Continuous-Time Latent Markov Factor Analysis for Exploring 					Measurement Model Changes Across Time

Vogelsmeier, Leonie V. D. E.; Vermunt, Jeroen K.; Böing-Messing, Florian; Roover, Kim De

doi:10.1027/1614-2241/a000176

Cited by 15 publications

(24 citation statements)

References 36 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a discrete-time (DT-)LMM, intervals between measurements,

, are assumed to be equal. A continuous-time (CT-)LMM ( Böckenholt, 2005 ; Jackson & Sharples, 2002 ; Vogelsmeier, Vermunt, Böing-Messing, & De Roover, 2019 ) allows the intervals to differ across time-points and subjects, which is often more realistic in ESM studies and therefore applied throughout the rest of this paper. The transition probabilities

are collected in the

matrix

, where the row sums of

are equal to 1.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In a discrete-time (DT-)LMM, intervals between measurements,

are collected in the

matrix

, where the row sums of

are equal to 1.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the following, we provide a brief summary. The interested reader is referred to Böckenholt (2005) and Jackson and Sharples (2002) for general information about CT-LMM and to Vogelsmeier, Vermunt, Böing-Messing, et al (2019) for more specific information on CT-LMFA. In brief, transitioning from the origin state to destination state is defined by the 'intensities' (or rates) (collected in the × intensity matrix ) that replace the transition probabilities and can be seen as probabilities to transition between states per very small time unit:…”

Section: Structural Partmentioning

confidence: 99%

“…The latter also determined the ESM sampling scheme (comparable to Vogelsmeier, Vermunt, Böing-Messing, et al, 2019): Imposing that a sampling day lasts from 9 am to 9 pm, both day and night intervals were on average 12 hours long. The = 9 measurement occasions during the day would lead to intervals of 1.5 hours if the measurement-occasions were fixed.…”

Section: Problemmentioning

confidence: 99%

“…However, the MM might be affected by time-or situation-specific artefacts (e.g., response styles) or substantive changes in item interpretation. To efficiently evaluate (violations of) longitudinal measurement invariance for multiple subjects simultaneously, latent Markov factor analysis (LMFA; Vogelsmeier, Vermunt, van Roekel, & De Roover, 2019;Vogelsmeier, Vermunt, Böing-Messing, & De Roover, 2019) was proposed, which combines a discrete-or continuous-time latent Markov model with mixture factor analysis. As will be described in more detail in Section 2.2., the Markov model clusters subject-and time-point-specific observations according to the underlying MM into latent 'states'.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

Vogelsmeier¹,

Vermunt²,

Bülow³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Longitudinal measurement invariance (LMI) of the measurement model (MM) is a prerequisite for drawing valid inferences about daily dynamics of psychological constructs. To efficiently evaluate LMI for multiple subjects simultaneously, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects’ observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, the authors employed a full information maximum likelihood (FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state-memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

show abstract

“…To conveniently evaluate (violations of) invariance of intensive longitudinal data for multiple subjects simultaneously, latent Markov factor analysis (LMFA; Vogelsmeier, Vermunt, van Roekel, & De Roover, 2019;Vogelsmeier, Vermunt, B€ oing-Messing, & De Roover, 2019) was proposed, which combines a discrete-or continuous-time latent Markov model with mixture factor analysis. 1 As will be described in more detail in Section "Latent Markov Factor Analysis", the Markov model clusters subject-and time-point-specific observations according to their underlying MM into dynamic latent MM classes or "states", which implies that subjects can transition between latent states and thus between MMs over time.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

Vogelsmeier

Vermunt

Bülow

et al. 2021

Multivariate Behavioral Research

Self Cite

View full text Add to dashboard Cite

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete-or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

show abstract

Continuous-Time Latent Markov Factor Analysis for Exploring Measurement Model Changes Across Time

Cited by 15 publications

References 36 publications

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

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

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

Contact Info

Product

Resources

About