Determining differences between therapists using an extended version of the facilitative interpersonal skills performance test.

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.

A general Monte Carlo method for sample size analysis in the context of network models.

We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: (1) a hypothesized network structure or desired characteristics of that structure, (2) an estimation performance measure and its corresponding target value (e.g., a sensitivity of 0.6), and (3) a statistic and its corresponding target value that determines how the target value for the performance measure be reached (e.g., reaching a sensitivity of 0.6 with a probability of 0.8). The method consists of a Monte Carlo simulation step for computing the performance measure and the statistic for several sample sizes selected from an initial candidate sample size range, a curve-fitting step for interpolating the statistic across the entire candidate range, and a stratified bootstrapping step to quantify the uncertainty around the recommendation provided. We evaluated the performance of the method for the Gaussian Graphical Model, but it can easily extend to other models. The method displayed good performance, providing sample size recommendations that were, on average, within three observations of a benchmark sample size, with the highest standard deviation of 25.87 observations. The method discussed is implemented in the form of an R package called powerly, available on GitHub and CRAN. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Awareness Is Bliss: How Acquiescence Affects Exploratory Factor Analysis.

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.

Pathways to antisocial behavior: a framework to improve diagnostics and tailor therapeutic interventions.

The Antisocial Personality Disorder (ASPD), and antisocial behavior (ASB) in general, is associated with significant impact on individuals themselves, their environment, and society. Although various interventions show promising results, no evidence-based treatments are available for individuals with ASPD. Therefore, making informed choices about which treatment can be applied to an individual patient is complicated. Furthermore, contradictory findings on therapy effectiveness and underlying factors of ASB, such as cognitive impairments and personality traits, fuel the debate whether the conceptualization of ASPD in the DSM-5 is accurate and whether this population can be seen as homogeneous. A conceptual framework, based on the reciprocal altruism theory, is presented in which we propose different pathways to ASB. These pathways suggest underlying dynamics of ASB and provide an explanation for previous contradictory research outcomes. This framework is intended to serve as a clinically relevant model that provides directions for improving diagnostics and matching treatments to underlying dynamics in the antisocial population.

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach.

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

The impact of ordinal scales on Gaussian mixture recovery.

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation-maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable.

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD.

Are Retrospective Assessments Means of People's Experiences?: Accounting for Interpersonal and Intrapersonal Variability when Comparing Retrospective Assessment Data to Ecological Momentary Assessment Data.

Retrospective Assessment (RA) scores are often found to be higher than the mean of Ecological Momentary Assessment (EMA) scores about a concurrent period. This difference is generally interpreted as bias towards salient experiences in RA. During RA participants are often asked to summarize their experiences in unspecific terms, leaving room for personal interpretation. As a result, participants may use various strategies to summarize their experiences. In this study, we reanalyzed an existing dataset (N = 92) using a repeated N = 1 approach. We assessed for each participant whether it was likely that their RA score was an approximation of the mean of their experiences as captured by their EMA scores. We found considerable interpersonal differences in the difference between EMA scores and RA scores, as well as some extreme cases. Furthermore, for a considerable part of the sample (n = 46 for positive affect, n = 56 for negative affect), we did not reject the null hypothesis that their RA score represented the mean of their experiences as captured by their EMA scores. We conclude that in its current unspecific form RA may facilitate bias, although not for everyone. Future studies may determine whether differences between RA and EMA are mitigated using more specific forms of RA, while acknowledging interindividual differences.

Scale length does matter: Recommendations for measurement invariance testing with categorical factor analysis and item response theory approaches.

In social sciences, the study of group differences concerning latent constructs is ubiquitous. These constructs are generally measured by means of scales composed of ordinal items. In order to compare these constructs across groups, one crucial requirement is that they are measured equivalently or, in technical jargon, that measurement invariance (MI) holds across the groups. This study compared the performance of scale- and item-level approaches based on multiple group categorical confirmatory factor analysis (MG-CCFA) and multiple group item response theory (MG-IRT) in testing MI with ordinal data. In general, the results of the simulation studies showed that MG-CCFA-based approaches outperformed MG-IRT-based approaches when testing MI at the scale level, whereas, at the item level, the best performing approach depends on the tested parameter (i.e., loadings or thresholds). That is, when testing loadings equivalence, the likelihood ratio test provided the best trade-off between true-positive rate and false-positive rate, whereas, when testing thresholds equivalence, the χ2 test outperformed the other testing strategies. In addition, the performance of MG-CCFA's fit measures, such as RMSEA and CFI, seemed to depend largely on the length of the scale, especially when MI was tested at the item level. General caution is recommended when using these measures, especially when MI is tested for each item individually.

