A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data.

Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.

The evolution of patients' concept of the alliance and its relation to outcome: A dynamic latent-class structural equation modeling approach.

The working alliance (WA) has been widely identified as the key concept for psychotherapy and allied health care services. The WA, measured at different phases of diverse kinds of therapies, has been shown to robustly predict posttreatment outcomes. But the way the clients' conceptualization of the alliance evolves overtime, and the relation between this kind of conceptual change and subsequent symptom improvements, has not been investigated. Dynamic Latent Class Structural Equation Models (DLC-SEM) were applied to data drawn from two randomized clinical trials of cognitive-behavioral therapy for generalized anxiety disorder (N = 57 and 80) to evaluate several potential models of the relation between the conceptual/structural changes in patients' self-reports of the quality of the alliance and subsequent treatment outcomes. Inspection of the DLC-SEM models suggests that, overtime, between 63% and 66% of the better session-level outcome clients switched from three factors (task, goal, bond) to an integrated single factor conceptualization of the therapeutic alliance. The study indicates that the majority of patients evolve their concept of the alliance overtime: The previously distinct alliance elements become integrated into a single factor construct. These findings suggest that if such overtime development is generalizable across diverse patient/treatment populations, future research ought to take these developments into account both methodologically (i.e., how alliance is measured) and in analyzing time-series data (e.g., using DLC-SEM). By modeling the patient's dynamic concept evolution, this initial study shows a potential to empirically explore prior theoretical propositions of the evolutions (or stability) of the alliance overtime. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Correction to: Psychosocial distress in acute cancer patients assessed with an expert rating scale.

The article "Psychosocial distress in acute cancer patients assessed with an expert rating scale".

A More Efficient Causal Mediator Model Without the No-Unmeasured-Confounder Assumption.

Mediator models have been developed primarily under the assumption of no-unmeasured-confounding. In many situations, this assumption is violated and may lead to the identification of mediator variables that actually are statistical artifacts. The rank preserving model (RPM) is an alternative approach to estimate controlled direct and mediator effects. It is based on the structural mean models framework and a no-effect-modifier assumption. The RPM assumes that unobserved confounders do not interact with treatment or mediators. This assumption is often more plausible to hold than the no-unmeasured-confounder assumption. So far, models using the no-effect-modifier assumption have been rarely used, which might be due to its low power and inefficiency in many scenarios. Here, a semi-parametric nonlinear extension, the nRPM, is proposed that overcomes this inefficiency using thin plate regression splines that both increase the predictive power of the model and decrease the misspecification present in many situations. In a simulation study, it is shown that the nRPM provides estimates that are robust against the violation of the no-effect-modifier assumption and that are substantively more efficient than those of the RPM. The model is illustrated using a data set on CD4 cell counts in a context of the human immunodeficiency virus (HIV).

Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches.

In measurement invariance testing, when a certain level of full invariance is not achieved, the sequential backward specification search method with the largest modification index (SBSS_LMFI) is often used to identify the source of non-invariance. SBSS_LMFI has been studied under complete data but not missing data. Focusing on Likert-type scale variables, this study examined two methods for dealing with missing data in SBSS_LMFI using Monte Carlo simulation: robust full information maximum likelihood estimator (rFIML) and mean and variance adjusted weighted least squared estimator coupled with pairwise deletion (WLSMV_PD). The result suggests that WLSMV_PD could result in not only over-rejections of invariance models but also reductions of power to identify non-invariant items. In contrast, rFIML provided good control of type I error rates, although it required a larger sample size to yield sufficient power to identify non-invariant items. Recommendations based on the result were provided.

A Heterogeneous Growth Curve Model for Nonnormal Data.

