Distinguishing state variability from trait change in longitudinal data: the role of measurement (non)invariance in latent state-trait analyses

Comparing the latent state-trait structure of the PANSS in cariprazine-medicated and placebo-controlled patients with acute schizophrenia

After over a hundred years of research, the question whether the symptoms of schizophrenia are rather trait-like (being a relatively stable quality of individuals) or state-like (being substance to change) is still unanswered. To assess the trait and the state component in patients with acute schizophrenia, one group receiving antipsychotic treatment, the other not. Data from four phase II/III, 6-week, randomized, double-blind, placebo-controlled trials of similar design that included patients with acute exacerbation of schizophrenia were pooled. In every trial, one treatment group received a third-generation antipsychotic, cariprazine, and the other group placebo. To assess symptoms of schizophrenia, the Positive and Negative Symptom Scale (PANSS) was applied. Further analyses were conducted using the five subscales as proposed by Wallwork and colleagues. A latent state-trait (LST) model was developed to estimate the trait and state components of the total variance of the observed scores. All symptom dimensions behaved more in a trait-like manner. The proportions of all sources of variability changed over the course of the observational period, with a bent around weeks 3 and 4. Visually inspected, no major differences were found between the two treatment groups regarding the LST structure of symptom dimensions. This high proportion of inter-individual stability may represent an inherent part of symptomatology that behaves independently from treatment status.

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

Adaptation and validation of the Chinese version of the Sleep Quality Questionnaire

STUDY OBJECTIVES

Sleep quality is essential to health. The current study aimed to adapt and validate the Sleep Quality Questionnaire (SQQ) into Chinese language.

METHODS

The Chinese version of the SQQ (SQQ-C) was created following the guidelines for cross-cultural adaptation. Compliant with the COSMIN methodology, baseline data (N = 13,325) examined three validity domains and internal consistency, including content validity using the content validity index (CVI) and the cognitive debriefing and focus group (relevance, comprehensiveness and comprehensibility), construct validity using structural validity and cross‑sectional measurement invariance, and criterion validity using concurrent/convergent validity. Follow-up data (N = 3410) gathered within a mean of 168 (167-207) h interval were used to additionally assess longitudinal measurement invariance and test-retest reliability using intraclass correlation coefficient (ICC).

RESULTS

Scale-level CVI/Average was equal to 0.922; Item-level CVIs ranged from 0.889 to 1.000 (excellent), except for item 2 (0.556-fair). A panel of local experts and local participants during cognitive debriefing and focus group stated that it had sufficient relevance and comprehensibility but a slight deficiency in comprehensiveness. Confirmatory factor analysis indicated a stable two-factor structure encompassing Daytime Sleepiness Subscale and Sleep Difficulty Subscale from baseline to follow-up data. The SQQ-C-9 (without item 2) outperformed the SQQ-C-10 (full form). The SQQ-C-9 provided evidence of measurement invariance (strict) across subgroups (cohorts, gender, and age) and across time. The SQQ-C was negatively correlated with the Chinese Nonrestorative Sleep Scale and the Chinese Sleep Condition Indicator. Cronbach's alpha (α), McDonald's Omega (ω), and ICC, respectively, ranged from 0.712 to 0.838, 0.723 to 0.840, and 0.738 to 0.764 for total scale and each subscale.

CONCLUSION

The SQQ-C exhibits adequate psychometric properties and a stable two-factor structure, and should enable valuable assessments of sleep quality in clinical and research settings.

Digital Life Data in the Clinical Whitespace

In our increasingly digital world, aspects of our lives are encoded in the routine interactions we have with technology. Over the past few years, psychologists and technologists have been exploring what possibilities these digital life data might hold for improving mental health and well-being. Here I examine some of the recent advances in this field, particularly in the use of language data; consider the ethical and pragmatic implications of this technology; and examine a few areas where I believe these advances could significantly alter the way in which mental health and well-being are approached. This technology holds special promise for providing information about a patient’s life in between clinical encounters, in the clinical whitespace.

