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Mon, Jan 22, 2018, noon: Narayan Sastry

Young woman considers contraception

New methods for predicting contraception behavior among young women

5/12/2015 feature story

Emily Marshall and Jennifer Barber use new analytic methods in research that links fertility-related attitudes to contraceptive behaviors.

More Information.

Jennifer S. Barber

Project Information:

Using Innovative Analyses of Attitudes to Predict Fertility-Related Behavior

This project adopts new methods for predicting fertility-related behaviors using attitudinal data. A key issue in prior research has been resolving whether weak associations found between attitudes and behavior result from disjunctures between cognition (what people think) and behavior (what they do), or from inadequate measurement of cognition. Our project focuses on the second possibility, drawing on recent advances in psychology regarding cognition and behavior. Specifically, we gauge the significance of attitude measures in relation to other attitude measures, and characterize patterns of relationships between attitude measures as proxies for patterns of cognitive associations. We then assess whether these methods predict behavioral outcomes (in this case, contraceptive use) better than do conventional methods of analyzing attitude data. Our analyses use the rich data from the Relationship Dynamics and Social Life (RDSL) study, a longitudinal survey of a population-based sample of young women. The data contain multiple measures of attitudes in many domains related to fertility behavior, including attitudes about motherhood, relationship sequences, contraception, childbearing, career, and education, in addition to detailed measures of contraceptive use. We use Latent Class Analysis and Relational Class Analysis to distinguish among respondents who give the same answers to some items but who think about fertility in fundamentally different ways. This research tests theories regarding the role of attitudes in fertility behavior and will provide replicable procedures for analyzing the predictive value of survey-based measures of attitudes.

Emily Ann Marshall, Jennifer S. Barber

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