Siek Toon Khoo

Risk and Protective Factors Predictive of Adolescent Pregnancy: A Longitudinal, Prospective Study.

One hundred twenty-eight Latina and African American girls from high-risk environments (e.g., poverty, family history of teen parenting, etc.) were studied from age 13 through age 19 to prospectively identify the protective factors that might guard against teenage pregnancy. Results indicated that involved and strict parenting during early adolescence buffered against pregnancy under conditions of multiple family risks and peer risks. Low childbearing intentions and desires during early adolescence also forecasted a reduced likelihood of pregnancy. Findings suggest the risk and protective factors evident during early adolescence that contribute to and potentially guard against subsequently experiencing an adolescent pregnancy.

Moderating effects of a risk factor: Modeling longitudinal moderated mediation in the development of adolescent heavy drinking

Researchers often grapple with the idea that an observed relationship may be part of a more complex chain of effects. These complex relationships are described in terms such as indirect influences, distal vs. proximal causes, intermediate outcomes, and ultimate causes; all of which share the concept of mediation. Similarly, researchers must often consider that an observed relationship may be part of a more complex, qualified system. These relationships are described using concepts such as interactions, subgroup differences, and shocks; all of which share the concept of moderation. Generally speaking, a mediator can be thought of as the carrier or transporter of information along the causal chain of effects. A moderator, on the other hand, is the changer of a relationship in as ystem. In this chapter, we explore both empirical and theoretical considerations in modeling mediation and moderation using structural equation modeling. OurThe multilevel approach uses a data set in which the records are person-days. On each record variables called “couple” (couple number), “exmprt” (examinee vs. partner), and “day” indicate which kind of person and which day is represented on the record. Before the analysis is run, some new variables are created that contain the same information. The variable “daycl” is identical to “day,” but will be used to define day as a class variable. The variable “exmnee” is a dummy code with 1 for examinee and 0 for partner. The variable “partner” is the complement of the latter: It is a dummy code with 1 for partner and 0 for examinee. With these variables one can use the following PROC MIXED syntax. PROC MIXED DATA=anger COVTEST METHOD=REML; TITLE ‘Examinee and Partner random effects and correlated errors’; CLASS couple exmprt daycl ; MODEL anger=exmnee partner day / S NOINT;Contents: Preface. N.A. Card, T.D. Little, J.A. Bovaird, Modeling Ecological and Contextual Effects in Longitudinal Studies of Human Development. S.M. Hofer, L. Hoffman, Statistical Analysis With Incomplete Data: A Developmental Perspective. K.J. Preacher, L. Cai, R.C. MacCullum, Alternatives to Traditional Model Comparison Strategies for Covariance Structure Models. S.E. Embretson, Impact of Measurement Scale in Modeling Developmental Processes and Ecological Factors. P.J. Curran, M.C. Edwards, R.J. Wirth, A.M. Hussong, L. Chassin, The Incorporation of Categorical Measurement Models in the Analysis of Individual Growth. T.D. Little, N.A. Card, D.W. Slegers, E.C. Ledford, Representing Contextual Effects in Multiple-Group MACS Models. J.A. Bovaird, Multilevel Structural Equation Models for Contextual Factors. D. Hedeker, R.J. Mermelstein, Mixed-Effects Regression Models With Heterogeneous Variance: Analyzing Ecological Momentary Assessment (EMA) Data of Smoking. T.D. Little, N.A. Card, J.A. Bovaird, K.J. Preacher, C.S. Crandel, Structural Equation Modeling of Mediation and Moderation With Contextual Factors. D.B. Flora, S.T. Khoo, L. Chassin, Moderating Effects of a Risk Factor: Modeling Longitudinal Moderated Mediation in the Development of Adolescent Heavy Drinking. D.J. Bauer, M.J. Shanahan, Modeling Complex Interactions: Person-Centered and Variable-Centered Approaches. N. Bolger, P.E. Shrout, Accounting for Statistical Dependency in Longitudinal Data on Dyads. S.M. Boker, J-P. Laurenceau, Coupled Dynamics and Mutually Adaptive Context. N. Ram, J.R. Nesselroade, Modeling Intraindividual and Intracontextual Change: Rendering Developmental Contextualism Operational. J.L. Rodgers, The Shape of Things to Come: Using Developmental Curves From Adolescent Smoking and Drinking Reports to Diagnose the Type of Social Process that Generated the Curves. K.J. Grimm, J.J. McArdle, A Dynamic Structural Analysis of the Impacts of Context on Shifts in Lifespan Development. K.F. Widaman, Intrauterine Environment Affects Infant and Child Intellectual Outcomes: Environment as Direct Effect. H. Jelicic, C. Theokas, E. Phelps, R.M. Lerner, Conceptualizing and Measuring the Context Within Person Context Models of Human Development: Implications for Theory, Research, and Application.Longitudinal studies are increasingly common in psychological and social sciences research. In these studies, subjects are measured repeatedly across time and interest often focuses on characterizing their growth or development across time. Mixed-effects regression models (MRMs) have become the method of choice for modeling of longitudinal data; variants of MRMs have been developed under a variety of names: Random-effects models. Laird and Ware (1982),variance component models (Dempster, Rubin, & Tsutakawa, 1981) , multilevel models (Goldstein, 1995), hierarchical linear models (Bryk & Raudenbush, 1992), two-stage models. Bock (1989), random coefficient models (Leeuw & Kreft, 1986), mixed models (Longford, 1987; Wolfinger, 1993), empirical Bayes models (Hui & Berger, 1983; Strenio, Weisberg, & Bryk, 1983), and random regression models (Bock, 1983b, 1983a; Gibbons, Hedeker, Waternaux, & Davis, 1988). A basic characteristic of these models is the inclusion of random subject effects into regression models in order to account for the influence of subjects on their repeated observations. These random effects reflect each person’s growth or development across time, and explain the correlational structure of the longitudinal data. Additionally, they indicate the degree of subject variation that exists in the population of subjects. There are several features that make MRMs especially useful in longitudinal research. First, subjects are not assumed to be measured on the same number of timepoints, thus, subjects with incomplete data across time are included in the

