Comparing within-person effects from multivariate longitudinal models.

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between two or more constructs over time, yet the within-person effects tested by each model are distinct. In this paper, we clarify the types of within-person inferences that can be made from each model. Whereas previous research has focused on detecting whether within-person effects exist over development, the present work can be used to understand the nature of these relationships. We compare each modeling approach using an example investigating the concurrent development of mother-child closeness and mother-child conflict. Our findings demonstrate that fundamentally different conclusions about developmental processes may be reached depending on which model is used, and we demonstrate a framework for making sense of seemingly contradictory findings

The Effects of Missing Time-Varying Covariates in Multilevel Models

Multilevel models are commonly used in psychological research to examine developmentally-motivated hypotheses concerning the within- and between- person effects of a time-varying covariate on some outcome. Whereas multilevel models are flexible to accommodate incomplete data on the outcome, missing time-varying covariates present significant challenges to researchers. Unless multiple imputation is used, missing time-varying covariates will lead to a loss of data. This project evaluated the effects of missing time-varying covariates and imputation of missing time-varying covariates in multilevel models using a multifaceted simulation study. My results showed that missing time-varying covariates can lead to biased parameter estimates. However this bias is likely minor compared to bias already present in complete data due to unreliable estimates of the person-mean of the time-varying covariate. The results presented here are clear motivation for researchers to choose alternative estimation strategies that can account for measurement error in the person mean, whether or not time-varying covariates are missing.Master of Art

Advantages of Integrative Data Analysis for Developmental Research

Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis is becoming an increasingly valuable tool. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item level data across multiple studies to make inferences possible both within and across studies and can be used to test questions not possible in individual contributing studies. Some of the potential benefits of IDA include the ability to study longer developmental periods, examine how the measurement of key constructs changes over time, increase subject heterogeneity, and improve statistical power and capability to study rare behaviors. Our goal in this paper is to provide a brief overview of the benefits and challenges of IDA in developmental research and to identify additional resources that provide more detailed discussions of this topic

Judgments of self-identified gay and heterosexual male speakers: Which phonemes are most salient in determining sexual orientation?

While numerous studies have demonstrated that a male speaker’s sexual orientation can be identified from relatively long passages of speech, few studies have evaluated whether listeners can determine sexual orientation when presented with word-length stimuli. If listeners are able to distinguish between self-identified gay and heterosexual male speakers of American English, it is unclear whether they form their judgments based on a phoneme, such as a vowel or consonant, or multiple phonemes, such as a vowel and a consonant. In this study, we first found that listeners can distinguish between self-identified gay and heterosexual speakers of American English upon hearing word-length stimuli. We extended these results in a separate experiment to demonstrate that listeners primarily rely on vowels, and to some extent consonants, when forming their judgments. Listeners were able to differentiate between the two groups of speakers for each of the vowels and three of the seven consonants presented. In a follow-up experiment we found evidence that listeners’ judgments improved if they were presented with multiple phonemes, such as a vowel and /s/. These results provide important information about how different phonemes can provide discriminant information about a male speaker’s sexual orientation

The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals.

Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between two constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful to varying degrees. However, none provide the unambiguous separation of between-person and within-person components of stability and change over time, components that are often hypothesized to exist in the psychological sciences. The goal of our paper is to propose and demonstrate a novel extension of the multivariate latent curve model to allow for the disaggregation of these effects

Estimating HIV transmissions in a large U.S. clinic-based sample: effects of time and syndemic conditions

Introduction: Little is known about onward HIV transmissions from people living with HIV (PLWH) in care. Antiretroviral therapy (ART) has increased in potency, and treatment as prevention (TasP) is an important component of ending the epidemic. Syndemic theory has informed modelling of HIV risk but has yet to inform modelling of HIV transmissions. Methods: Data were from 61,198 primary HIV care visits for 14,261 PLWH receiving care through the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) at seven United States (U.S.) sites from 2007 to 2017. Patient-reported outcomes and measures (PROs) of syndemic conditions – depressive symptoms, anxiety symptoms, drug use (opiates, amphetamines, crack/cocaine) and alcohol use – were collected approximately four to six months apart along with sexual behaviours (mean = 4.3 observations). Counts of syndemic conditions, HIV sexual risk group and time in care were modelled to predict estimated HIV transmissions resulting from sexual behaviour and viral suppression status (HIV RNA \u3c 400/mL) using hierarchical linear modelling. Results: Patients averaged 0.38 estimated HIV transmissions/100 patients/year for all visits with syndemic conditions measured (down from 0.83, first visit). The final multivariate model showed that per 100 patients, each care visit predicted 0.05 fewer estimated transmissions annually (95% confidence interval (CI): 0.03 to 0.06; p \u3c 0.0005). Cisgender women, cisgender heterosexual men and cisgender men of undisclosed sexual orientation had, respectively, 0.47 (95% CI: 0.35 to 0.59; p \u3c 0.0005), 0.34 (95% CI: 0.20 to 0.49; p \u3c 0.0005) and 0.22 (95% CI: 0.09 to 0.35; p \u3c 0.005) fewer estimated HIV transmissions/100 patients/year than cisgender men who have sex with men (MSM). Each within-patient syndemic condition predicted 0.18 estimated transmissions/100 patients/year (95% CI: 0.12 to 0.24; p \u3c 0.0005). Each between-syndemic condition predicted 0.23 estimated HIV transmissions/100 patients/year (95% CI: 0.17 to 0.28; p \u3c 0.0005). Conclusions: Estimated HIV transmissions among PLWH receiving care in well-resourced U.S. clinical settings varied by HIV sexual risk group and decreased with time in care, highlighting the importance of TasP efforts. Syndemic conditions remained a significant predictor of estimated HIV transmissions notwithstanding the effects of HIV sexual risk group and time in care

Interpretational Confounding or Confounded Interpretations of Causal Indicators?

In measurement theory, causal indicators are controversial and little understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning intended by a researcher. This article questions the validity of evidence used to claim that causal indicators are inherently susceptible to interpretational confounding. Further, a simulation study demonstrates that causal indicator coefficients are stable across correctly specified models. Determining the suitability of causal indicators has implications for the way we conceptualize measurement and build and evaluate measurement models

The Impact of Moderate Priors For Bayesian Estimation and Testing of Item Factor Analysis Models When Maximum Likelihood is Unsuitable

In psychological research, available data are often insufficient to estimate item factor analysis (IFA) models using traditional estimation methods, such as maximum likelihood (ML) or limited information estimators. Bayesian estimation with common-sense, moderately informative priors can greatly improve efficiency of parameter estimates and stabilize estimation. There are a variety of methods available to evaluate model fit in a Bayesian framework; however, past work investigating Bayesian model fit assessment for IFA models has assumed flat priors, which have no advantage over ML in limited data settings. In this paper, we evaluated the impact of moderately informative priors on ability to detect model misfit for several candidate indices: posterior predictive checks based on the observed score distribution, leave-one-out cross-validation, and widely available information criterion (WAIC). We found that although Bayesian estimation with moderately informative priors is an excellent aid for estimating challenging IFA models, methods for testing model fit in these circumstances are inadequate.</p