The heterogeneous growth curve model (HGM; Klein & Muthén, 2006 ) is a method for modeling heterogeneity of growth rates with a heteroscedastic residual structure for the slope factor. It has been developed as an extension of a conventional growth curve model and a complementary tool to growth curve mixture models. In this article, a robust version of the heterogeneous growth curve model (HGM-R) is presented that extends the original HGM with a mixture model to allow for an unbiased parameter estimation under the condition of nonnormal data. In two simulation studies, the performance of the method is examined under the condition of nonnormality and a misspecified heteroscedastic residual structure. The results of the simulation studies suggest an unbiased estimation of the heterogeneity by the HGM-R when sample size was large enough and a good approximation of the heteroscedastic residual structure even when the functional form of the heteroscedasticity was misspecified. The practical application of the approach is demonstrated for a data set from HIV-infected patients.

Causal definitions versus casual estimation: Reply to Valente et al. (2022).

In this response to Valente et al. (2022), I am discussing the plausibility and applicability of the proposed mediation model and its causal effects estimation for single case experimental designs (SCEDs). I will focus on the underlying assumptions that the authors use to identify the causal effects. These assumptions include the particularly problematic assumption of sequential ignorability or no-unmeasured confounders. First, I will discuss the plausibility of the assumption in general and then particularly for SCEDs by providing an analytic argument and a reanalysis of the empirical example in Valente et al. (2022). Second, I will provide a simulation that reproduces the design by Valente et al. (2022) with the exception that, for a more realistic depiction of empirical data, an unmeasured confounder affects the mediator and outcome variables. The results of this simulation study indicate that even minor violations will lead to Type I error rates up to 100% and coverage rates as low as 0% for the defined causal direct and indirect effects. Third, using historical data on the effect of birth control on stork population and birth rates, I will show that mediation models like the proposed method can lead to surprising artifacts. These artifacts can hardly be identified with statistically means including methods such as sensitivity analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis.

Measurement invariance (MI) is one of the main psychometric requirements for analyses that focus on potentially heterogeneous populations. MI allows researchers to compare latent factor scores across persons from different subgroups, whereas if a measure is not invariant across all items and persons then such comparisons may be misleading. If full MI does not hold further testing may identify problematic items showing differential item functioning (DIF). Most methods developed to test DIF focused on simple scenarios often with comparisons across two groups. In practical applications, this is an oversimplification if many grouping variables (e.g., gender, race) or continuous covariates (e.g., age) exist that might influence the measurement properties of items; these variables are often correlated, making traditional tests that consider each variable separately less useful. Here, we propose the application of Bayesian Moderated Nonlinear Factor Analysis to overcome limitations of traditional approaches to detect DIF. We investigate how modern Bayesian shrinkage priors can be used to identify DIF items in situations with many groups and continuous covariates. We compare the performance of lasso-type, spike-and-slab, and global-local shrinkage priors (e.g., horseshoe) to standard normal and small variance priors. Results indicate that spike-and-slab and lasso priors outperform the other priors. Horseshoe priors provide slightly lower power compared to lasso and spike-and-slab priors. Small variance priors result in very low power to detect DIF with sample sizes below 800, and normal priors may produce severely inflated type I error rates. We illustrate the approach with data from the PISA 2018 study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys.

Behavioral scientists have become increasingly reliant on online survey platforms such as Amazon's Mechanical Turk (Mturk). These platforms have many advantages, for example it provides ease of access to difficult to sample populations, a large pool of participants, and an easy to use implementation. A major drawback is the existence of bots that are used to complete online surveys for financial gain. These bots contaminate data and need to be identified in order to draw valid conclusions from data obtained with these platforms. In this article, we will provide a Bayesian latent class joint modeling approach that can be routinely applied to identify bots and simultaneously estimate a model of interest. This method can be used to separate the bots' response patterns from real human responses that were provided in line with the item content. The model has the advantage that it is very flexible and is based on plausible assumptions that are met in most empirical settings. We will provide a simulation study that investigates the performance of the model under several relevant scenarios including sample size, proportion of bots, and model complexity. We will show that ignoring bots will lead to severe parameter bias whereas the Bayesian latent class model results in unbiased estimates and thus controls this source of bias. We will illustrate the model and its capabilities with data from an empirical political ideation survey with known bots. We will discuss the implications of the findings with regard to future data collection via online platforms.