Applying A Biopsychosocial Framework to Achieve Durable Behavior Change in Kidney Disease

Chronic disease self-management is the establishment and maintenance of behaviors needed to be an active participant in one's health care and experience the best health outcomes. Kidney disease self-management behaviors to slow disease progression include engaging in exercise or physical activity; adhering to a diet low in sodium, potassium, and phosphorus; monitoring laboratory parameters; managing complex medication regimens; coping with disease-related emotional distress; and communicating effectively with providers. Durable behavior change has been difficult to achieve in kidney disease, in part because of an incomplete understanding of the multilevel factors determining chronic disease self-management in this patient group. The biopsychosocial model of chronic illness care posits that an individual's health outcomes result from biological, psychological, social, and environmental factors as part of a multilevel systems hierarchy. Although this theoretical model has been used to comprehensively identify factors driving self-management in other chronic conditions, it has been applied infrequently to behavioral interventions in kidney disease. In this scoping review, we apply the biopsychosocial model of health to identify individual, interpersonal, and systems-level drivers of kidney disease self-management behaviors. We further highlight factors that may serve as novel, impactful targets of theory-based behavioral interventions to understand and sustain behavior change in kidney disease.

The Development of Vocational Interests in Early Adolescence: Stability, Change, and State-Trait Components

Vocational interests predict major life outcomes such as job performance, college major choice, and life goals. It is therefore important to gain a better understanding of their development during the crucial years of late childhood and early adolescence, when trait-like interests are starting to develop. The present study investigated the development of vocational interests in a longitudinal sample, comprising N = 3,876 participants—assessed at four time points from ages 11 to 14. Stability, state-trait variance components, mean-level development, and gender differences in mean-levels of Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (RIASEC) dimensions were examined. Stabilities were moderate for all dimensions, but Realistic, Investigative, Social, and Conventional interests became more stable over time. For Realistic, Artistic, Social, and Conventional interests, the trait variance increased over time. At age 14, all dimensions had substantial trait variance components. The mean-levels of Realistic, Investigative, Artistic, and Conventional interests decreased over the 3 years (–0.44 <  d < –0.24). Initial gender differences—with girls having higher Artistic and Social interests and boys having higher Realistic and Investigative interests—increased over time. By investigating the development of vocational interests in late childhood and early adolescence, we complement previous findings and provide first insights about state-trait proportions in early adolescence.

The reciprocal relationships between social media self-control failure, mindfulness and wellbeing: A longitudinal study

This paper aims to shed light on the question whether, and how, social media self-control failure is related to mindfulness and wellbeing. Using a 3-wave longitudinal design, the present study among 594 daily social media users examined the reciprocal relationships between social media self-control failure and mindfulness, and between social media self-control failure and wellbeing (as assessed by subjective vitality and life satisfaction). Results of the random-intercept cross-lagged panel model showed that social media self-control failure has a time-invariant negative association with mindfulness and subjective vitality. No full reciprocal influence was found between social media self-control failure and mindfulness, yet part of this trajectory was observed, suggesting that social media self-control failure could impair mindfulness, which, in turn, might increase future social media self-control failure. For wellbeing, life satisfaction was found to predict subsequent drops in social media self-control failure.

Heart Rate Variability During Physical Exercise Is Associated With Improved Cognitive Performance in Alzheimer's Dementia Patients-A Longitudinal Feasibility Study

Heart rate variability (HRV) rapidly gains attention as an important marker of cardiovascular autonomic modulation. Moreover, there is evidence for a link between the autonomic deficit measurable by reduced HRV and the hypoactivity of the cholinergic system, which is prominently affected in Alzheimer's disease (AD). Despite the positive influence of physical exercise on cognition and its promising association with HRV, previous studies did not explore the effect of long-term physical exercise in older adults with AD. Taking advantage of a longitudinal study we analyzed the effect of a 20-week dual task training regime (3 × 15-min per week) on the vagal mediated HRV index RMSSD (root mean square of successive RR interval differences) during physical exercise and the short-term memory performance in a AD cohort (N = 14). Each training contained physical exercise on a bicycle ergometer while memorizing 30 successively presented pictures as well as the associated post-exercise picture recognition memory test. Linear-mixed modeling revealed that HRV-RMSSD significantly increased over the intervention time. Moreover, the reaction time in the picture recognition task decreased while the accuracy remained stable. Furthermore, a significantly negative relationship between increased fitness measured by HRV-RMSSD and decreased reaction time was observed. This feasibility study points to the positive effects of a dual task regime on physical and cognitive fitness in a sample with impaired cognitive performance. Beyond this, the results show that the responsiveness of parasympathetic system as measured with HRV can be improved in patients with dementia.

