Applying developmental previewing to the principles of family therapy

This paper discusses the application of principles of infant psychiatry to the diagnosis and treatment of multigenerational family conflict. Using a technique referred to as previewing, the therapist can focus on the interpersonal meaning that the infant's development precipitates in the family and determine how the parents' relationships with the infant replicate their relationships with their own families of origin. The therapist may then use these insights for overcoming conflict and for acclimating parents to new developmental skills in an optimal manner. Specific suggestions for how parents may promote more adaptive patterns of interaction with the infant using previewing are offered.     

Measurement bias and error correction in a two-stage estimation for multilevel IRT models

Among current state-of-the-art estimation methods for multilevel IRT models, the two-stage divide-and-conquer strategy has practical advantages, such as clearer definition of factors, convenience for secondary data analysis, convenience for model calibration and fit evaluation, and avoidance of improper solutions. However, various studies have shown that, under the two-stage framework, ignoring measurement error in the dependent variable in stage II leads to incorrect statistical inferences. To this end, we proposed a novel method to correct both measurement bias and measurement error of latent trait estimates from stage I in the stage II estimation. In this paper, the HO-IRT model is considered as the measurement model, and a linear mixed effects model on overall (i.e., higher-order) abilities is considered as the structural model. The performance of the proposed correction method is illustrated and compared via a simulation study and a real data example using the National Educational Longitudinal Survey data (NELS 88). Results indicate that structural parameters can be recovered better after correcting measurement biases and errors.     

汉语口语词汇产生中的多重音韵激活：单词翻译任务的ERP研究

口语词汇产生过程中, 非目标词是否会产生音韵激活是独立两阶段模型和交互激活模型的争论焦点之一。研究运用事件相关电位技术, 考察了被试在翻译命名任务中是否受到背景图片音韵或语义干扰词的影响。行为反应时中未发现显著的音韵效应, 而语义效应显著, 表明非目标词不会产生音韵激活。事件相关电位的结果显示在目标单词呈现后的400~600 ms时间窗口中出现了显著的语义效应, 在600~700 ms时间窗口内出现了边缘显著的语义效应和音韵效应, 均表现为相关条件比无关条件波幅更正。上述结果表明在将英语翻译成汉语的过程中, 尽管在脑电上呈现出可能存在微弱的多重音韵激活, 但行为结果并不会显示出非目标项的音韵激活。研究结果支持了汉语口语词汇产生遵循独立两阶段模式的观点。     

Using bifactor models to identify faking on Big Five questionnaires

To identify faking, bifactor models were applied to Big Five personality data in three studies of laboratory and applicant samples using within‐subjects designs. The models were applied to homogenous data sets from separate honest, instructed faking, applicant conditions, and to simulated applicant data sets containing random individual responses from honest and faking conditions. Factor scores from the general factor in a bifactor model were found to be most highly related to response condition in both types of data sets. Domain factor scores from the faking conditions were found less affected by faking in measurement of Big Five domains than summated scale scores across studies. We conclude that bifactor models are efficacious in assessing the Big Five domains while controlling for faking.     

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known. Furthermore, results from a real-data analysis were used to inform the design of the simulation. Overall, it would be recommended for researchers to utilize the CCMM-LVR model over the HM3-LVR model when individuals are cross-classified, and to use a bare minimum of more than 100 clustering units in order to avoid overestimation of the level-3 variance component estimates.     

More Precise Estimation of Lower-Level Interaction Effects in Multilevel Models

In hierarchical data, the effect of a lower-level predictor on a lower-level outcome may often be confounded by an (un)measured upper-level factor. When such confounding is left unaddressed, the effect of the lower-level predictor is estimated with bias. Separating this effect into a within- and between-component removes such bias in a linear random intercept model under a specific set of assumptions for the confounder. When the effect of the lower-level predictor is additionally moderated by another lower-level predictor, an interaction between both lower-level predictors is included into the model. To address unmeasured upper-level confounding, this interaction term ought to be decomposed into a within- and between-component as well. This can be achieved by first multiplying both predictors and centering that product term next, or vice versa. We show that while both approaches, on average, yield the same estimates of the interaction effect in linear models, the former decomposition is much more precise and robust against misspecification of the effects of cross-level and upper-level terms, compared to the latter.     

Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data

Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities. However, the existence of outliers or influential points may misidentify the hidden states and distort the associated inference. In this study, we develop a Bayesian local influence procedure for HMMs with latent variables in the presence of missing data. The proposed model enables us to investigate the dynamic heterogeneity of multivariate longitudinal data, reveal how the interrelationships among latent variables change from one state to another, and simultaneously conduct statistical diagnosis for the given data, model assumptions, and prior inputs. We apply the proposed procedure to analyze a dataset collected by the UCLA center for advancing longitudinal drug abuse research. Several outliers or influential points that seriously influence estimation results are identified and removed. The proposed procedure also discovers the effects of treatment and individuals’ psychological problems on cocaine use behavior and delineates their dynamic changes across the cocaine-addiction states.     

Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation

There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions. We highlight these advantages through a data set including patients with burn injuries and analyze trajectories of posttraumatic stress symptoms using the Bayesian framework following the steps of the WAMBS-checklist. In the included example, we illustrate how to obtain background information using previous literature based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate the importance of conducting a prior sensitivity analysis. Although our example is from the trauma field, the techniques we illustrate can be applied to any field.     

Superstars are not necessarily role models: Morality perceptions moderate the impact of competence perceptions on supervisor role modeling

There is a general assumption that we choose role models from the ranks of those who have demonstrated extraordinary competence. However, the person perception literature supports the expectation that morality may also matter, and that we may be most likely to role model competent individuals if we also believe that they have good moral character. To test this possibility, we conducted four studies of adults’ role modeling of workplace supervisors. Study 1 (N = 245) and Study 2 (N = 110) showed that workplace supervisors’ perceived competence was most strongly associated with role model perceptions when the supervisor was also seen as moral. Study 3 (N = 492) and 4 (N = 335) replicated these findings with pre‐registered experiments, and revealed indirect effects of supervisor attributes on role modeling through emulation. Results suggest that we choose organizational role models who have achieved success in ways that are in line with our moral values.     

TEACHING SCIENCE AND RELIGION IN THE TWENTY‐FIRST CENTURY: THE MANY PEDAGOGICAL ROLES OF CHRISTOPHER SOUTHGATE

With the goal of understanding how Christopher Southgate communicates his in‐depth knowledge of both science and theology, we investigated the many roles he assumes as a teacher. We settled upon wide‐ranging topics that all intertwine: (1) his roles as author and coordinating editor of a premier textbook on science and theology, now in its third edition; (2) his oral presentations worldwide, including plenaries, workshops, and short courses; and (3) the team teaching approach itself, which is often needed by others because the knowledge of science and theology do not always reside in the same person. Southgate provides, whenever possible, teaching contexts that involve students in experiential learning, where they actively participate with other students. We conclude that Southgate's ultimate goal is to teach students how to reconcile science and theology in their values and beliefs, so that they can take advantage of both forms of rational thinking in their own personal and professional lives. The co‐authors consider several examples of models that have been successfully used by people in various fields to integrate science and religion.