Forecasting Intra-individual Changes of Affective States Taking into Account Inter-individual Differences Using Intensive Longitudinal Data from a University Student Dropout Study in Math.

The longitudinal process that leads to university student dropout in STEM subjects can be described by referring to (a) inter-individual differences (e.g., cognitive abilities) as well as (b) intra-individual changes (e.g., affective states), (c) (unobserved) heterogeneity of trajectories, and d) time-dependent variables. Large dynamic latent variable model frameworks for intensive longitudinal data (ILD) have been proposed which are (partially) capable of simultaneously separating the complex data structures (e.g., DLCA; Asparouhov et al. in Struct Equ Model 24:257-269, 2017; DSEM; Asparouhov et al. in Struct Equ Model 25:359-388, 2018; NDLC-SEM, Kelava and Brandt in Struct Equ Model 26:509-528, 2019). From a methodological perspective, forecasting in dynamic frameworks allowing for real-time inferences on latent or observed variables based on ongoing data collection has not been an extensive research topic. From a practical perspective, there has been no empirical study on student dropout in math that integrates ILD, dynamic frameworks, and forecasting of critical states of the individuals allowing for real-time interventions. In this paper, we show how Bayesian forecasting of multivariate intra-individual variables and time-dependent class membership of individuals (affective states) can be performed in these dynamic frameworks using a Forward Filtering Backward Sampling method. To illustrate our approach, we use an empirical example where we apply the proposed forecasting method to ILD from a large university student dropout study in math with multivariate observations collected over 50 measurement occasions from multiple students ([Formula: see text]). More specifically, we forecast emotions and behavior related to dropout. This allows us to predict emerging critical dynamic states (e.g., critical stress levels or pre-decisional states) 8 weeks before the actual dropout occurs.

Psychosocial distress in acute cancer patients assessed with an expert rating scale.

PURPOSE: The identification of psychosocial stress in cancer patients has remained a challenging task especially in an acute care environment. The aims of the present study were to apply a short expert rating scale for the assessment of distress during the acute treatment phase and to identify potential sociodemographic and disease-related predictors. METHODS: Four hundred seventy-eight ward cancer patients were assessed with the short form of the psycho-oncological basis documentation and its breast-cancer-specific version. In addition, they completed a self-rating questionnaire on stress in cancer patients. We recorded sociodemographic and disease-related variables and assessed their predictive value for psychosocial distress. RESULTS: According to the expert rating scale, 56.3% of patients were rated distressed. While only 31.3% of patients were classified as distressed according to a patient self-rating, both approaches showed a good degree of concurrence with a consistent classification of 69% of patients. Younger age, current psychotropic medication, and past psychological treatment were associated with higher distress levels. Patients with metastases and those with a poorer functional status were more distressed. Interestingly, having an operation was associated with a better psychological well-being. CONCLUSIONS: This study demonstrated that a substantial proportion of cancer patients in acute care are psychosocially distressed. A short expert rating scale proved to be a feasible tool for the assessment of distress in an acute care setting.

Comparing estimators for latent interaction models under structural and distributional misspecifications.

