Ryne Estabrook

Life Satisfaction Shows Terminal Decline in Old Age: Longitudinal Evidence from the German Socio-Economic Panel Study (SOEP).

Longitudinal data spanning 22 years, obtained from deceased participants of the German Socio-Economic Panel Study (SOEP; N = 1,637; 70- to 100-year-olds), were used to examine if and how life satisfaction exhibits terminal decline at the end of life. Changes in life satisfaction were more strongly associated with distance to death than with distance from birth (chronological age). Multiphase growth models were used to identify a transition point about 4 years prior to death where the prototypical rate of decline in life satisfaction tripled from -0.64 to -1.94 T-score units per year. Further individual-level analyses suggest that individuals dying at older ages spend more years in the terminal periods of life satisfaction decline than individuals dying at earlier ages. Overall, the evidence suggests that late-life changes in aspects of well-being are driven by mortality-related mechanisms and characterized by terminal decline.

Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

Growth mixture models (GMMs; B. O. Muthén & Muthén, 2000; B. O. Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this article, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study–Kindergarten Cohort to illustrate their use.

A monte carlo simulation study of the reliability of intraindividual variability

Recent research has seen intraindividual variability become a useful technique to incorporate trial-to-trial variability into many types of psychological studies. Intraindividual variability, as measured by individual standard deviations (ISDs), has shown unique prediction to several types of positive and negative outcomes (Ram, Rabbit, Stollery, & Nesselroade, 2005). One unanswered question regarding measuring intraindividual variability is its reliability and the conditions under which optimal reliability is achieved. Monte Carlo simulation studies were conducted to determine the reliability of the ISD as compared with the intraindividual mean. The results indicate that ISDs generally have poor reliability and are sensitive to insufficient measurement occasions, poor test reliability, and unfavorable amounts and distributions of variability in the population. Secondary analysis of psychological data shows that use of individual standard deviations in unfavorable conditions leads to a marked reduction in statistical power, although careful adherence to underlying statistical assumptions allows their use as a basic research tool.

A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence.

Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have generally been ignored in this debate. This article presents a simulation study that compares the reliability of sum scores, regression-based and expected posterior methods for factor score estimation for confirmatory factor models in the presence of missing data. Although all methods perform reasonably well with complete data, expected posterior-weighted (full) maximum likelihood methods are significantly more reliable than sum scores and regression estimators in the presence of missing data. Factor score reliability for complete data can be predicted by Guttmans 1955 formula for factor communality. Furthermore, factor score reliability for incomplete data can be reasonably approximated by communality raised to the power. An empirical demonstration shows that the full maximum likelihood method best preserves the relationship between nicotine dependence and a genetic predictor under missing data. Implications and recommendations for applied research are discussed.

Separating Family-Level and Direct Exposure Effects of Smoking During Pregnancy on Offspring Externalizing Symptoms: Bridging the Behavior Genetic and Behavior Teratologic Divide

AbstractMaternal smoking during pregnancy (MSDP) has been robustly associated with externalizing problems and their developmental precursors in offspring in studies using behavioral teratologic designs (Wakschlag et al., Am J Public Health 92(6):966–974, 2002; Espy et al., Dev Psychol 47(1):153–169, 2011). In contrast, the use of behavior genetic approaches has shown that the effects commonly attributed to MSDP can be explained by family-level variables (D’Onofrio et al., Dev Psychopathol 20(01):139–164, 2008). Reconciling these conflicting findings requires integration of these study designs. We utilize longitudinal data on a preschool proband and his/her sibling from the Midwest Infant Development Study-Preschool (MIDS-P) to test for teratologic and family level effects of MSDP. We find considerable variation in prenatal smoking patterns both within and across pregnancies within families, indicating that binary smoking measures are not sufficiently capturing exposure. Structural equation models indicate that both conduct disorder and oppositional defiant disorder symptoms showed unique effects of MSDP over and above family level effects. Blending high quality exposure measurement with a within-family design suggests that it is premature to foreclose the possibility of a teratologic effect of MSDP on externalizing problems. Implications and recommendations for future studies are discussed.

