Comparison of weighted versus non-weighted path analysis in adolescent health risk behavior

Large-scale national surveys often provide a raw weight variable, that when applied without transformation, yields no change in statistical results, thus leading to spurious conclusions about relationships between predictors and outcomes. For example, most research studies using various components of the National Longitudinal Survey on Youth (NLSY) data sets to test hypotheses, do not employ a weighting technique or post-stratification procedure to normalize the sample against the population from which it is drawn. Therefore, this paper illustrates how an algebraic weight formula introduced by Oh and Scheuren (1983), can be used in path analysis to elucidate the relationship between underlying psychosocial mechanisms and health risk behaviors among adolescents in the 1998 NLSY Young Adult cohort. This project, then, investigates the relationship between self-assessed risk perception or risk proneness and how that perception affects the likelihood of an adolescent to engage in substance use and sexual behavior, partitioned into clusters by mother's educational attainment. To control for oversampling of under represented racial/ethnic groups, mathematically adjusted design weights are implemented in the calculation of the covariance matrices, comparing non-normalized versus normalized path analysis results. The impact of ignoring weights leading to serious bias in parameter estimates, with the underestimation of standards errors will be discussed. This research makes an important contribution by using a weighted case approach in testing different samples of youth by race/ethnicity and mother's educational attainment. Determining policy initiatives and objectives requires that the data be representative of the population, ensured by applying the weight formula.