This workshop covers multistep mixture modeling for latent transition analysis using Mplus, combining conceptual foundations with detailed implementation guidance. It explores 3-step, BCH, and 2-step imputation approaches, transition-specific distal outcomes, multiple-group and random-intercept LTA, and common troubleshooting strategies.

As empirical applications and methodological research on second-order exploratory factor analysis have matured, important refinements to estimation procedures, interpretive frameworks, and theoretical expectations have emerged. These advances establish second-order EFA as a rigorous alternative to bi-factor EFA and to conventional EFA models characterized by substantial factor correlations. We further demonstrate that bi-factor EFA models constitute a reparameterization of second-order EFA with direct effects, clarifying the formal relationship between these approaches. The unification of bi-factor and second-order EFA models reveals a novel rotation approach for bi-factor solutions that outperforms bi-geomin in simulation studies and yields more interpretable factor solutions in empirical studies.

This workshop explores methods for detecting, estimating, and reporting direct effects in latent class analysis using Mplus, including penalized SEM, DIFF priors, and class-varying specifications. It combines conceptual exposition with practical Mplus implementation and an applied antisocial behavior example to demonstrate end-to-end analysis and interpretation.

In this paper, we describe a three level dynamic structural modeling framework as a generalization of the DSEM framework of Asparouhov et al. Model estimation is discussed and the framework is illustrated with simulation studies and practical examples. Two common scenarios are described. The first is intensive longitudinal data for a group of individuals where observations are nested within days, periods, waves or bursts. The second is intensive longitudinal data for individuals nested within higher level clusters. Comparisons are made with two-level DSEM models and emphasis is given to what can be learned from the additional level of clustering.

This seminar provides an in-depth exploration of cross-lagged modeling for categorical variables using Mplus, focusing on interpreting temporal relationships and addressing complex modeling challenges in longitudinal research. Participants will gain practical skills in specifying models, handling missing data, and reporting results, enhancing their ability to conduct rigorous academic research and critically evaluate existing literature.

This seminar provides an in-depth exploration of cross-lagged modeling using Mplus, focusing on analyzing causal relationships in longitudinal studies with continuous variables. Attendees will gain technical skills to set up and interpret models, address common data issues, and integrate advanced statistical techniques into their research, with an official Instats certificate provided upon completion.

Intensive longitudinal data analysis, commonly used in psychological studies, often concerns outcomes that have strong floor effects, that is, a large percentage at its lowest value. Ignoring a strong floor effect, using regular analysis with modeling assumptions suitable for a continuous-normal outcome, is likely to give misleading results. This article suggests that two-part modeling may provide a solution. It can avoid potential biasing effects due to ignoring the floor effect. It can also provide a more detailed description of the relationships between the outcome and covariates allowing different covariate effects for being at the floor or not and the value above the floor. A smoking cessation example is analyzed to demonstrate available analysis techniques. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

This seminar provides an in-depth exploration of Dynamic Structural Equation Modeling (DSEM) using Mplus, allowing participants to understand and apply this advanced technique to analyze time series data and capture dynamic processes. Led by expert Bengt Muthen, the workshop equips researchers and PhD students with practical skills and theoretical knowledge essential for implementing complex dynamic models and enhancing their analytical capabilities in longitudinal research.

This seminar, led by renowned scholar Bengt Muthen, delves into the advancements of Structural Equation Modeling (SEM) over the past decades with a focus on a modern reinterpretation of Wheaton et al.'s seminal longitudinal study. Participants will gain expertise in applying advanced SEM techniques using Mplus, enhancing their ability to conduct robust longitudinal data analyses across various research domains.

This insightful seminar focuses on utilizing Mplus software to conduct Latent Transition Analysis (LTA) and Random Intercept Latent Transition Analysis (RI-LTA), essential for understanding complex patterns in longitudinal data. Led by expert Bengt Muthen, participants will enhance their skills in applying these advanced statistical methods to uncover dynamic processes in various research fields.

Alcohol use has been shown to increase stress, and there is some evidence that stress predicts subsequent alcohol use during treatment for alcohol use disorder (AUD), particularly among females who are more likely to report coping-motivated drinking. Gaining a better understanding of the processes by which stress and alcohol use are linked during treatment could potentially inform AUD treatment planning. The current study aimed to characterize the association between stress and drinking during the course of AUD treatment and whether there were sex differences in these associations. Secondary data analyses of the COMBINE study (N = 1375; 69% male, 76.3% non-Hispanic and white, average age of 44.4 years) were conducted to examine self-reported perceived stress and alcohol consumption across 16 weeks of treatment for AUD using a Bayesian random-intercept cross-lagged panel model. There was stronger evidence for any alcohol use predicting greater than typical stress in subsequent weeks and less strong evidence for stress increasing the subsequent probability of alcohol use, particularly among males. For females, greater stress predicted subsequent drinking earlier in the treatment period, and a lower probability of subsequent drinking in the last week of treatment. Interventions might specifically focus on targeting reductions in stress following drinking occasions.

