Analyzing factorial survey data with structural equation models

• Published in: Sociological methods & research Vol. 52; no. 4; pp. 2050 - 2082
• Main Authors: Weijters, Bert, Davidov, Eldad, Baumgartner, Hans
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.11.2023; SAGE PUBLICATIONS, INC
• Subjects: Attributes; Evaluation Methods; Experiments; Factor Analysis; Individual differences; Polls & surveys; Regression (Statistics); Social research; Social Science Research; Structural equation modeling; Structural Equation Models; Structural models; Vignettes; survey methods; Factorial survey; structural equation modeling; vignette study; conjoint analysis
• ISSN: 0049-1241; 1552-8294
• DOI: 10.1177/00491241211043139

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different attributes by participants to arrive at an overall response to the vignettes. In the current paper, we explain how data from factorial surveys can be analyzed in a structural equation modeling framework using an approach called structural equation modeling for within-subject experiments. We review the use of factorial surveys in social science research, discuss typically used methods to analyze factorial survey data, introduce the structural equation modeling for within-subject experiments approach, and present an empirical illustration of the proposed method. We conclude by describing several extensions, providing some practical recommendations, and discussing potential limitations.