The association of changes in repetitive negative thinking with changes in depression and anxiety

BACKGROUND

Repetitive negative thinking (RNT) is a common feature of different mental disorders in the affective spectrum. Most measures of RNT are disorder-specific and measure e.g. rumination in depression or worry in anxiety.

METHODS

In the Netherlands Study of Depression and Anxiety (NESDA), 1820 adults completed the Perseverative Thinking Questionnaire to assess content-independent RNT over a 3-year follow-up period. We investigated the relative stability of content-independent RNT (Perseverative Thinking Questionnaire), over time as well as the association between changes in RNT and changes in affective disorder status (Composite International Diagnostic Interview) and depressive and anxiety severity (Inventory of Depressive Symptomatology, Beck Anxiety Inventory, Fear Questionnaire).

RESULTS

In the total group, baseline RNT was strongly related to RNT three years later, while the difference between the scores at baseline and three years later was negligible. Increases and decreases in RNT were associated with the occurrence and recovery of affective disorders, respectively. Furthermore, changes in RNT between baseline and three years later were associated with corresponding changes in depression, anxiety, and avoidance symptom severity. These associations were small or negligible.

LIMITATIONS

Our findings may not be representative of all affective disorders as individuals with an obsessive-compulsive disorder or bipolar disorder were excluded from our sample.

CONCLUSIONS

The findings suggest that RNT is not primarily an index of disorder status or epiphenomenon of symptom severity and may constitute a relatively stable transdiagnostic person characteristic.

Psychometric properties of instruments assessing exercise in patients with eating disorders: a systematic review

BACKGROUND

Research has identified factors specific to exercise in eating disorder patients such as affect regulation and compulsivity. Existing measures of exercise behaviour which were not originally designed for eating disorder patients may not adequately assess these factors. The aim of this systematic review is to identify and assess the psychometric properties of all self-report measures of exercise designed to be used with eating disorder patients.

METHOD

A systematic review was conducted following the PRISMA guidelines. MedLine, Scopus and PsycINFO were systematically searched. A total of 12 studies examining two measures, the Exercise and Eating Disorders and the Compulsive Exercise Test, met inclusion criteria.

RESULTS

Validation studies showed promising results for both tests and established internal consistency, concurrent and convergent validity, and construct validity. The factor structure of the Compulsive Exercise Test was not confirmed in the majority of the studies included in this review, while there are only two studies conducting factor analysis on the Exercise and Eating Disorders.

CONCLUSION

The two measures identified by this systematic review represent the current research on measures of compulsive exercise for eating disorder patients. Further research is needed to confirm a factor structure and validate both the Compulsive Exercise Test and the Exercise and Eating Disorders in more diverse clinical samples.

How Consistent Is Sluggish Cognitive Tempo Across Occasions, Sources, and Settings? Evidence From Latent State-Trait Modeling

Research has yet to determine how much of the variance in sluggish cognitive tempo (SCT) symptom ratings is consistent across occasions, sources, and settings versus specific to occasion, source, and setting. Our first objective was to determine the amount of variance in SCT ratings that was consistent ( trait consistency) across three occasions of measurement over 12 months versus specific to the occasion ( occasion-specificity) with ratings by mothers, fathers, primary teachers, and secondary teachers of 811 Spanish children. Our second objective was then to determine the convergent validity for trait consistency and occasion-specificity variance components within and across settings. SCT ratings reflected mostly trait consistency for mothers, fathers, and primary teachers (less so for secondary teachers) with the convergent validity for trait consistency also being strong for mothers with fathers and for primary teachers with secondary teachers. Across home and school, however, convergent validity for trait consistency was low and even lower for occasion-specificity. SCT appears to be more trait-like rather than state-like, with similar levels of trait consistency across occasions and convergent validity within settings as attention-deficit/hyperactivity disorder (ADHD) symptoms in a prior study. However, SCT symptoms had slightly weaker convergent validity for trait consistency across settings relative to ADHD symptoms.