Mixture multigroup factor analysis for unraveling factor loading noninvariance across many groups.

Psychological research often builds on between-group comparisons of (measurements of) latent variables; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same latent variable(s) are measured in exactly the same way across all groups (i.e., measurement invariance). Otherwise, one would be comparing apples and oranges. Nowadays, measurement invariance is often tested across a large number of groups by means of multigroup factor analysis. When the assumption is untenable, one may compare group-specific measurement models to pinpoint sources of noninvariance, but the number of pairwise comparisons exponentially increases with the number of groups. This makes it hard to unravel invariances from noninvariances and for which groups they apply, and it elevates the chances of falsely detecting noninvariance. An intuitive solution is clustering the groups into a few clusters based on the measurement model parameters. Therefore, we present mixture multigroup factor analysis (MMG-FA) which clusters the groups according to a specific level of measurement invariance. Specifically, in this article, clusters of groups with metric invariance (i.e., equal factor loadings) are obtained by making the loadings cluster-specific, whereas other parameters (i.e., intercepts, factor (co)variances, residual variances) are still allowed to differ between groups within a cluster. MMG-FA was found to perform well in an extensive simulation study, but a larger sample size within groups is required for recovering more subtle loading differences. Its empirical value is illustrated for data on the social value of emotions and data on emotional acculturation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on Self-Report EMA Data.

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.

Heterogeneity in Quality of Life of Long-Term Colon Cancer Survivors: A Latent Class Analysis of the Population-Based PROFILES Registry.

BACKGROUND: Long-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) of survivors with similar HRQOL patterns and investigated their stability over time and the association of clinical covariates with these classes. MATERIALS AND METHODS: Data from the population-based PROFILES registry were used. Included were survivors with nonmetastatic (TNM stage I-III) colon cancer (n = 1,489). HRQOL was assessed with the Dutch translation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 version 3.0. Based on survivors' HRQOL, latent class analysis (LCA) was used to identify unobserved classes of survivors. Moreover, latent transition analysis (LTA) was used to investigate changes in class membership over time. Furthermore, the effect of covariates on class membership was assessed using multinomial logistic regression. RESULTS: LCA identified five classes at baseline: class 1, excellent HRQOL (n = 555, 37.3%); class 2, good HRQOL with prevalence of insomnia (n = 464, 31.2%); class 3, moderate HRQOL with prevalence of fatigue (n = 213, 14.3%); class 4, good HRQOL with physical limitations (n = 134, 9.0%); and class 5, poor HRQOL (n = 123, 8.3%). All classes were stable with high self-transition probabilities. Longer time since the diagnosis, no comorbid conditions, and male sex were associated with class 1, whereas older age was associated with class 4. Clinical covariates were not associated with class membership. CONCLUSION: The identified classes are characterized by distinct patterns of HRQOL and can support patient-centered care. LCA and LTA are powerful tools for investigating HRQOL in cancer survivors. IMPLICATIONS FOR PRACTICE: Long-term colon cancer survivors show great heterogeneity in their health-related quality of life. This study identified five distinct clusters of survivors with similar patterns of health-related quality of life and showed that these clusters remain stable over time. It was also shown that these clusters do not significantly differ in tumor characteristics or received treatment. Cluster membership of long-term survivors can be identified by sociodemographic characteristics but is not predetermined by diagnosis and treatment.

Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data.

Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed "latent Markov factor analysis" (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent "states" according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present "latent Markov latent trait analysis" (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents' affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.

Identifying return to work trajectories among employees on sick leave due to mental health problems using latent class transition analysis.

