best-worst scaling and each can be used to elicit preferences about observable and unobservable attributes of foods (e.g., price, taste etc.). Within the field of psychology, several techniques have been used including the Food Choice Questionnaire and Food Choice Values. Other approaches include qualitative interviews, pile sorting, and Likert scale-based instruments. Each method has strengths and weaknesses but in general, those from marketing and economics have the benefit of resulting in a ranked choice, in contrast to Likert scales and pile sorting which can be difficult to interpret and cognitively burdensome. Conclusions Most methods have been primarily developed, validated, and used in high-income countries with much less application in LMICs. Further research is required to adapt and develop preference elicitation methods for LMICs to better measure food preferences in the context of rapidly evolving food environments. Funding Sources N/A.



2007 ◽  
Vol 64 (8) ◽  
pp. 1738-1753 ◽  
Author(s):  
Emily Lancsar ◽  
Jordan Louviere ◽  
Terry Flynn


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