Testing a bilinear domain-specific model of acculturation and enculturation across generational status

This study attempted to replicate Miller's (2007) finding that a bilinear domain-specific model of Asian American acculturation demonstrated superior model fit when compared to unilinear and bilinear domain-generic models. Current confirmatory factor analytic tests of competing acculturation models in a cross-validation sample of 306 participants were consistent with Miller's findings. In addition, this study provided novel findings regarding the nature of the acculturation process by testing the bilinear domain-specific model across 1st and 2nd generation samples. Specifically, the generational status moderator hypothesis-that a unilinear model of acculturation would be most appropriate for 1st generation individuals and a bilinear model of acculturation would be most appropriate for 2nd generation individuals-was tested with 494 1st and 2nd generation Asian Americans. Contrary to the assumptions of the generational status moderator hypothesis, present findings demonstrated the appropriateness of the bilinear domain-specific model for both 1st and 2nd generation Asian Americans. Present findings also revealed a pattern of shared and unique relationships between cultural orientations and acculturation domains across generations. Implications for research and counseling are explored.     

A latent class approach to fitting the weighted Euclidean model,clascal

A weighted Euclidean distance model for analyzing three-way proximity data is proposed that incorporates a latent class approach. In this latent class weighted Euclidean model, the contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. This model removes the rotational invariance of the classical multidimensional scaling model retaining psychologically meaningful dimensions, and drastically reduces the number of parameters in the traditional INDSCAL model. The probability density function for the data of a subject is posited to be a finite mixture of spherical multivariate normal densities. The maximum likelihood function is optimized by means of an EM algorithm; a modified Fisher scoring method is used to update the parameters in the M-step. A model selection strategy is proposed and illustrated on both real and artificial data.The second author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek.     

Modelling inter-individual differences in latent within-person variation: The confirmatory factor level variability model

Psychological theories often produce hypotheses that pertain to individual differences in within-person variability. To empirically test the predictions entailed by such hypotheses with longitudinal data, researchers often use multilevel approaches that allow them to model between-person differences in the mean level of a certain variable and the residual within-person variance. Currently, these approaches can be applied only when the data stem from a single variable. However, it is common practice in psychology to assess not just a single measure but rather several measures of a construct. In this paper we describe a model in which we combine the single-indicator model with confirmatory factor analysis. The new model allows individual differences in latent mean-level factors and latent within-person variability factors to be estimated. Furthermore, we show how the model's parameters can be estimated with a maximum likelihood estimator, and we illustrate the approach using an example that involves intensive longitudinal data.     

Consumer segments in social commerce: A latent class approach

Social commerce is thriving; therefore, it becomes essential to recognize different types of social commerce shopper. In this study, we assume that social networking sites such as Facebook contain differently behaving consumer segments. Based on the assumption that the majority of social commerce shoppers are young adults, we are interested in identifying unobservable shopper segments that the young group may contain. A conceptual model has been tested that incorporates the psychological factor (trust), the unified theory of acceptance and the use of technology (UTAUT2), and behavioral aspects using mixture modeling techniques to reveal the unobservable consumer segments who have searched for clothing items through Facebook when shopping (n = 309). Three segments were found: social patrons (n = 219), wary explorers (n = 72), and sporadic explorers (n = 18). The study is a rare attempt to specifically categorize social commerce shopper segments by using mixture modeling techniques. The study offers further research avenues. Copyright © 2016 John Wiley & Sons, Ltd.     

Exploring types of career orientation: A latent class analysis approach

Career literature has been discussing the decline of the traditional career. Despite this debate, systematic information on the prevalence of contemporary career types is lacking. Two studies with large samples of employees aimed to determine types of career orientation, to explore their prevalence, and to validate these types by testing hypotheses relating each of the career orientations to work attitudes and sociodemographical variables. In study 1 (N = 835), we identified four types of career orientation – traditional/promotion, traditional/loyalty, independent, disengaged – applying exploratory latent class analysis. These were confirmed in study 2 (N = 737) with confirmatory latent class analysis. The variables associated with the career orientation types mostly followed the predicted pattern. Almost two thirds reported a traditional career orientation, while one fifth each expressed an independent and a disengaged orientation. This finding shows that people’s career orientation does not reflect the changes that many authors argue have been occurring.     

