250451 Revisiting the link between self-regulation, risk proneness and sexual behavior with application of data weights

Wednesday, November 2, 2011: 12:50 PM

Lynn Agre, MPH, PhD , Dimacs/Ccicada, Rutgers University, Piscataway, NJ
N. Andrew Peterson, PhD , School of Social Work, Rutgers University, New Brunswick, NJ
In a recent study, Crockett, Raffaelli and Shen (2006) examined the link between self-regulation (behavioral problems) in mid-childhood, risk proneness (sensation seeking) and peer pressure in early adolescence and health risk behavior (alcohol use and sexual risk taking) in mid-adolescence drawing from a sample (n=518) in the National Longitudinal Survey on Youth (NLSY). Their structural equation model (SEM) revealed that behavioral problems in early childhood (measured in survey year 1990) predispose youth in mid-adolescence to perceive themselves as engaging in higher sensation seeking (assessed in 1994), thereby leading to alcohol use and sexual risk taking in mid-adolescence (outcomes in 1998). Though their research substantiated the relationship among these underlying mechanisms longitudinally, their computations did not yield significant pathways between self-regulation in mid-childhood and peer pressure in early adolescence. Further, differences between racial/ethnic groups were not detected. Thus, in order to control for oversampling of underrepresented minorities, their study is replicated in this paper by applying the transformed raw weights to the covariance matrix calculated in SPSS and analyzed in AMOS. Preliminary results from the data weighting procedure applied to path analysis again demonstrate the relationship between risk proneness in early adolescence and substance use and sexual behavior in mid-adolescence, but also that self-regulation in early childhood significantly influences peer pressure. Moreover, the findings from the weighted SEM show that risk proneness does directly impact sexual risk taking. Thus, replicating the multi-wave SEM model by introducing mathematical weighting techniques to the NLSY ensures generalizability and replicability to other study populations.

Learning Areas:
Assessment of individual and community needs for health education
Conduct evaluation related to programs, research, and other areas of practice
Planning of health education strategies, interventions, and programs
Public health or related public policy
Social and behavioral sciences

Learning Objectives:
Examine how statistical weighting techniques alter results of previously published research study. Understand why weighting data using algebraic impacts findings and thus public health policy.

Keywords: Methodology, Risk Taking Behavior

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I conducted all analysis and wrote the narrative for this research study.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.