A latent state-trait model for analyzing states, traits, situations, method effects, and their interactions

OBJECTIVE

Studies on situation and Person × Situation interaction effects often use only one method of measurement, so that the extent to which the effects may be method-specific cannot be determined. We introduce a new multimethod latent state-trait model for random and fixed situations (MM-LST-RF), which allows examining person, situation, and Person × Situation interaction effects in the context of multimethod measurement designs (e.g., studies with multiple reporters), and discuss potential areas of application for the new model in personality research.

METHOD

The new model allows analyzing novel features of multimethod, multi-situation data, including (a) the convergent validity and method specificity of trait (person) effects, situation effects, and Person × Situation interaction effects; (b) the degree of situation specificity of method effects; and (c) potential Method × Situation interactions. An application to smoker's affect (N = 235; 57% female; 93% Caucasian) before and after quitting smoking is presented with positively and negatively worded items as methods.

RESULTS

The MM-LST-RF model fit the smoking data well. Method specificity of many effects was high.

CONCLUSIONS

The MM-LST-RF model provides researchers with a new framework for testing method specificity of person, situation, and interaction effects.

Using Generalizability Theory to Disattenuate Correlation Coefficients for Multiple Sources of Measurement Error

Over the years, research in the social sciences has been dominated by reporting of reliability coefficients that fail to account for key sources of measurement error. Use of these coefficients, in turn, to correct for measurement error can hinder scientific progress by misrepresenting true relationships among the underlying constructs being investigated. In the research reported here, we addressed these issues using generalizability theory (G-theory) in both traditional and new ways to account for the three key sources of measurement error (random-response, specific-factor, and transient) that affect scores from objectively scored measures. Results from 20 widely used measures of personality, self-concept, and socially desirable responding showed that conventional indices consistently misrepresented reliability and relationships among psychological constructs by failing to account for key sources of measurement error and correlated transient errors within occasions. The results further revealed that G-theory served as an effective framework for remedying these problems. We discuss possible extensions in future research and provide code from the computer package R in an online supplement to enable readers to apply the procedures we demonstrate to their own research.

A systematic review of the quality of reporting of simulation studies about methods for the analysis of complex longitudinal patient-reported outcomes data

PURPOSE

This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift.

METHODS

Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines.

SYNTHESIS

A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method.

CONCLUSIONS

While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored.

Are You Ready to Have Fun? The Spanish State Form of the State-Trait-Cheerfulness Inventory

Although cheerfulness, seriousness, and bad mood as traits have been widely studied as the basis of sense of humor, data are scarce regarding the same dimensions as states. In this study, we adapted the state form of the State-Trait-Cheerfulness Inventory (STCI-S) into Spanish. At the same time, we empirically tested new predictions. We assessed 5 independent samples accounting for 1,029 participants (647 women) with ages ranging from 18 to 78 years. We confirmed the 3-dimensional structure as well as a strong measurement invariance between men and women. The internal consistency of the scale was satisfactory, the expected intercorrelations emerged, and the convergence between states and traits was corroborated. We also confirmed that the STCI-S's items were sensitive to affective changes in the environment. A longitudinal stability study of the state-trait dimensions using latent state-trait (LST) models revealed that all three trait measures capture mostly stable interindividual differences, with occasion-specific effects mainly in the state dimensions. Finally, we found that the STCI-S dimensions were related to state well-being. The results suggest that the STCI-S is a valid option for measuring the state basis of sense of humor in the Spanish population.

Latent State-Trait Models for Longitudinal Family Data

Abstract. Support is key to healthy family functioning. Using the family social relations model (SRM), it has already been shown that variability in perceived support is mostly attributed to individual perceiver effects. Little is known, however, as to whether those effects are stable or occasion-specific. Several methods have been proposed within the structural equation modeling (SEM) framework for the investigation of hypotheses on stable and occasion-specific aspects of such psychological attributes. In this paper, we explore the applicability of different models for determining the consistency of SRM effects of perceived support: the multistate model, the singletrait-multistate model, and the trait-state occasion model. We provide a detailed description of the model building process and assumption verification, as well as the supporting R-code. In addition to the methodological contribution on how to combine these models with the SRM, we also provide substantive insights into the consistency of perceived family support. We rely on round robin data on relational support from the Dutch RADAR-Y (Research on Adolescent Development and Relationships – Younger Cohort) study, a 6-year longitudinal study of 500 families with a 13-year-old target adolescent at the start of the study.