Exploring types of career orientation: A latent class analysis approach

Career literature has been discussing the decline of the traditional career. Despite this debate, systematic information on the prevalence of contemporary career types is lacking. Two studies with large samples of employees aimed to determine types of career orientation, to explore their prevalence, and to validate these types by testing hypotheses relating each of the career orientations to work attitudes and sociodemographical variables. In study 1 (N = 835), we identified four types of career orientation – traditional/promotion, traditional/loyalty, independent, disengaged – applying exploratory latent class analysis. These were confirmed in study 2 (N = 737) with confirmatory latent class analysis. The variables associated with the career orientation types mostly followed the predicted pattern. Almost two thirds reported a traditional career orientation, while one fifth each expressed an independent and a disengaged orientation. This finding shows that people’s career orientation does not reflect the changes that many authors argue have been occurring.     

A latent class model for rating data

A latent class model for rating data is presented which is the analogue of Andrich's binomial Rasch model for Lazarsfeld's latent class analysis (LCA). The response probabilities for the rating categories follow a binomial distribution and depend on class-specific item parameters. The EM-algorithm for parameter estimation as well as goodness of fit tests for the model are described. An example using questionnaire items on interest in physics illustrates the use of the model as an alternative to the latent trait approach of analyzing test data.I would like to thank Clifford Clogg and the anonymous reviewers for their helpful comments.     

The comorbidities of adolescent problem behaviors: A latent class model

The correlations and comorbidities of a series of adolescent problem behaviors were studied in a sample of 739 New Zealand 15-year-olds. This analysis revealed the presence of strong comorbidities between different problem behaviors. The data were modeled using methods of unrestricted latent class analysis and this suggested that the best fitting model to describe the data was one which assumed that adolescent problem behaviors were described by four general classes of children. While the same general four-class model applied to males and females, there were marked gender differences in the rates of problems. Specifically, the predominant problem behaviors in females were those relating to an accelerated transition to adulthood marked by early sexual activity, alcohol abuse, and cannabis use whereas the predominant problems for boys were related to antisocial and law-breaking behaviors. Rates of children with no problems (85%) and with multiple problems (3%) were similar for boys and girls.This research was funded by grants from the Health Research Council of New Zealand, the Canterbury Medical Research Foundation, and the National Child Health Research Foundation.     

Training ethical psychologists: an acculturation model

This article presents an approach to graduate (and professional) training that views becoming an ethical psychologist as an acculturation process. J.W. Berry's (1980, 2003) model of acculturation strategies is used as a framework for understanding ethical acculturation, a developmental process during which students can use several types of adaptation strategies. Students enter training with their own moral value traditions and concepts but are confronted with new ethical principles and rules, some of which may be inconsistent with their ethics of origin. The article explores several applications of the framework to ethics courses, practicum supervision, and other areas of training.     

A note on the derivation of the general latent class model

A general formulation of the latent structure principle is suggested, from which it is possible to derive Lazarsfeld's accounting equations in their most general form. The basic equations of Gibson's latent profile model can thence be derived in a single step.     

Psychological resilience and dysfunction among hospitalized survivors of the SARS epidemic in Hong Kong: a latent class approach

OBJECTIVE: To examine trajectories of psychological functioning using latent class analysis on a sample of hospitalized survivors of the 2003 severe acute respiratory syndrome (SARS) epidemic in Hong Kong. DESIGN: A longitudinal study of 997 survivors, recruited from among 1,331 individuals hospitalized for SARS, were interviewed at 6, 12, and 18 months after hospitalization. MAIN OUTCOME MEASURES: Psychological and physical functioning at each time point was measured using the 12-item Medical Outcome Study Short-Form Health Survey (SF-12). RESULTS: Four latent classes were identified--chronic dysfunction, delayed dysfunction, recovery, and resilience. All groups had better physical health than the chronic group. Resilient and recovered individuals had greater social support and less SARS-related worry, and resilient individuals were more likely to be male. The resilient group also had greater social support than the delayed group and better physical functioning than the recovered group. CONCLUSION: This study demonstrated that longitudinal outcome trajectories following a major health-threat event in an Asian sample bear close resemblance to prototypical trajectories observed in trauma studies using Western samples. Unique predictors of the trajectories included factors observed in previous studies, such as social support, as well as factors of particular relevance to a major disease outbreak, such as SARS-related worry.     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.     