To date, cross-lagged panel modeling has been studied only for continuous outcomes. This article presents methods that are suitable also when there are binary and ordinal outcomes. Modeling, testing, identification, and estimation are discussed. A two-part ordinal model is proposed for ordinal variables with strong floor effects often seen in applications. An example considers the interaction between stress and alcohol use in an alcohol treatment study. Extensions to multiple-group analysis and modeling in the presence of trends are discussed.

This article presents dynamic structural equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the structural equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine time-series modeling with structural equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.

Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies. Maximum-likelihood and weighted least squares PSEM estimation is discussed for SEM models with continuous and categorical variables. We show that traditional EFA, multiple group alignment (MGA), and Bayesian SEM (BSEM) are examples of PSEM. The PSEM framework also extends standard SEM models with the possibility to structurally align various model parameters. Exploratory latent growth models, also referred to as Tuckerized curve models, can also be estimated in the PSEM framework and are illustrated here with simulation studies and an empirical example.

This article considers identification, estimation, and model fit issues for models with contemporaneous and reciprocal effects. It explores how well the models work in practice using Monte Carlo studies as well as real-data examples. Furthermore, by using models that allow contemporaneous and reciprocal effects, the paper raises a fundamental question about current practice for cross-lagged panel modeling using models such as cross-lagged panel model (CLPM) or random intercept cross-lagged panel model (RI-CLPM): Can cross-lagged panel modeling be relied on to establish cross-lagged effects? The article concludes that the answer is no, a finding that has important ramifications for current practice. It is suggested that analysts should use additional models to probe the temporalities of the CLPM and RI-CLPM effects to see if these could be considered contemporaneous rather than lagged.

This free seminar provides comprehensive training in multilevel and single-level modeling through Latent Transition Analysis (LTA), equipping researchers with enhanced analytical techniques using Mplus for diverse research applications. Participants will gain insights into LTA's practical applications, from handling longitudinal data to exploring multilevel factor analysis.

Penalized structural equation models (PSEM) is a powerful technique that unlocks a variety of new modeling frameworks. PSEM applications have been established previously for standard SEM and ESEM models. In this note we aim to extend these ideas to more general types of models such as finite mixture models, multilevel models as well as models with more general types of outcomes. Maximum likelihood and weighted least squares estimation methods naturally accommodate a penalty term. In Mplus 8.12 the PSEM methodology is implemented for all models that can be estimated with these two estimators. Therefore we can now easily combine the more general models with the features of PSEM such as EFA, Alignment, and parameter invariance. Some additional basic SEM applications are also included.

Cyclical phenomena are commonly observed in many areas of repeated measurements, especially with intensive longitudinal data. A typical example is circadian (24-hour) rhythm of physical measures such as blood pressure, heart rate, glucose level, and alertness. This paper focuses on positive affect, which is a common measure in psychological studies and for which circadian rhythm has been observed but not analyzed by modern statistical methods. The paper demonstrates that a large new analysis arsenal is available for analysis of cyclical features in intensive longitudinal data. This can help researchers extract more information from their data to get more valid estimates of coupled processes and to get new theoretical insights into circadian rhythms of mood. To assist in this effort, the analyses are based on general models with a rich set of features while still being accessible without an unduly steep learning curve. Scripts for the Mplus software are available for all the analyses presented.

This review summarizes the current state of the art of statistical and (survey) methodological research on measurement (non)invariance, which is considered a core challenge for the comparative social sciences. After outlining the historical roots, conceptual details, and standard procedures for measurement invariance testing, the paper focuses in particular on the statistical developments that have been achieved in the last 10 years. These include Bayesian approximate measurement invariance, the alignment method, measurement invariance testing within the multilevel modeling framework, mixture multigroup factor analysis, the measurement invariance explorer, and the response shift-true change decomposition approach. Furthermore, the contribution of survey methodological research to the construction of invariant measurement instruments is explicitly addressed and highlighted, including the issues of design decisions, pretesting, scale adoption, and translation. The paper ends with an outlook on future research perspectives.

Welzel et al. (2021) claim that non-invariance of instruments is inconclusive and inconsequential in the field for cross-cultural value measurement. In this response, we contend that several key arguments on which Welzel et al. (2021) base their critique of invariance testing are conceptually and statistically incorrect. First, Welzel et al. (2021) claim that value measurement follows a formative rather than reflective logic. Yet they do not provide sufficient theoretical arguments for this conceptualization, nor do they discuss the disadvantages of this approach for validation of instruments. Second, their claim that strong inter-item correlations cannot be retrieved when means are close to the endpoint of scales ignores the existence of factor-analytic approaches for ordered-categorical indicators. Third, Welzel et al. (2021) propose that rather than of relying on invariance tests, comparability can be assessed by studying the connection with theoretically related constructs. However, their proposal ignores that external validation through nomological linkages hinges on the assumption of comparability. By means of two examples, we illustrate that violating the assumptions of measurement invariance can distort conclusions substantially. Following the advice of Welzel et al. (2021) implies discarding a tool that has proven to be very useful for comparativists. Keywords Measurement invariance , cross-cultural research , reflective vs. formative measurement , ordered-categorical data analysis , nomological linkages