Validation of a culturally adapted developmental screening tool for Australian Aboriginal children: Early findings and next steps

BACKGROUND Early detection of developmental problems is important for facilitating access to targeted intervention and maximising its positive effects. The later problems are identified, the more likely that they will become increasingly difficult to ameliorate. Standardised developmental screening tools are known to improve detection rates of developmental problems compared to clinical judgement alone and are widely recommended for use with all children. The Ages and Stages Questionnaire (ASQ-3) is a tool that is widely used in Australia. However, mainstream screening tools may not be appropriate for remote-dwelling Australian Aboriginal children. While Australian Aboriginal children face multiple developmental risk factors, there are no developmental screening tools that have been validated for use in this population. AIMS To determine the concurrent validity of the culturally adapted ASQ-3 - the ASQ-TRAK - for Australian Aboriginal children compared to the Bayley Scales of Infant and Toddler Development (Bayley-III), a standardised, professionally administered developmental assessment. SUBJECTS The ASQ-TRAK and Bayley-III were administered cross-sectionally to 67 Central Australian Aboriginal children between 2 and 36months of age. RESULTS The ASQ-TRAK communication, gross motor, fine motor and problem-solving domains and the corresponding domains on the Bayley-III were moderately correlated. Overall sensitivity for the ASQ-TRAK was 71% (95% CI 29-96) and specificity was 92% (95% CI 88-99). Percentage agreement between the ASQ-TRAK and the Bayley-III was 90%. CONCLUSIONS The ASQ-TRAK shows promise as a tool that can be used to improve developmental monitoring for remote dwelling Australian Aboriginal children. Further research is necessary to build on the current findings.