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Schmitt MC, Vogelsmeier LVDE, Erbas Y, Stuber S, Lischetzke T. Exploring Within-Person Variability in Qualitative Negative and Positive Emotional Granularity by Means of Latent Markov Factor Analysis. Multivariate Behav Res 2024:1-20. [PMID: 38600826 DOI: 10.1080/00273171.2024.2328381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Emotional granularity (EG) is an individual's ability to describe their emotional experiences in a nuanced and specific way. In this paper, we propose that researchers adopt latent Markov factor analysis (LMFA) to investigate within-person variability in qualitative EG (i.e., variability in distinct granularity patterns between specific emotions across time). LMFA clusters measurement occasions into latent states according to state-specific measurement models. We argue that state-specific measurement models of repeatedly assessed emotion items can provide information about qualitative EG at a given point in time. Applying LMFA to the area of EG for negative and positive emotions separately by using data from an experience sampling study with 11,662 measurement occasions across 139 participants, we found three latent EG states for the negative emotions and three for the positive emotions. Momentary stress significantly predicted transitions between the EG states for both the negative and positive emotions. We further identified two and three latent classes of individuals who differed in state trajectories for negative and positive emotions, respectively. Neuroticism and dispositional mood regulation predicted latent class membership for negative (but not for positive) emotions. We conclude that LMFA may enrich EG research by enabling more fine-grained insights into variability in qualitative EG patterns.
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Affiliation(s)
- Marcel C Schmitt
- Department of Psychology, RPTU Kaiserslautern-Landau, Landau, Germany
| | | | - Yasemin Erbas
- Department of Developmental Psychology, Tilburg University, Tilburg, The Netherlands
- Department of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium
| | - Simon Stuber
- Department of Psychology, RPTU Kaiserslautern-Landau, Landau, Germany
| | - Tanja Lischetzke
- Department of Psychology, RPTU Kaiserslautern-Landau, Landau, Germany
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Castro-Alvarez S, Sinharay S, Bringmann LF, Meijer RR, Tendeiro JN. Assessment of fit of the time-varying dynamic partial credit model using the posterior predictive model checking method. Br J Math Stat Psychol 2024. [PMID: 38379504 DOI: 10.1111/bmsp.12339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/22/2024]
Abstract
Several new models based on item response theory have recently been suggested to analyse intensive longitudinal data. One of these new models is the time-varying dynamic partial credit model (TV-DPCM; Castro-Alvarez et al., Multivariate Behavioral Research, 2023, 1), which is a combination of the partial credit model and the time-varying autoregressive model. The model allows the study of the psychometric properties of the items and the modelling of nonlinear trends at the latent state level. However, there is a severe lack of tools to assess the fit of the TV-DPCM. In this paper, we propose and develop several test statistics and discrepancy measures based on the posterior predictive model checking (PPMC) method (PPMC; Rubin, The Annals of Statistics, 1984, 12, 1151) to assess the fit of the TV-DPCM. Simulated and empirical data are used to study the performance of and illustrate the effectiveness of the PPMC method.
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Affiliation(s)
- Sebastian Castro-Alvarez
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Human Ecology, University of California, Davis, California, USA
| | | | - Laura F Bringmann
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Rob R Meijer
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Jorge N Tendeiro
- Office of Research and Academia-Government-Community Collaboration, Education, Research Center for Artificial Intelligence and Data Innovation, Hiroshima University, Higashihiroshima, Japan
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Castro-Alvarez S, Bringmann LF, Meijer RR, Tendeiro JN. A Time-Varying Dynamic Partial Credit Model to Analyze Polytomous and Multivariate Time Series Data. Multivariate Behav Res 2024; 59:78-97. [PMID: 37318274 DOI: 10.1080/00273171.2023.2214787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
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.
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Affiliation(s)
- Sebastian Castro-Alvarez
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Laura F Bringmann
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rob R Meijer
- Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Jorge N Tendeiro
- Office of Research and Academia-Government-Community Collaboration, Education and Research Center for Artificial Intelligence and Data Innovation, Hiroshima University, Japan
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Vogelsmeier LVDE, Vermunt JK, De Roover K. How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa. Behav Res Methods 2023; 55:2387-2422. [PMID: 36050575 PMCID: PMC10439104 DOI: 10.3758/s13428-022-01898-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2022] [Indexed: 11/08/2022]
Abstract
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.
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Affiliation(s)
- Leonie V. D. E. Vogelsmeier
- Department of Methodology and Statistics, Tilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands
| | - Jeroen K. Vermunt
- Department of Methodology and Statistics, Tilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands
| | - Kim De Roover
- Department of Methodology and Statistics, Tilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands
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Smit AC, Schat E, Ceulemans E. The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications. Assessment 2023; 30:1354-1368. [PMID: 35603660 PMCID: PMC10248291 DOI: 10.1177/10731911221086985] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.
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Affiliation(s)
- Arnout C. Smit
- University of Groningen, the
Netherlands
- VU Amsterdam, the Netherlands
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van den Heuvel MI, Bülow A, Heininga VE, de Moor EL, Janssen LHC, Vanden Abeele M, Boekhorst MGBM. Tracking Infant Development With a Smartphone: A Practical Guide to the Experience Sampling Method. Front Psychol 2021; 12:703743. [PMID: 35035365 PMCID: PMC8752460 DOI: 10.3389/fpsyg.2021.703743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
The COVID-19 pandemic has forced developmental researchers to rethink their traditional research practices. The growing need to study infant development at a distance has shifted our research paradigm to online and digital monitoring of infants and families, using electronic devices, such as smartphones. In this practical guide, we introduce the Experience Sampling Method (ESM) – a research method to collect data, in the moment, on multiple occasions over time – for examining infant development at a distance. ESM is highly suited for assessing dynamic processes of infant development and family dynamics, such as parent-infant interactions and parenting practices. It can also be used to track highly fluctuating family dynamics (e.g., infant and parental mood or behavior) and routines (e.g., activity levels and feeding practices). The aim of the current paper was to provide an overview by explaining what ESM is and for what types of research ESM is best suited. Next, we provide a brief step-by-step guide on how to start and run an ESM study, including preregistration, development of a questionnaire, using wearables and other hardware, planning and design considerations, and examples of possible analysis techniques. Finally, we discuss common pitfalls of ESM research and how to avoid them.
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Affiliation(s)
- Marion I. van den Heuvel
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, Netherlands
- *Correspondence: Marion I. van den Heuvel,
| | - Anne Bülow
- Department of Psychology Education & Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands
- Department of Developmental Psychology, Tilburg University, Tilburg, Netherlands
| | - Vera E. Heininga
- Department of Developmental Psychology, Groningen University, Groningen, Netherlands
- Department of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium
| | | | - Loes H. C. Janssen
- Department of Clinical Psychology, Leiden University, Leiden, Netherlands
| | - Mariek Vanden Abeele
- imec-mict-UGent, Department of Communication Sciences, Ghent University, Ghent, Belgium
| | - Myrthe G. B. M. Boekhorst
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, Netherlands
- Department of Medical and Clinical Psychology, Tilburg University, Tilburg, Netherlands
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