Dimensions of Reproductive Attitudes and Knowledge Related to Unintended Childbearing Among U.S. Adolescents and Young Adults
Guzzo, Karen Benjamin, Sarah R. Hayford, Vanessa Wanner Lang, Hsueh-Sheng Wu, Jennifer S. Barber, and Yasamin Kusunoki. 2019. "Dimensions of Reproductive Attitudes and Knowledge Related to Unintended Childbearing Among U.S. Adolescents and Young Adults." Demography, 56(1): 201-228.
Measures of attitudes and knowledge predict reproductive behavior, such as unintended fertility among adolescents and young adults. However, there is little consensus as to the underlying dimensions these measures represent, how to compare findings across surveys using different measures, or how to interpret the concepts captured by existing measures. To guide future research on reproductive behavior, we propose an organizing framework for existing measures. We suggest that two overarching multidimensional concepts-reproductive attitudes and reproductive knowledge-can be applied to understand existing research using various measures. We adapt psychometric analytic techniques to analyze two data sets: the National Longitudinal Survey of Adolescent to Adult Health (Add Health) and the Relationship Dynamics and Social Life study (RDSL). Although the specific survey measures and sample composition of the two data sets are different, the dimensionality of the concepts and the content of the items used to measure their latent factors are remarkably consistent across the two data sets, and the factors are predictive of subsequent contraceptive behavior. However, some survey items do not seem strongly related to any dimension of either construct, and some dimensions of the two concepts appear to be poorly measured with existing survey questions. Nonetheless, we argue that the concepts of reproductive attitudes and reproductive knowledge are useful for categorizing and analyzing social psychological measures related to unintended fertility. The results can be used to guide secondary data analyses to predict reproductive behavior, compare results across data sets, and structure future data collection efforts.