Can Innovative Methods Improve Prediction of Fertility-related Behavior Using Attitudinal Measures?

• In Event: 231 - Section on Sociology of Population Refereed Roundtable Session and Business Meeting
  In Refereed Roundtable 60min: Table 06. Contraceptive Use, Fertility, and Union Formation: New Insights

Abstract

Demographers have long struggled with the difficulty of predicting individual-level fertility outcomes using attitudinal measures. This study applies new methods of analysis to improve the utility of attitudinal data in predicting fertility-related behaviors, addressing recent calls by prominent demographers to use models of cognitive processes and demographic behavior that reflect advances in scientific knowledge about cognition and behavior (e.g., Bachrach 2014; Bachrach and Morgan 2013; Johnson-Hanks et al. 2011). We use an innovative conceptualization of attitudes that draws on advances in psychology, using the insight that attitude measures are meaningful in relation to other attitude measures, and thus consider patterns of relationships between attitude measures as a proxy for patterns of cognitive associations. We use two types of methods to analyze fertility-related attitudinal data in order to group survey respondents who think similarly about fertility: Latent Class Analysis, which creates latent groups based on sharing similar patterns of responses across a series of variables, and Relational Class Analysis (Goldberg 2013), which identifies subgroups of respondents who share similar relationships among their responses. Using data from the Relationship Dynamics and Social Life study, we use these methods to distinguish among respondents who give the same answers to some survey items but who think about fertility in fundamentally different ways. We describe the patterns of cognitive associations and sociodemographic characteristics of the groups identified. We then use these groupings to determine whether these methods predict a behavioral outcome—contraceptive use—better than do conventional methods of analyzing attitude data.

Authors

• Hana Shepherd, Rutgers University

• Emily A. Marshall, Franklin & Marshall College