Community Effects on Youth Sexual Activity

Using longitudinal data from the National Survey of Children, we examined the impact of community socioeconomic status on four dimensions of adolescent and young adult premarital sexual activity—the timing of first intercourse, the frequency of intercourse, the number of different sex partners, and the likelihood of engaging in unprotected intercourse. We found significant positive effects of a multiitem index of community socioeconomic disadvantage on all but the timing of first premarital intercourse, net of controls for the socioeconomic and demographic status of adolescents and their families. None of the most commonly cited explanations for neighborhood effects on adolescent behavior can fully explain these associations. Only the attitudes and behaviors of peers account for even a small portion of the observed impact of community disadvantage on youth sexual behavior. Adolescents' acceptance of premarital childbearing, educational aspirations and attachment to school, and parental supervision, although frequently associated with youth sexual behavior, do little to mediate the impact of community disadvantage on sexual activity.     

Design and analysis of a fractional 413125 split-plot experiment

SUMMARY This paper is a case study on two aspects of constructing mixed factorial experiments: (1) three equally sized fractions of a 2p+ 2 design are combined under a three level factor, yielding a 312p+ 2 experiment; (2) two carefully selected factors from a 2p+ 2 design are combined to obtain a 412p design. We consider both aspects for the design of a 1/8 fraction of a 413125 experiment (48 observations) to investigate a DNA amplification technique. The experiment is of the split-plot type, because the main effects of two factors had to be confounded with runs of a piece of equipment (whole-plots), while the other factors were varied between vials (subplots) contained within the equipment. We confounded an additional effect to avoid the usual difficulty in evaluating the whole-plot effects in unreplicated experiments. Both whole-plot and subplot effects can then be evaluated with half-normal plots. The analysis is illustrated with the results of the experiment.     

Cognitive Self‐Regulation and Depression: Examining Academic Self‐Efficacy and Goal Characteristics in Youth of a Northern Plains Tribe

The relationship between cognitive self‐regulatory processes and depression was examined in American Indian adolescents from a Northern Plains tribe. Students completed measures of negative life events, self‐efficacy, goals, and depressive symptoms. Results indicated that academic self‐efficacy was strongly associated with depression. Academic self‐efficacy also correlated with intrinsically motivating goal representations, such that students who indicated high academic self‐efficacy had goals that were more important to them, goals they thought more about, and goals they viewed as wanted by the self instead of as imposed on by others. However, we did not find the hypothesized mediational model in which academic self‐efficacy influenced depression indirectly by influencing goal characteristics. Rather, this indirect model varied by grade, and differed from what we expected. Specifically, for older adolescents, higher levels of academic self‐efficacy predicted goals that were more likely to be identified as the adolescent's own, and in turn, these self‐ as opposed to other‐oriented goals predicted higher levels of depressive symptoms. Results are discussed as providing support for continued investigations into the role of specific cognitive self‐regulatory processes in youth adjustment.     

Analyzing Risk Response Dynamics on the Web: The Case of Hurricane Katrina

Using a unique data set that documented the hourly web-surfing behavior of over 140,000 Internet users in five southeastern states in August 2005, we explore the dynamics of information gathering as Hurricane Katrina developed and then hit South Florida and the Northern Gulf Coast. Using both elementary statistical methods and advanced techniques from functional data analysis,^{( 1 )} we examine both how storm events (such as the posting of warnings) affected traffic to weather-related websites, and how this traffic varied across locations and by characteristics of the web user. A general finding is that spatial-temporal variation in weather-site web traffic generally tracked the timing and scale of the storm threat experienced by a given area. There was, however, considerable variation in this responsiveness. Residents in Florida counties that had been most directly affected by Hurricane Dennis just a month earlier, for example, displayed more active visitation rates than those who had been less affected. We also find evidence of a gender effect where male users displayed a disproportionately larger rate of visitation to weather sites given the onset of storm warnings than females. The implications of this work for the broader study of behavioral risk response dynamics during hazards are explored.     

Testing for Imperfect Debugging in Software Reliability

This paper continues the study of the software reliability model of Fakhre-Zakeri & Slud (1995), an "exponential order statistic model" in the sense of Miller (1986) with general mixing distribution, imperfect debugging and large-sample asymptotics reflecting increase of the initial number of bugs with software size. The parameters of the model are θ (proportional to the initial number of bugs in the software), G (·, μ) (the mixing df, with finite dimensional unknown parameter μ, for the rates λ _i with which the bugs in the software cause observable system failures), and p (the probability with which a detected bug is instantaneously replaced with another bug instead of being removed). Maximum likelihood estimation theory for (θ, p , μ) is applied to construct a likelihood-based score test for large sample data of the hypothesis of "perfect debugging" ( p = 0) vs "imperfect" ( p > 0) within the models studied. There are important models (including the Jelinski–Moranda) under which the score statistics with 1/√ n normalization are asymptotically degenerate. These statistics, illustrated on a software reliability data of Musa (1980), can serve nevertheless as important diagnostics for inadequacy of simple models     

Mitchell's Relational Conflict Model: An Analysis of Its Usefulness in Clinical Social Work

This article examines the relational conflict model developed by Stephen A. Mitchell. The main concepts of Mitchell's relational conflict model are discussed, particularly early patterns of connection to parental figures and the interactive treatment approach to understanding and changing those relational patterns. The model is contrasted with classical drive and developmental arrest theories in the psychodynamic treatment of psychopathology. We explain how Mitchell's model, in keeping with a social work perspective, takes into consideration the social, cultural, and environmental factors as well as the intrapsychic. We discuss how patterns of relating are reworked throughout the life course, and we explore the potential for individual agency in constructing new patterns of relating. We also highlight the social constructivist aspects of Mitchell's approach which are congruent with current contemporary trends in social work theory. A case vignette is presented to illustrate the viability of this model for clinical social work practice.     

Factor Stochastic Volatility in Mean Models: A GMM Approach

This paper provides a semiparametric framework for modeling multivariate conditional heteroskedasticity. We put forward latent stochastic volatility (SV) factors as capturing the commonality in the joint conditional variance matrix of asset returns. This approach is in line with common features as studied by Engle and Kozicki (1993), and it allows us to focus on identication of factors and factor loadings through first- and second-order conditional moments only. We assume that the time-varying part of risk premiums is based on constant prices of factor risks, and we consider a factor SV in mean model. Additional specification of both expectations and volatility of future volatility of factors provides conditional moment restrictions, through which the parameters of the model are all identied. These conditional moment restrictions pave the way for instrumental variables estimation and GMM inference.