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Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurements of individuals' latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychometric properties, where the number of measured constructs (i.e., factors) is assessed, and, afterward, rotational freedom is resolved to interpret these factors. This study assessed the effects of an acquiescence response style (ARS) on EFA for unidimensional and multidimensional (un)balanced scales. Specifically, we evaluated (a) whether ARS is captured as an additional factor, (b) the effect of different rotation approaches on the content and ARS factors recovery, and (c) the effect of extracting the additional ARS factor on the recovery of factor loadings. ARS was often captured as an additional factor in balanced scales when it was strong. For these scales, ignoring extracting this additional ARS factor, or rotating to simple structure when extracting it, harmed the recovery of the original MM by introducing bias in loadings and cross-loadings. These issues were avoided by using informed rotation approaches (i.e., target rotation), where (part of) the rotation target is specified according to a priori expectations on the MM. Not extracting the additional ARS factor did not affect the loading recovery in unbalanced scales. Researchers should consider the potential presence of ARS when assessing the psychometric properties of balanced scales and use informed rotation approaches when suspecting that an additional factor is an ARS factor.

Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive modeling. However, this approach neglects the uncertainty in the factor scores, leading to biased parameter estimates and threatening the validity of conclusions about the dynamic process. We propose Three-Step Latent Vector Autoregression that adheres to this stepwise procedure while correcting for the factor scores' uncertainty. Stepwise approaches offer various advantages, for example the ability to visualize and inspect the factor scores. A simulation study demonstrates that the method performs well in obtaining correct parameter estimates of a dynamic process. We also provide an empirical example and scripts for implementation in the open-source software R using the lavaan package. [ABSTRACT FROM AUTHOR]

Introduction: Identifying subgroups of Temporary (alcohol) Abstinence Challenge (TAC) participants may offer opportunities to enhance intervention effectiveness. However, knowledge about such subgroups is missing. This study aimed to (i) describe a TAC population; (ii) identify subgroups of participants based on determinants of changes in drinking behaviour; and (iii) characterise subgroups in terms of sociodemographic and other characteristics. Methods: Data from 3803 Dutch TAC participants were analysed to identify subgroups using three‐step Latent Class Analysis. Classes were based on determinants of changes in drinking behaviour (i.e., drinking refusal self‐efficacy, craving and behavioural automaticity) and were characterised by sociodemographic characteristics, drinking behaviour, previous participation in TACs, self‐reported health and life satisfaction. Results: The majority of TAC participants were female, highly educated, employed, 53 years old on average, participated in previous TACs and reported relatively high alcohol use. Four classes of participants were identified: (i) 'ordinary drinkers' (49.0%); (ii) 'drinkers in control' (21.4%); (iii) 'habitual drinkers with perceived control to refuse' (18.4%); and (iv) 'drinkers not in control' (11.2%). Class 2 drank least often and non‐excessive volumes, while other classes typically drank 4 or more days per week and 3 to 4 glasses per drinking day, with the highest alcohol use found in class 4. Discussion and Conclusions: Different configurations of determinants in this study's four subgroups may require different intervention approaches and might inform personalised support. Future research is needed to examine the predictive value of these subgroups on post‐challenge drinking behaviour to assess support needs and participation value. [ABSTRACT FROM AUTHOR]

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After formally introducing the model and showing how it can be implemented, we carry out a simulation study and a real-data study in order to assess its performance and compare it with the commonly used listwise deletion and an available R-routine. Results indicate that the BMLC model is able to recover unbiased parameter estimates of the analysis models considered in our studies, as well as to correctly reflect the uncertainty due to missing data, outperforming the competing methods.

Objectives: The therapist‐facilitative interpersonal skills (FIS) has shown to predict therapy outcomes, demonstrating that high FIS therapists are more effective than low FIS therapists. There is a need for more insight into the variability in strengths and weaknesses in therapist skills. This study investigates whether a revised and extended FIS‐scoring leads to more differentiation in measuring therapists' interpersonal skills. Furthermore, we explorative examine whether subgroups of therapists can be distinguished in terms of differences in their interpersonal responses. Method: Using secondary data analysis, 93 therapists were exposed to seven FIS‐clips. Responses of therapists using the original and the extended FIS scoring were rated. Results: Three factors were found on the extended FIS scoring distinguishing supportive, expressive, and persuasive interpersonal responses of therapists. A latent profile analysis enlightened the presence of six subgroups of therapists. Conclusion: Using the revised and extended FIS‐scoring contributes to our understanding of the role of interpersonal skills in the therapeutic setting by unraveling the question what works for whom. [ABSTRACT FROM AUTHOR]

