Approaches for Addressing Missing Data in Statistical Analyses of Female and Male Adolescent Fertility

Abstract

Missing data is a pervasive problem in social science research. Allison (2002: 1) has written that “sooner or later, usually sooner, anyone who does statistical analysis runs into problems with missing data. In a typical dataset, information is missing for some variables for some cases. … Missing data are a ubiquitous problem in both the social and health sciences … [Yet] the vast majority of statistical textbooks have nothing whatsoever to say about missing data or how to deal with it.” Treiman (2009: 182) has noted that “missing data is a vexing problem in social research. It is both common and difficult to manage.” In this chapter we undertake two separate analyses, one for females and the other for males, of the likelihood of the respondent reporting having had a teen birth. We use several independent variables in our analyses that have been shown in prior studies to be important predictors of adolescent fertility. We handle the problem of missing data using several different approaches.