Using genetic information to test causal relationships in cross-sectional data

Cross-sectional data from twins contain information that can be used to derive a test of causality between traits. This test of directionality is based upon the fact that genetic relationships between family members conform to an established structural pattern. In this paper we examine several common methods for empirically testing causality as well as several genetic models that we build on for the Direction of Causation (DoC) model. We then discuss the mathematical components of the DoC model and highlight limitations of the model and potential solutions to these limitations. We conclude by presenting an example from the personality and politics literature that has begun to explore the question whether or not personality traits cause people to hold specific political attitudes.

Contextual variation in young children's observed disruptive behavior on the DB‐DOS: implications for early identification

BACKGROUND Contextual variation in child disruptive behavior is well documented but remains poorly understood. We first examine how variation in observed disruptive behavior across interactional contexts is associated with maternal reports of contextual variation in oppositional-defiant behavior and functional impairment. Second, we test whether child inhibitory control explains the magnitude of contextual variation in observed disruptive behavior. METHODS Participants are 497 young children (mean age = 4 years, 11 months) from a subsample of the MAPS, a sociodemographically diverse pediatric sample, enriched for risk of disruptive behavior. Observed anger modulation and behavioral regulation problems were coded on the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS) during interactions with parent and examiner. Oppositional-defiant behavior, and impairment in relationships, with parents and nonparental adults, were measured with the Preschool Age Psychiatric Assessment (PAPA) interview with the mother. Functional impairment in the home and out-and-about was assessed with the Family Life Impairment Scale (FLIS), and expulsion from child care/school was measured with the baseline survey and FLIS. RESULTS Observed disruptive behavior on the DB-DOS Parent Context was associated with oppositional-defiant behavior with parents, and with impairment at home and out-and-about. Observed disruptive behavior with the Examiner was associated with oppositional-defiant behavior with both parents and nonparental adults, impairment in relationships with nonparental adults, and child care/school expulsion. Differences in observed disruptive behavior in the Parent versus Examiner Contexts was related to the differences in maternal reports of oppositional-defiant behavior with parents versus nonparental adults. Children with larger decreases in disruptive behavior from Parent to Examiner Context had better inhibitory control and fewer attention-deficit/hyperactivity disorder symptoms. CONCLUSIONS The DB-DOS showed clinical utility in a community sample for identifying contextual variation that maps onto reported oppositional-defiant behavior and functioning across contexts. Elucidating the implications of contextual variation for early identification and targeted prevention is an important area for future research.

Patterns and Predictors of Compliance in a Prospective Diary Study of Substance Use and Sexual Behavior in a Sample of Young Men Who Have Sex With Men

Behavioral diaries are used for observing health-related behaviors prospectively. Little is known about patterns and predictors of diary compliance to better understand differential attrition. An analytic sample of 241 young men who have sex with men (YMSM) from a 2-month diary study of substance use and sexual behavior were randomized to complete daily or weekly timeline followback diaries. Latent class growth analyses were used to analyze data. Weekly and daily diary groups produced similar compliance patterns: high, low, and declining compliance groups. Black YMSM were more likely to be in the declining compared with the high compliance group. YMSM who were randomly assigned to receive automated feedback about risk behaviors did not differ in compliance rate compared with those who did not. Risk behavior engagement did not predict compliance in the daily condition, but some substances predicted compliance in the weekly condition. Implications for observational and behavior change methods are discussed.

Evaluating Measurement of Dynamic Constructs: Defining a Measurement Model of Derivatives

While measurement evaluation has been embraced as an important step in psychological research, evaluating measurement structures with longitudinal data is fraught with limitations. This article defines and tests a measurement model of derivatives (MMOD), which is designed to assess the measurement structure of latent constructs both for analyses of between-person differences and for the analysis of change. Simulation results indicate that MMOD outperforms existing models for multivariate analysis and provides equivalent fit to data generation models. Additional simulations show MMOD capable of detecting differences in between-person and within-person factor structures. Model features, applications, and future directions are discussed.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant’s personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual’s data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.