A Multimethod Latent State-Trait Model for Structurally Different And Interchangeable Methods

A new multiple indicator multilevel latent state-trait (LST) model for the analysis of multitrait-multimethod-multioccasion (MTMM-MO) data is proposed. The LST-COM model combines current CFA-MTMM modeling approaches of interchangeable and structurally different methods and LST modeling approaches. The model enables researchers to specify construct and method factors on the level of time-stable (trait) as well as time-variable (occasion-specific) latent variables and analyze the convergent and discriminant validity among different rater groups across time. The statistical performance of the model is scrutinized by a simulation study and guidelines for empirical applications are provided.

Using a Few Snapshots to Distinguish Mountains from Waves: Weak Factorial Invariance in the Context of Trait-State Research

In this article, we show that the underlying dimensions obtained when factor analyzing cross-sectional data actually form a mix of within-person state dimensions and between-person trait dimensions. We propose a factor analytical model that distinguishes between four independent sources of variance: common trait, unique trait, common state, and unique state. We show that by testing whether there is weak factorial invariance across the trait and state factor structures, we can tackle the fundamental question first raised by Cattell; that is, are within-person state dimensions qualitatively the same as between-person trait dimensions? Furthermore, we discuss how this model is related to other trait-state factor models, and we illustrate its use with two empirical data sets. We end by discussing the implications for cross-sectional factor analysis and suggest potential future developments.

Using Multigroup-Multiphase Latent State-Trait Models to Study Treatment-Induced Changes in Intra-Individual State Variability: An Application to Smokers' Affect

Sometimes, researchers are interested in whether an intervention, experimental manipulation, or other treatment causes changes in intra-individual state variability. The authors show how multigroup-multiphase latent state-trait (MG-MP-LST) models can be used to examine treatment effects with regard to both mean differences and differences in state variability. The approach is illustrated based on a randomized controlled trial in which N = 338 smokers were randomly assigned to nicotine replacement therapy (NRT) vs. placebo prior to quitting smoking. We found that post quitting, smokers in both the NRT and placebo group had significantly reduced intra-individual affect state variability with respect to the affect items calm and content relative to the pre-quitting phase. This reduction in state variability did not differ between the NRT and placebo groups, indicating that quitting smoking may lead to a stabilization of individuals' affect states regardless of whether or not individuals receive NRT.

Trait and state variance in oppositional defiant disorder symptoms: A multi-source investigation with Spanish children

The objective was to determine and compare the trait and state components of oppositional defiant disorder (ODD) symptom reports across multiple informants. Mothers, fathers, primary teachers, and secondary teachers rated the occurrence of the ODD symptoms in 810 Spanish children (55% boys) on 2 occasions (end first and second grades). Single source latent state-trait (LST) analyses revealed that ODD symptom ratings from all 4 sources showed more trait (M = 63%) than state residual (M = 37%) variance. A multiple source LST analysis revealed substantial convergent validity of mothers' and fathers' trait variance components (M = 68%) and modest convergent validity of state residual variance components (M = 35%). In contrast, primary and secondary teachers showed low convergent validity relative to mothers for trait variance (Ms = 31%, 32%, respectively) and essentially zero convergent validity relative to mothers for state residual variance (Ms = 1%, 3%, respectively). Although ODD symptom ratings reflected slightly more trait- than state-like constructs within each of the 4 sources separately across occasions, strong convergent validity for the trait variance only occurred within settings (i.e., mothers with fathers; primary with secondary teachers) with the convergent validity of the trait and state residual variance components being low to nonexistent across settings. These results suggest that ODD symptom reports are trait-like across time for individual sources with this trait variance, however, only having convergent validity within settings. Implications for assessment of ODD are discussed. (PsycINFO Database Record

Trait and State Variance in Multi-Informant Assessments of ADHD and Academic Impairment in Spanish First-Grade Children

OBJECTIVE

We examined the stable trait and variable state components of ADHD-inattention (IN), ADHD-hyperactivity/impulsivity (HI), and academic impairment (AI) dimensions using mothers', fathers', primary and secondary teachers' ratings of children's behavior at home and school. We also examined between-informant agreement with regard to trait and state components.

METHOD

Mothers, fathers, primary and secondary teachers rated HI, IN, and AI in N = 758 Spanish first grade children (55% boys) over three measurement occasions across 12 months.