OBJECTIVES: To develop effective return to work (RTW) interventions for employees on sick leave due to mental health problems (MHPs), a better understanding of individual variation in the RTW process is needed. We investigated which RTW trajectories can be identified among employees with MHPs in terms of RTW duration and relapse occurrence during the RTW process. Additionally, we examined how different RTW trajectories can be described in terms of personal and work characteristics. METHODS: Longitudinal sickness absence registry data were collected retrospectively from the largest Dutch occupational health service. Quantitative RTW information as well as personal and work characteristics were extracted. In total, 9517 employees with a sickness absence due to MHPs were included in the analyses (62 938 data points; RTW durations from 29 to 730 days). RESULTS: A latent class transition analysis revealed five distinct RTW trajectories, namely (1) fast RTW with little chance of relapse, (2) slow RTW with little chance of relapse, (3) fast RTW with considerable chance of relapse, (4) slow RTW with considerable chance of relapse and (5) very fast RTW with very small chance of relapse. Differences between employees in the slower and faster trajectories were observed regarding gender, age, type of MHP, organisation sector and organisation size but not regarding part-time work. CONCLUSIONS: RTW trajectories among employees with MHPs showed large individual variability and differed on personal and work characteristics. Knowledge on different RTW trajectories and their characteristics contributes to the development of personalised RTW treatments, tailored to specific individuals and organisations.

The family context as a foundation for romantic relationships: A person-centered multi-informant longitudinal study.

This 8-wave person-centered multi-informant study tested whether the quality of parent-adolescent relationships predicted the romantic experiences of young adults and their partners (N = 374; 54.8% girls; Mage = 13.08 years, SDage = 0.48 at the first measurement wave). Perceptions of parent-adolescent relationships were assessed using adolescent, mother, and father reports. Results show that both young adults and their partners reported the highest levels of support, intimacy, and passion when young adults had an authoritative relationship quality with their parents. A distant parent-adolescent relationship quality, however, predicted the lowest support, intimacy, and passion in romantic relationships. Interestingly, the association between parent-adolescent relationships with the experience of young adults' romantic partners was indirect. Parent-adolescent relationships predicted target young adults' romantic relationship experiences, which predicted partners' romantic relationship experiences. Parent-child relationship quality therefore has far-reaching, yet subtle, effects on later romantic relationships, affecting both young adults and their partners. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Multiple imputation of longitudinal categorical data through bayesian mixture latent Markov models.

Standard latent class modeling has recently been shown to provide a flexible tool for the multiple imputation (MI) of missing categorical covariates in cross-sectional studies. This article introduces an analogous tool for longitudinal studies: MI using Bayesian mixture Latent Markov (BMLM) models. Besides retaining the benefits of latent class models, i.e. respecting the (categorical) measurement scale of the variables and preserving possibly complex relationships between variables within a measurement occasion, the Markov dependence structure of the proposed BMLM model allows capturing lagged dependencies between adjacent time points, while the time-constant mixture structure allows capturing dependencies across all time points, as well as retrieving associations between time-varying and time-constant variables. The performance of the BMLM model for MI is evaluated by means of a simulation study and an empirical experiment, in which it is compared with complete case analysis and MICE. Results show good performance of the proposed method in retrieving the parameters of the analysis model. In contrast, competing methods could provide correct estimates only for some aspects of the data.

Insomnia disorder subtypes derived from life history and traits of affect and personality.