Estimation methods for structural equation models with interactions of latent variables were compared in several studies. Yet none of these studies examined models that were structurally misspecified. Here, the model-implied instrumental variable 2-stage least square estimator (MIIV-2SLS; Bollen, 1995; Bollen & Paxton, 1998), the 2-stage method of moments estimator (2SMM; Wall & Amemiya, 2003), the nonlinear structural equation mixture model approach (NSEMM; Kelava, Nagengast, & Brandt, 2014), and the unconstrained product indicator approach (UPI; Marsh, Wen, & Hau, 2004) were compared in a Monte Carlo simulation. The design included structural misspecifications in the measurement model involving the scaling indicator or not, the size of the misspecification, normal and nonnormal data, the indicators' reliability, and sample size. For the structural misspecifications that did not involve the scaling indicator, we found that MIIV-2SLS' parameter estimates were less biased compared with 2SMM, NSEMM, and UPI. If the reliability was high, the RMSE for all approaches was very similar; for low reliability, MIIV-2SLS' RMSE became larger compared with the other approaches. If the structural misspecification involved the scaling indicator, all estimators were seriously biased, with the largest bias for MIIV-2SLS. In most scenarios, this bias was more severe for the linear effects than for the interaction effect. The RMSE for conditions with misspecified scaling indicators was smallest for 2SMM, especially for low reliability scenarios, but the overall magnitude of bias was such that we cannot recommend any of the estimators in this situation. Our article showed the damage done when researchers omit cross-loadings of the scaling indicator and the importance of giving more attention to these indicators particularly if the indicators' reliability is low. It also showed that no one estimator is superior to the others across all conditions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Standardization of Linear and Nonlinear Effects in Direct and Indirect Applications of Structural Equation Mixture Models for Normal and Nonnormal Data.

The application of mixture models to flexibly estimate linear and nonlinear effects in the SEM framework has received increasing attention (e.g., Jedidi et al., 1997b; Bauer, 2005; Muthén and Asparouhov, 2009; Wall et al., 2012; Kelava and Brandt, 2014; Muthén and Asparouhov, 2014). The advantage of mixture models is that unobserved subgroups with class-specific relationships can be extracted (direct application), or that the mixtures can be used as a statistical tool to approximate nonnormal (latent) distributions (indirect application). Here, we provide a general standardization procedure for linear and nonlinear interaction and quadratic effects in mixture models. The procedure can also be applied to multiple group models or to single class models with nonlinear effects like LMS (Klein and Moosbrugger, 2000). We show that it is necessary to take nonnormality of the data into account for a correct standardization. We present an empirical example from education science applying the proposed procedure.

A general non-linear multilevel structural equation mixture model.

In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, 2000), and multilevel modeling (Rabe-Hesketh et al., 2004). We present a general non-linear multilevel structural equation mixture model (GNM-SEMM) that combines recent semiparametric non-linear structural equation models (Kelava and Nagengast, 2012; Kelava et al., 2014) with multilevel structural equation mixture models (Muthén and Asparouhov, 2009) for clustered and non-normally distributed data. The proposed approach allows for semiparametric relationships at the within and at the between levels. We present examples from the educational science to illustrate different submodels from the general framework.

Cell death biomarkers as early predictors for hepatic dysfunction in patients after orthotopic liver transplantation.

BACKGROUND: Valid prognostic factors for early identification of a complicated course after orthotopic liver transplantation from deceased donors are rare. The aim of this study was to investigate the prognostic value of different cell death biomarkers and inflammatory markers in patients after orthotopic liver transplantation from deceased donors. METHODS: In total, 100 patients were evaluated for short-term complications within 10 days after orthotopic liver transplantation from deceased donors. Blood samples were collected before surgery, immediately after the end of the surgical procedure, and 1 day and 3, 5, and 7 days later. Plasma levels of total keratin 18, keratin 18 fragments, interleukin 6, tumor necrosis factor α, and soluble intercellular adhesion molecule 1 were measured. RESULTS: Total keratin 18 was demonstrated to be favorable in its prognostic value for early identification of a complicated course in comparison to routine markers of liver impairment (e.g., aspartate aminotransferase, alanine aminotransferase, lactate dehydrogenase). In contrast, inflammation markers (e.g., interleukin 6, tumor necrosis factor α and soluble intercellular adhesion molecule 1) were unsuitable for predicting early complications after liver transplantation from deceased donors. CONCLUSIONS: For early identification of patients at high risk for complications, the implementation of total keratin 18 measurements in routine diagnostics after orthotopic liver transplantation from deceased donors should be taken into consideration.