Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies

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Abstract

When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.
Original languageEnglish
Pages (from-to)557-575
JournalStructural Equation Modeling
Volume26
Issue number4
DOIs
Publication statusPublished - 2019

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Longitudinal Study
Factor analysis
Factor Analysis
factor analysis
longitudinal study
Methodology
methodology
Longitudinal Data
Markov processes
Markov chain
artifact
Person
well-being
Clustering
Model
Sampling
interpretation
human being
Range of data
time

Keywords

  • experience sampling
  • measurement invariance
  • factor analysis
  • latent Markov modeling
  • EXPLORATORY FACTOR-ANALYSIS
  • CROSS-CULTURAL RESEARCH
  • WEAK FACTOR LOADINGS
  • MAXIMUM-LIKELIHOOD
  • MONTE-CARLO
  • COMPONENT ANALYSIS
  • ANALYTIC ROTATION
  • RESPONSE STYLE
  • SAMPLE-SIZE
  • DAILY-LIFE

Cite this

@article{e17222fbc2fc46258e848ca2f89c7755,
title = "Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies",
abstract = "When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.",
keywords = "experience sampling, measurement invariance, factor analysis, latent Markov modeling, EXPLORATORY FACTOR-ANALYSIS, CROSS-CULTURAL RESEARCH, WEAK FACTOR LOADINGS, MAXIMUM-LIKELIHOOD, MONTE-CARLO, COMPONENT ANALYSIS, ANALYTIC ROTATION, RESPONSE STYLE, SAMPLE-SIZE, DAILY-LIFE",
author = "Vogelsmeier, {Leonie V.D.E.} and Vermunt, {Jeroen K.} and {van Roekel}, Eeske and {De Roover}, Kim",
year = "2019",
doi = "10.1080/10705511.2018.1554445",
language = "English",
volume = "26",
pages = "557--575",
journal = "Structural Equation Modeling",
issn = "1070-5511",
publisher = "ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD",
number = "4",

}

TY - JOUR

T1 - Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies

AU - Vogelsmeier, Leonie V.D.E.

AU - Vermunt, Jeroen K.

AU - van Roekel, Eeske

AU - De Roover, Kim

PY - 2019

Y1 - 2019

N2 - When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.

AB - When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.

KW - experience sampling

KW - measurement invariance

KW - factor analysis

KW - latent Markov modeling

KW - EXPLORATORY FACTOR-ANALYSIS

KW - CROSS-CULTURAL RESEARCH

KW - WEAK FACTOR LOADINGS

KW - MAXIMUM-LIKELIHOOD

KW - MONTE-CARLO

KW - COMPONENT ANALYSIS

KW - ANALYTIC ROTATION

KW - RESPONSE STYLE

KW - SAMPLE-SIZE

KW - DAILY-LIFE

U2 - 10.1080/10705511.2018.1554445

DO - 10.1080/10705511.2018.1554445

M3 - Article

VL - 26

SP - 557

EP - 575

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 4

ER -