RESULTS

Latent state-trait analyses revealed that mothers', fathers', and primary teachers' (but not secondary teachers') ratings reflected more trait variance for ADHD-HI (M = 73%), ADHD-IN (M = 74%), and AI (M = 76%) than occasion-specific variance (M = 27%, M = 26%, and M = 24%, respectively). Fathers' ratings shared a meaningful level of trait variance with mothers' ratings of ADHD-HI and ADHD-IN (range 78% to 82%), whereas primary and secondary teachers' ratings shared lower levels of trait variance with mothers' ratings (range 41% to 63%). The trait components of fathers', primary teachers', and secondary teachers' ratings of AI showed high levels of convergence with mothers' ratings (88%, 70%, and 59% respectively).

CONCLUSIONS

ADHD symptom reports reflect both trait (48 to 86%) and state (14 to 53%) variance components. The lower amount of shared variability between home and school suggests the setting-specificity of trait and state components of ADHD symptoms. Our findings indicate that ADHD symptom reports may reflect context-specific traits, suggesting the importance of differentiating and targeting ADHD behaviors across different settings.

Collapsing factors in multitrait-multimethod models: examining consequences of a mismatch between measurement design and model

Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM) investigations. Many applications of CFA-MTMM and similarly structured models result in solutions in which at least one method (or specific) factor shows non-significant loading or variance estimates. Eid et al. (2008) distinguished between MTMM measurement designs with interchangeable (randomly selected) vs. structurally different (fixed) methods and showed that each type of measurement design implies specific CFA-MTMM measurement models. In the current study, we hypothesized that some of the problems that are commonly seen in applications of CFA-MTMM models may be due to a mismatch between the underlying measurement design and fitted models. Using simulations, we found that models with M method factors (where M is the total number of methods) and unconstrained loadings led to a higher proportion of solutions in which at least one method factor became empirically unstable when these models were fit to data generated from structurally different methods. The simulations also revealed that commonly used model goodness-of-fit criteria frequently failed to identify incorrectly specified CFA-MTMM models. We discuss implications of these findings for other complex CFA models in which similar issues occur, including nested (bifactor) and latent state-trait models.

The Relationship of Mathematical Competence and Mathematics Anxiety

In educational contexts, it is assumed that mathematical competence can be viewed as a trait. However, studies have yet to examine whether mathematical competence is actually a stable personality characteristic or rather depends on situational factors. Thus, construct validity has not yet been confirmed in this respect. The present study closes this research gap with regard to prospective pre-school teachers when measured across measurement occasions with similar situational characteristics. This study also examines the idea that math anxiety is a relevant negative predictor of mathematical competence. Both research objectives were examined using latent state-trait theory (LST) modeling, which allows for the investigation of occasion-independent and occasion-specific variability over time. The competence and anxiety of n = 354 prospective pre-school teachers were assessed twice across a period of three weeks. Results indicated no occasion-specific effects and moderate negative relations between math anxiety and all mathematical domains. The utility of LST modeling for construct validation and the investigation of complex relationships are discussed.

A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods

One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed.

Evaluating the Construct Validity of Objective Personality Tests Using a Multitrait-Multimethod-Multioccasion-(MTMM-MO)-Approach

Although Objective Personality Tests (OPTs) have a long history in psychology and the field of psychological assessment, their validity, and reliability have not yet been sufficiently studied. In this study, we examined the convergent and discriminant validity of objective (personality) tests, Implicit Association Tests (IATs), and self-report measures for the assessment of conscientiousness and intelligence. Moreover, the convergent and discriminant validity of these measures was assessed on the trait (stable) and occasion specific (momentary) level by using the multimethod latent state-trait (MM-LST) model proposed by Courvoisier, Nussbeck, Eid, and Cole (2008) which allows for the decomposition of different sources of variance. Data from 367 students assessed on three different measurement occasions was incorporated. Results indicate generally low convergence of OPTs with data gained by other approaches. Additional analyses revealed that the OPTs used assess stable rather than momentary components of the constructs. Reliabilities of different tests ranged from .54 to .95. Furthermore, a substantial amount of trait method specificity revealed that different methods assess trait components that are not shared between OPTs and other measures. Data on the criterion validity of the objective conscientiousness test revealed that it is related to the punctuality of test takers in the laboratory.