The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, namely the mixed-effects TSO (ME-TSO). Hence, the ME-TSO model is a measurement model suitable to analyze intensive longitudinal data that allows studying the psychometric properties of the indicators per individual, the heterogeneity of psychological dynamics, and the person-situation interaction effects. We showcase how to use the model by analyzing the items of positive affect activation of the crowdsourcing study HowNutsAreTheDutch (HoeGekisNL).
We wish to thank prof. dr. Peter de Jonge and the team of HoeGekisNL, as well as the participants, for allowing us to use their data in this study. We would also like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster.Partial results of this study were presented at the measurement error in longitudinal data workshop held in the university of Manchester in June 2019 and at the IMPS 2019 meeting in Santiago de Chile. Additionally, all the code developed during this research is available in the following GitHub repository: https://github.com/secastroal/LST_Analyses. We are also very grateful for the helpful comments provided by four anonymous reviewers. Finally, we are especially thankful to dr. Markus I. Eronen and María Angélica Acevedo-Mesa, who help us to proofread the revised manuscript.
The accessibility to electronic devices and the novel statistical methodologies available have allowed researchers to comprehend psychological processes at the individual level. However, there are still great challenges to overcome as, in many cases, collected data are more complex than the available models are able to handle. For example, most methods assume that the variables in the time series are measured on an interval scale, which is not the case when Likert-scale items were used. Ignoring the scale of the variables can be problematic and bias the results. Additionally, most methods also assume that the time series are stationary, which is rarely the case. To tackle these disadvantages, we propose a model that combines the partial credit model (PCM) of the item response theory framework and the time-varying autoregressive model (TV-AR), which is a popular model used to study psychological dynamics. The proposed model is referred to as the time-varying dynamic partial credit model (TV-DPCM), which allows to appropriately analyze multivariate polytomous data and nonstationary time series. We test the performance and accuracy of the TV-DPCM in a simulation study. Lastly, by means of an example, we show how to fit the model to empirical data and interpret the results.
In this article, the newly created GGUM R package is presented. This package finally brings the generalized graded unfolding model (GGUM) to the front stage for practitioners and researchers. It expands the possibilities of fitting this type of item response theory (IRT) model to settings that, up to now, were not possible (thus, beyond the limitations imposed by the widespread GGUM2004 software). The outcome is therefore a unique software, not limited by the dimensions of the data matrix or the operating system used. It includes various routines that allow fitting the model, checking model fit, plotting the results, and also interacting with GGUM2004 for those interested. The software should be of interest to all those who are interested in IRT in general or to ideal point models in particular.
The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, namely the mixed-effects TSO (ME-TSO). Hence, the ME-TSO model is a measurement model suitable to analyze intensive longitudinal data that allows studying the psychometric properties of the indicators per individual, the heterogeneity of psychological dynamics, and the person-situation interaction effects. We showcase how to use the model by analyzing the items of positive affect activation of the crowdsourcing study HowNutsAreTheDutch (HoeGekisNL).
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