A latent class unfolding model for analyzing single stimulus preference ratings

A multidimensional unfolding model is developed that assumes that the subjects can be clustered into a small number of homogeneous groups or classes. The subjects that belong to the same group are represented by a single ideal point. Since it is not known in advance to which group of class a subject belongs, a mixture distribution model is formulated that can be considered as a latent class model for continuous single stimulus preference ratings. A GEM algorithm is described for estimating the parameters in the model. The M-step of the algorithm is based on a majorization procedure for updating the estimates of the spatial model parameters. A strategy for selecting the appropriate number of classes and the appropriate number of dimensions is proposed and fully illustrated on some artificial data. The latent class unfolding model is applied to political science data concerning party preferences from members of the Dutch Parliament. Finally, some possible extensions of the model are discussed.The first author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek. Part of this paper was presented at the Distancia meeting held in Rennes, France, June 1992.     

A data-driven approach to neuropsychological features in isolated REM behaviour disorder: A latent class analysis

Recent evidence demonstrated that neuropsychological assessment may be considered a valid marker of neurodegeneration in idiopathic REM sleep behaviour disorder (iRBD). However, little is known about the possible neuropsychological heterogeneity within the iRBD population. This retrospective study aimed to identify and describe different neuropsychological phenotypes in iRBD patients by means of a data-driven approach using latent class analysis. A total of 289 iRBD patients underwent a neuropsychological assessment evaluating cognitive domains: global cognition, language, short- and long-term memory, executive functions and visuospatial abilities. The presence of mild cognitive impairment (MCI) was also assessed. Latent class analysis was carried out to identify iRBD subtypes according to neuropsychological scores. The most parsimonious model identified three latent classes. Groups were labelled as follows: Class 2 “severely impaired” (n = 83/289): mean pathological scores in different tests, a high percentage of MCI multiple-domain and impairment in all neuropsychological domains. Class 1 “moderately impaired” (n = 44/289): mean neuropsychological score within the normal value, a high percentage of MCI (high risk to phenoconversion) and great impairment in the visuospatial domain. Class 3 “slightly impaired” (n = 162/289): no deficit worthy of attention except for short- and long-term memory. Our results suggest three different clinical phenotypes within the iRBD population. These findings may be relevant in the future for predicting the clinical trajectories of phenoconversion in iRBD.     

A latent class model for individual differences in the interpretation of conditionals

Ohne Zusammenfassung     

Alexithymia and psychosocial problems among Italian preadolescents. A latent class analysis approach

The study, conducted on Italian preadolscents aged 11 to 13 belonging to the general population, aims to investigate the relationship between the emotional functioning, namely, alexithymia, and the risk of developing behavioral and emotional problems measured using the Strength and Difficulty Questionnaire. The latent class analysis approach allowed to identify two latent variables, accounting for the internalizing (emotional symptoms and difficulties in emotional awareness) and for the externalizing problems (conduct problems and hyperactivity, problematic relationships with peers, poor prosocial behaviors and externally oriented thinking). The two latent variables featured two latent classes: the difficulty in dealing with problems and the strength to face problems that was representative of most of the healthy participants with specific gender differences. Along with the analysis of psychopathological behaviors, the study of resilience and strengths can prove to be a key step in order to develop valuable preventive approaches to tackle psychiatric disorders.     

The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies

In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach, in which the observed (manifest) group mean is assumed to be perfectly reliable. This article demonstrates mathematically and with simulation results that this MMC approach can result in substantially biased estimates of contextual effects and can substantially underestimate the associated standard errors, depending on the number of L1 individuals per group, the number of groups, the intraclass correlation, the sampling ratio (the percentage of cases within each group sampled), and the nature of the data. To address this pervasive problem, the authors introduce a new multilevel latent covariate (MLC) approach that corrects for unreliability at L2 and results in unbiased estimates of L2 constructs under appropriate conditions. However, under some circumstances when the sampling ratio approaches 100%, the MMC approach provides more accurate estimates. Based on 3 simulations and 2 real-data applications, the authors evaluate the MMC and MLC approaches and suggest when researchers should most appropriately use one, the other, or a combination of both approaches.     

Evaluation of latent construct correlations in the presence of missing data: a note on a latent variable modelling approach

A latent variable modelling approach is discussed, which can be used to evaluate indices of linear relationship between latent constructs in incomplete data sets. The method is based on an application of maximum-likelihood estimation and inclusion of covariates predictive of missing values. The approach can be employed for point and interval estimation of latent correlations in the presence of missing data, and capitalizes on enhanced plausibility of the assumption of data missing at random through introduction of informative covariates. The method is illustrated on empirical data.