Transgressive incidents directed at staff by forensic patients occur frequently, leading to detrimental psychological and physical harm, underscoring urgency of preventive measures. These incidents, emerging within therapeutic relationships, involve complex interactions between patient and staff behavior. This study aims to identify clusters of transgressive incidents based on incident characteristics such as impact, severity, (presumed) cause, type of aggression, and consequences, using latent class analysis (LCA). Additionally, variations in incident clusters based on staff, patient, and context characteristics were investigated. A total of 1,184 transgressive incidents, reported by staff and targeted at staff by patients between 2018-2022, were extracted from a digital incident reporting system at Fivoor, a Dutch forensic psychiatric healthcare organisation. Latent Class Analysis revealed six incident classes: 1) verbal aggression with low impact; 2) verbal aggression with medium impact; 3) physical aggression with medium impact; 4) verbal menacing/aggression with medium impact; 5) physical aggression with high impact; and 6) verbal and physical menacing/aggression with high impact. Significant differences in age and gender of both staff and patients, staff function, and patient diagnoses were observed among these classes. Incidents with higher impact were more prevalent in high security clinics, while lower-impact incidents were more common in clinics for patients with intellectual disabilities. Despite limitations like missing information, tailored prevention approaches are needed due to varying types of transgressive incidents across patients, staff, and units. [ABSTRACT FROM AUTHOR]

Measuring values in sociological research sometimes involves the use of ranking data. A disadvantage of a ranking assignment is that the order in which the items are presented might influence the choice preferences of respondents regardless of the content being measured. The standard procedure to rule out such effects is to randomize the order of items across respondents. However, implementing this design may be impractical and the biasing impact of a response order effect cannot be evaluated. We use a latent choice factor (LCF) model that allows statistically controlling for response order effects. Furthermore, the model adequately deals with the known issue of ipsativity of ranking data. Applying this model to a Dutch survey on work values, we show that a primacy effect accounts for response order bias in item preferences. Our findings demonstrate the usefulness of the LCF model in modeling ranking data while taking into account particular response biases.

Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding (group differences in) an acquiescence response style (ARS; an agreeing tendency regardless of item content). If non-invariance results solely from neglecting ARS, one should not worry about scale inequivalences but model the ARS instead. In a simulation study, we investigated the effect of ARS on MI testing, both when including ARS as a factor in the measurement model or not. For (semi-) balanced scales, disregarding a large ARS resulted in non-invariance already at the configural level. This was resolved by including an ARS factor for all groups. For unbalanced scales, disregarding ARS did not affect MI testing, and including an ARS factor often resulted in non-convergence. Implications and recommendations for applied research are discussed. [ABSTRACT FROM AUTHOR]

Mplus and LatentGOLD implement the Vuong-Lo-Mendell-Rubin test (comparing models with K and K + 1 latent classes) in slightly differ manners. While LatentGOLD uses the formulae from Vuong (1989; https://doi.org/10.2307/1912557), Mplus replaces the standard parameter variance-covariance matrix by its robust version. Our small simulation study showed why such a seemingly small difference may sometimes yield rather different results. The main finding is that the Mplus approximation of the distribution of the likelihood-ratio statistic is much more data dependent than the LatentGOLD one. This data dependency is stronger when the true model serves as the null hypothesis (H0) with K classes than when it serves as the alternative hypothesis (H1) with K + 1 classes, and it is also stronger for low class separation than for high class separation. Another important finding is that neither of the two implementations yield uniformly distributed p-values under the correct null hypothesis, indicating this test is not the best model selection tool in mixture modeling. [ABSTRACT FROM AUTHOR]

In large-scale testing, the use of multigroup approaches is limited for assessing differential item functioning (DIF) across multiple variables as DIF is examined for each variable separately. In contrast, the item response theory with covariate (IRT-C) procedure can be used to examine DIF across multiple variables (covariates) simultaneously. To assess the utility of the IRT-C procedure, we conducted a simulation study. Using SAT data for realistic parameters, uniform DIF on three covariates were simulated: gender (dichotomous), race/ethnicity (categorical), and income (continuous). Simulations were conducted across several conditions: two test lengths (14 items, 21 items), four sample sizes (5,000, 10,000, 20,000, 40,000), and two DIF effect sizes (medium, large). It was found that the IRT-C procedure could accurately recover the latent means and the three-parameter logistic model parameters well with a substantial sample size of 20,000. There was good control of Type I error rates to the nominal rates across the sample sizes. Good power to detect DIF across all covariates (>0.80) was observed when the sample size was 20,000 for large DIF effect size and 40,000 for medium DIF effect size. Practical implications for the use of the IRT-C procedure are discussed.

In educational measurement, responses of students on items are used not only to measure the ability of students, but also to evaluate and compare the performance of schools. Analysis should ideally account for the multilevel structure of the data, and school-level processes not related to ability, such as working climate and administration conditions, need to be separated from student and school ability. However, in educational studies such as Programme for International Student Assessment, Trends in International Mathematics and Science Study, and COOL[superscript 5-18], this is hardly ever done. This study presents a model that simultaneously accounts for the nested structure, controls student ability for processes at school level, classifies schools to monitor and compare schools, and tests for school-level item bias.