Claudia Crayen

Exploring dynamics in mood regulation--mixture latent Markov modeling of ambulatory assessment data.

Objective To illustrate how fluctuation patterns in ambulatory assessment data with features such as few categorical items, measurement error, and heterogeneity in the change pattern can adequately be analyzed with mixture latent Markov models. The identification of fluctuation patterns can be of great value to psychosomatic research concerned with dysfunctional behavior or cognitions, such as addictive behavior or noncompliance. In our application, unobserved subgroups of individuals who differ with regard to their mood regulation processes, such as mood maintenance and mood repair, are identified. Methods In an ambulatory assessment study, mood ratings were collected 56 times during 1 week from 164 students. The pleasant-unpleasant mood dimension was assessed by the two ordered categorical items unwell-well and bad-good. Mixture latent Markov models with different number of states, classes, and degrees of invariance were tested, and the best model according to information criteria was interpreted. Results Two latent classes that differed in their mood regulation pattern during the day were identified. Mean classification probabilities were high (>0.88) for this model. The larger class showed a tendency to stay in and return to a moderately pleasant mood state, whereas the smaller class was more likely to move to a very pleasant mood state and to stay there with a higher probability. Conclusions Mixture latent Markov models are suitable to obtain information about interindividual differences in stability and change in ambulatory assessment data. Identified mood regulation patterns can serve as reference for typical mood fluctuation in healthy young adults. Abbreviations AA = ambulatory assessment MLM = mixture latent Markov MMQ = Multidimensional Mood Questionnaire BIC = Bayesian Information Criterion AIC3 = modified Akaike Information Criterion

Evaluating Interventions with Multimethod Data: A Structural Equation Modeling Approach

In many intervention and evaluation studies, outcome variables are assessed using a multimethod approach comparing multiple groups over time. In this article, we show how evaluation data obtained from a complex multitrait–multimethod–multioccasion–multigroup design can be analyzed with structural equation models. In particular, we show how the structural equation modeling approach can be used to (a) handle ordinal items as indicators, (b) test measurement invariance, and (c) test the means of the latent variables to examine treatment effects. We present an application to data from an evaluation study of an early childhood prevention program. A total of 659 children in intervention and control groups were rated by their parents and teachers on prosocial behavior and relational aggression before and after the program implementation. No mean change in relational aggression was found in either group, whereas an increase in prosocial behavior was found in both groups. Advantages and limitations of the proposed approach are highlighted.

It's in the mix: psychological distress differs between combinations of alexithymic facets.

Alexithymia is a personality trait characterized by difficulties in identifying, describing, and communicating ones emotions. The aim of the present study is to examine the usefulness of a typological approach considering the interaction between distinct alexithymic features within a population of high-alexithymic German adults (N = 217). Latent profile analysis (LPA) was employed to test for possible underlying profiles. A 3-profile solution showed the best fit: The profiles can be described as (1) “low”: lower load on all facets of alexithymia, (2) “mixed”: specific problems on identifying emotions, and (3) “high”: higher load on all facets of alexithymia. Moreover, this study tested how these profiles differed in psychological distress. “Mixed” profile, with specific problems on identifying emotions showed the highest levels of psychological distress. The present study suggests the importance of a specific combination of alexithymic features, rather than total alexithymia scores, as a risk factor for psychological distress.

The assessment of emotional clarity via response times to emotion items: shedding light on the response process and its relation to emotion regulation strategies

ABSTRACT Researchers have begun to use response times (RTs) to emotion items as an indirect measure of emotional clarity. Our first aim was to scrutinise the properties of this RT measure in more detail than previously. To be able to provide recommendations as to whether (and how) emotional intensity – as a possible confound – should be controlled for, we investigated the specific form of the relation between emotional intensity and RTs to emotion items. In particular, we assumed an inverted U-shaped relation at the item level. Moreover, we analysed the RT measure’s convergent validity with respect to individuals’ confidence in their emotion ratings. As a second aim, we compared the predictive validity of emotional clarity measures (RT measure, self-report) with respect to daily emotion regulation. The results of three experience sampling studies showed that the association between emotional intensity and RT followed an inverted U shape. RT was in part related to confidence. Emotional clarity measures were unrelated to reappraisal. There was some evidence that lower emotional clarity was related to a greater use of suppression. The findings highlight that emotional intensity and squared emotional intensity should be controlled for when using the RT measure of emotional clarity in future research.

A Continuous-Time Mixture Latent-State-Trait Markov Model for Experience Sampling Data

In psychological research, statistical models of latent state-trait (LST) theory are popular for the analysis of longitudinal data. We identify several limitations of available models when applied to intensive longitudinal data with categorical observed and latent variables and inter- and intraindividually varying time intervals. As an extension of available LST models for categorical data, we describe a general mixed continuous-time LST model that is suitable for intensive longitudinal data with unobserved heterogeneity and individually varying time intervals. This model is illustrated by an application to momentary mood data that were collected in an experience sampling study (N = 164). In addition, the results of a simulation study are reported that was conducted to find out (a) the minimal data requirements with respect to sample size and number of occasions, and (b) how strong the bias is if the continuous-time structure is ignored. The empirical application revealed two classes for which the transition pattern and effects of time-varying covariates differ. In the simulation study, only small differences between the continuous-time model and its discrete-time counterpart emerged. Sample sizes N = 100 and larger in combination with six or more occasions of measurement tended to produce reliable estimation results. Implications of the models for future research are discussed.

Using a Mixed IRT Model to Assess the Scale Usage in the Measurement of Job Satisfaction

This study investigated the adequacy of a rating scale with a large number of response categories that is often used in panel surveys for assessing diverse aspects of job satisfaction. An inappropriate scale usage is indicative of overstraining respondents and of diminished psychometric scale quality. The mixed Item Response Theory (IRT) approach for polytomous data allows exploring heterogeneous patterns of inappropriate scale usage in form of avoided categories and response styles. In this study, panel data of employees (n = 7036) on five aspects of job satisfaction measured on an 11-point rating scale within the “Household, Income and Labor Dynamics in Australia” (wave 2001) were analyzed. A three-class solution of the restricted mixed generalized partial credit model fit the data best. The results showed that in no class the 11-point scale was appropriately used but that the number of categories used was reduced in all three classes. Respondents of the large class (40%) appropriately differentiate between up to six categories. The two smaller classes (33 and 27%) avoid even more categories and show some kind of extreme response style. Furthermore, classes differ in socio-demographic and job-related factors. In conclusion, a two- to six-point scale without the middle point might be more adequate for assessing job satisfaction.