Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models

Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study are as follows: (1) to develop a PGMM using a Bayesian inference approach that allows the estimation of multiple random changepoints within each class; (2) to develop a procedure to empirically detect the number of random changepoints within each class; and (3) to empirically investigate the bias and precision of the estimation of the model parameters, including the random changepoints, via a simulation study. We have developed the user-friendly package BayesianPGMM for R to facilitate the adoption of this methodology in practice, which is available at https://github.com/lockEF/BayesianPGMM. We describe an application to mouse-tracking data for a visual recognition task.     

A Finite Mixture of Nonlinear Random Coefficient Models for Continuous Repeated Measures Data

Nonlinear random coefficient models (NRCMs) for continuous longitudinal data are often used for examining individual behaviors that display nonlinear patterns of development (or growth) over time in measured variables. As an extension of this model, this study considers the finite mixture of NRCMs that combine features of NRCMs with the idea of finite mixture (or latent class) models. The efficacy of this model is that it allows the integration of intrinsically nonlinear functions where the data come from a mixture of two or more unobserved subpopulations, thus allowing the simultaneous investigation of intra-individual (within-person) variability, inter-individual (between-person) variability, and subpopulation heterogeneity. Effectiveness of this model to work under real data analytic conditions was examined by executing a Monte Carlo simulation study. The simulation study was carried out using an R routine specifically developed for the purpose of this study. The R routine used maximum likelihood with the expectation–maximization algorithm. The design of the study mimicked the output obtained from running a two-class mixture model on task completion data.     

Religion Benefiting Brain Tumour Patients: A Qualitative Study

As the focus on modern neurosurgery has shifted to the realm of technological advancement, some patients and their loved ones still hold a strong faith in their religion to guide them through the process. This study aimed to determine whether religion as a coping mechanism was beneficial for patients before, during and after craniotomy. Qualitative case study methodology was used. Interviews were conducted with randomly selected 36 adult patients who underwent surgery for a benign or malignant brain tumour. Interviews were audio recorded and transcribed, and the data subjected to thematic analysis. Four overarching themes emerged from the data: (1) religion significantly benefited neurosurgical patients; (2) neurosurgical patients did not require a dedicated religious room in the hospital; (3) neurosurgical patients required religious resources such as leaders and/or groups; and (4) patients were not in favour of their physician engaging in the religious ritual. Most patients found religion to be an effective coping mechanism, offering them strength, comfort, and hope through the surgery. The findings from this study emphasize the need for including a “religious time-out” before and after surgery and the inclusion of religious leaders/groups for those in favour to ensure quality care and patient satisfaction.     

Modeling Growth in Latent Variables Using a Piecewise Function

Latent growth curve models with piecewise functions for continuous repeated measures data have become increasingly popular and versatile tools for investigating individual behavior that exhibits distinct phases of development in observed variables. As an extension of this framework, this research study considers a piecewise function for describing segmented change of a latent construct over time where the latent construct is itself measured by multiple indicators gathered at each measurement occasion. The time of transition from one phase to another is not known a priori and thus is a parameter to be estimated. Utility of the model is highlighted in 2 ways. First, a small Monte Carlo simulation is executed to show the ability of the model to recover true (known) growth parameters, including the location of the point of transition (or knot), under different manipulated conditions. Second, an empirical example using longitudinal reading data is fitted via maximum likelihood and results discussed. Mplus (Version 6.1) code is provided in Appendix C to aid in making this class of models accessible to practitioners.     

Anxiety and adjustment levels among the visually and hearing impaired and their relationship to locus of control, cognitive, social, and biographical variables

Forty-five hearing-impaired and 40 visually impaired male residential school children aged 6 to 16 years were administered measures of adjustment, anxiety, and locus of control (LOC). Scores related to intelligence and academic level, teacher and peer acceptance, and biographical variables were also obtained. The findings show that the correlates of anxiety and adjustment differed qualitatively as well as quantitatively between the groups.     

Motivational and Cultural Variations in Mortality Salience Effects: Contemplations on Terror Management Theory and Consumer Behavior

Terror management theory provides a viable framework to examine the effects of mortality salience (MS) and related coping behaviors. We explore the utility of viewing MS effects from an information processing perspective and discuss the implications for understanding consumer behavior. We suggest that a better understanding of the motivational nature of MS and the underlying processes could provide additional insights on the persuasive impact of MS. We also anticipate effects of MS to differ across cultures and predict some novel outcomes. Finally, we outline several potential avenues for investigating MS effects in consumer domains such as branding and advertising.     

A comparison of a brief and long version of the Situational Confidence Questionnaire

Assessing confidence to resist drinking in high risk situations is an important part of behavioral treatments for alcohol problems. The present study assessed the reliability and validity of the original 100-item Situational Confidence Questionnaire (SCQ) and of an 8-item brief version (BSCQ). Using a visual analog scale, the BSCQ asked respondents to report their confidence to resist urges to drink heavily using the original eight SCQ subscales (e.g., pleasant times with others, social pressure). Data were collected from 120 adult problem drinkers who voluntarily entered an outpatient alcohol treatment program. The comparability of the BSCQ and the SCQ-100 was evaluated through intraclass correlations between the eight subscales and comparison of both instruments' underlying factor structures. Correlation coefficients for the subscales ranged from 0.56 to 0.80. Both instruments showed similar, but not identical factor structures. The present results indicate that the BSCQ provides comparable information to the SCQ-100. Limitations, as well as the clinical advantages, of using the BSCQ over longer versions are discussed.