BACKGROUND: Insomnia disorder is the second most prevalent mental disorder, and it is a primary risk factor for depression. Inconsistent clinical and biomarker findings in patients with insomnia disorder suggest that heterogeneity exists and that subtypes of this disease remain unrecognised. Previous top-down proposed subtypes in nosologies have had insufficient validity. In this large-scale study, we aimed to reveal robust subtypes of insomnia disorder by use of data-driven analyses on a multidimensional set of biologically based traits. METHODS: In this series of studies, we recruited participants from the Netherlands Sleep Registry, a database of volunteers aged 18 years or older, who we followed up online to survey traits, sleep, life events, and health history with 34 selected questionnaires of which participants completed at least one. We identified insomnia disorder subtypes by use of latent class analyses. We evaluated the value of our identified subtypes of insomnia disorder by use of a second, non-overlapping cohort who were recruited through a newsletter that was emailed to a new sample of Netherlands Sleep Registry participants, and by assessment of within-subject stability over several years of follow-up. We extensively tested the clinical validity of these subtypes for the development of sleep complaints, comorbidities (including depression), and response to benzodiazepines; in two subtypes of insomnia disorder, we also assessed the clinical relevance of these subtypes by use of an electroencephalogram biomarker and the effectiveness of cognitive behavioural therapy. To facilitate implementation, we subsequently constructed a concise subtype questionnaire and we validated this questionnaire in the second, non-overlapping cohort. FINDINGS: 4322 Netherlands Sleep Registry participants completed at least one of the selected questionnaires, a demographic questionnaire, and an assessment of their Insomnia Severity Index (ISI) between March 2, 2010, and Oct 28, 2016. 2224 (51%) participants had probable insomnia disorder, defined as an ISI score of at least 10, and 2098 (49%) participants with a lower ISI score served as a control group. With a latent class analysis of the questionnaire responses of 2224 participants, we identified five novel insomnia disorder subtypes: highly distressed, moderately distressed but reward sensitive (ie, with intact responses to pleasurable emotions), moderately distressed and reward insensitive, slightly distressed with high reactivity (to their environment and life events), and slightly distressed with low reactivity. In a second, non-overlapping replication sample of 251 new participants who were assessed between June 12, 2017, and Nov 26, 2017, five subtypes were also identified to be optimal. In both the development sample and replication sample, each participant was classified as having only one subtype with high posterior probability (0·91-1·00). In 215 of the original sample of 2224 participants with insomnia who were reassessed 4·8 (SD 1·6) years later (between April 13, 2017, and June 21, 2017), the probability of maintaining their original subtype was 0·87, indicating a high stability of the classification. We found differences between the identified subtypes in developmental trajectories, response to treatment, the presence of an electroencephalogram biomarker, and the risk of depression that was up to five times different between groups, which indicated a clinical relevance of these subtypes. INTERPRETATION: High-dimensional data-driven subtyping of people with insomnia has addressed an unmet need to reduce the heterogeneity of insomnia disorder. Subtyping facilitates identification of the underlying causes of insomnia, development of personalised treatments, and selection of patients with the highest risk of depression for inclusion in trials regarding prevention of depression. FUNDING: European Research Council and Netherlands Organization for Scientific Research.

Perceived Relationship Development in Anxious and Non-Anxious Adolescents: a Person-Centered Five-Wave Longitudinal Study.

Developmental changes in adolescents' relationships with parents and friends intertwine, but individual differences in these relationships are likely to emerge as not all adolescents develop similarly. Generalized anxiety symptoms may underlie these individual differences, as these symptoms have frequently been associated with interpersonal difficulties. This study examines relationship quality development with parents and friends in adolescents with low and high levels of generalized anxiety symptoms. A latent transition analysis was performed in a two-cohort five-wave study design covering ages 12 to 16 (n = 923, 50.8% males) and 16 to 20 (n = 390, 43.4% males). About one-third of adolescents with high levels of generalized anxiety symptoms perceived a turbulent relationship with both their parents and best friends, whereas only one-tenth of those with low levels of generalized anxiety symptoms did. Low levels as opposed to high levels of generalized anxiety symptoms predicted a twice as high likelihood to perceive harmonious relationships with both their parents and best friends. Nevertheless, adolescents with low and high levels of generalized anxiety symptoms exhibited similar trends in relationship development. Overall, our findings indicate that generalized anxiety symptoms are not deterministic markers for relationship difficulties as there were plenty of adolescents with high levels of generalized anxiety symptoms that experienced no relationship difficulties across adolescence.

Deciding on the Starting Number of Classes of a Latent Class Tree.

In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by sequentially splitting classes into two subclasses. The resulting tree structure gives a clear insight into how the classes are formed and how solutions with different numbers of classes are substantively linked to one another. A limitation of the current LCT modeling approach is that it allows only for binary splits, which in certain situations may be too restrictive. Especially at the root node of the tree, where an initial set of classes is created based on the most dominant associations present in the data, it may make sense to use a model with more than two classes. In this article, we propose a modification of the LCT approach that allows for a nonbinary split at the root node, and we provide methods to determine the appropriate number of classes in this first split, based either on theoretical grounds or on a relative improvement of fit measure. This novel approach also can be seen as a hybrid of a standard LC model and a binary LCT model, in which an initial, oversimplified but interpretable model is refined using an LCT approach. Furthermore, we show how to apply an LCT model when a nonstandard LC model is required. These new approaches are illustrated using two empirical applications: one on social capital and the other on (post)materialism.

Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data.

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.