scholarly journals Mixed-Effects Trait-State-Occasion Model: Studying the Psychometric Properties and the Person-Situation Interactions of Psychological Dynamics

2021 ◽  
Author(s):  
Sebastian Castro-Alvarez ◽  
Jorge Tendeiro ◽  
Peter de Jonge ◽  
Rob Meijer ◽  
Laura Francina Bringmann
Keyword(s):  
Longitudinal Data ◽  
Positive Affect ◽  
Psychometric Properties ◽  
Measurement Model ◽  
Mixed Effects ◽  
Psychological Constructs ◽  
Intensive Longitudinal Data ◽  
Trait Theory ◽  
Psychological Dynamics ◽  
Carry Over

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).

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

2021 ◽  
Author(s):  
Leonie V. D. E. Vogelsmeier ◽  
Jeroen K. Vermunt ◽  
Kim De Roover
Keyword(s):  
Longitudinal Data ◽  
Open Source Software ◽  
Software Package ◽  
R Package ◽  
Measurement Model ◽  
Response Styles ◽  
Psychological Constructs ◽  
Intensive Longitudinal Data ◽  
Time Points ◽  
User Friendly

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Prior to investigating what the dynamics look like, it is important 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 introduce the new user-friendly software package lmfa, which allows researchers to perform the analysis in the freely available software R. We provide a step-by-step tutorial for the lmfa package so that researchers can easily investigate MM differences in their own ILD.

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

2020 ◽  
pp. 016327872097676
Author(s):  
Leonie V. D. E. Vogelsmeier ◽  
Jeroen K. Vermunt ◽  
Loes Keijsers ◽  
Kim De Roover
Keyword(s):  
Longitudinal Data ◽  
Measurement Invariance ◽  
Ordinal Data ◽  
Latent Trait ◽  
Well Being ◽  
Measurement Model ◽  
Social Contexts ◽  
Psychological Constructs ◽  
Intensive Longitudinal Data ◽  
Trait Analysis

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.

scholarly journals Measurement in Intensive Longitudinal Data

2021 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Self Regulation ◽  
Measurement Model ◽  
Equation Modeling ◽  
Longitudinal Methods ◽  
Intensive Longitudinal Data ◽  
Measurement Models ◽  
Technological Advances ◽  
Intensive Longitudinal Methods

Technological advances have increased the prevalence of intensive longitudinal data as well as statistical techniques appropriate for these data, such as dynamic structural equation modeling (DSEM). Intensive longitudinal designs often investigate constructs related to affect or mood and do so with multiple item scales. However, applications of intensive longitudinal methods often rely on simple sums or averages of the administered items rather than considering a proper measurement model. This paper demonstrates how to incorporate measurement models into DSEM to (1) provide more rigorous measurement of constructs used in intensive longitudinal studies and (2) assess whether scales are invariant across time and across people, which is not possible when item responses are summed or averaged. We provide an example from an ecological momentary assessment study on self-regulation in adults with binge eating disorder and walkthrough how to fit the model in Mplus and how to interpret the results.

scholarly journals Awareness is bliss: How acquiescence affects exploratory factor analysis

2021 ◽  
Author(s):  
E. Damiano D'Urso ◽  
Jesper Tijmstra ◽  
Jeroen K. Vermunt ◽  
Kim De Roover
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Exploratory Factor Analysis ◽  
A Priori ◽  
Measurement Model ◽  
Self Report ◽  
Psychological Constructs ◽  
Factor Ii ◽  
Target Rotation ◽  
Rotational Freedom

Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurement 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, afterwards, 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 (i) whether ARS is captured as an additional factor, (ii) the effect of different rotation approaches on the recovery of the content and ARS factors, and (iii) 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 (i.e., not 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 MM is specified a priori. Not extracting the additional ARS factor did not affect the loading recovery in unbalanced scales. Researchers should consider the potential presence of an additional ARS factor when assessing the psychometric properties of balanced scales, and use informed rotation approaches when suspecting that an additional factor is an ARS factor.

Construct Validity of Sensation Seeking: A Psychometric Investigation

1999 ◽  
Vol 20 (3) ◽  
pp. 155-171 ◽  
Author(s):  
André Beauducel ◽  
Burkhard Brocke ◽  
Alexander Strobel ◽  
Anja Strobel
Keyword(s):  
Substance Use ◽  
Construct Validity ◽  
Psychometric Properties ◽  
Factor Structure ◽  
Sensation Seeking ◽  
German Version ◽  
Trait Theory ◽  
Multilevel Theory ◽  
Sensation Seeking Scale ◽  
Psychometric Investigation

Abstract: Zuckerman postulated a biopsychological multilevel theory of Sensation Seeking, which is part of a more complex multi-trait theory, the Alternative Five. The Sensation Seeking Scale Form V (SSS V) was developed for the measurement of Sensation Seeking. The process of validation of Sensation Seeking as part of a multilevel theory includes analyses of relations within and between several levels of measurement. The present study investigates validity and basic psychometric properties of a German version of the SSS V in a broader context of psychometric traits. - The 120 participants were mainly students. They completed the SSS V, the Venturesomeness- and Impulsiveness-Scales of the IVE, the BIS/BAS-Scales, the ZKPQ and the NEO-FFI. - The results reveal acceptable psychometric properties for the SSS V but with limitations with regard to factor structure. Indications for criterion validity were obtained by prediction of substance use by the subscales Dis and BS. The results of a MTMM analysis, especially the convergent validities of the SSS V were quite satisfying. On the whole, the results yielded sufficient support for the validity of the Sensation Seeking construct or the instrument respectively. They also point to desirable modifications.

scholarly journals How intensive longitudinal data can stimulate advances in health behavior maintenance theories and interventions

2019 ◽  
Author(s):  
Genevieve F Dunton ◽  
Alexander J Rothman ◽  
Adam M Leventhal ◽  
Stephen S Intille
Keyword(s):  
Health Behavior ◽  
Longitudinal Data ◽  
Research Methods ◽  
Health Behavior Change ◽  
Noncommunicable Disease ◽  
Time Activity ◽  
Intensive Longitudinal Data ◽  
Reflective Systems ◽  
Behavior Maintenance

Abstract Interventions that promote long-term maintenance of behaviors such as exercise, healthy eating, and avoidance of tobacco and excessive alcohol are critical to reduce noncommunicable disease burden. Theories of health behavior maintenance tend to address reactive (i.e., automatic) or reflective (i.e., deliberative) decision-making processes, but rarely both. Progress in this area has been stalled by theories that say little about when, why, where, and how reactive and reflective systems interact to promote or derail a positive health behavior change. In this commentary, we discuss factors influencing the timing and circumstances under which an individual may shift between the two systems such as (a) limited availability of psychological assets, (b) interruption in exposure to established contextual cues, and (c) lack of intrinsic or appetitive motives. To understand the putative factors that regulate the interface between these systems, research methods are needed that are able to capture properties such as (a) fluctuation over short periods of time, (b) change as a function of time, (c) context dependency, (d) implicit and physiological channels, and (e) idiographic phenomenology. These properties are difficult to assess with static, cross-sectional, laboratory-based, or retrospective research methods. We contend that intensive longitudinal data (ILD) collection and analytic strategies such as smartphone and sensor-based real-time activity and location monitoring, ecological momentary assessment (EMA), machine learning, and systems modeling are well-positioned to capture and interpret within-person shifts between reactive and reflective systems underlying behavior maintenance. We conclude with examples of how ILD can accelerate the development of theories and interventions to sustain health behavior over the long term.

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