Continuous-time Latent Markov Factor Analysis for exploring measurement model changes across time

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Abstract

Drawing valid inferences about daily or long-term dynamics of psychological constructs (e.g., depression) requires the measurement model (indicating which constructs are measured by which items) to be invariant within persons over time. However, it might be affected by time- or situation-specific artifacts (e.g., response styles) or substantive changes in item interpretation. To efficiently evaluate longitudinal measurement invariance, and violations thereof, we proposed Latent Markov factor analysis (LMFA), which clusters observations based on their measurement model into separate states, indicating which measures are validly comparable. LMFA is, however, tailored to “discretetime” data, where measurement intervals are equal, which is often not the case in longitudinal data. In this paper, we extend LMFA to accommodate unequally spaced intervals. The so-called “continuous-time” (CT) approach considers the measurements as snapshots of continuously evolving processes. A simulation study compares CT-LMFA parameter estimation to its discrete-time counterpart and a depression data application shows the advantages of CT-LMFA.
Original languageEnglish
Pages (from-to)29–42
JournalMethodology: European Journal of Research Methods for the Behavioral and Social Sciences
Volume15
Issue numberSuppl 1
DOIs
Publication statusPublished - 2019

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Statistical Factor Analysis
factor analysis
Artifacts
time
artifact
simulation
interpretation
human being

Keywords

  • experience sampling
  • measurement invariance
  • factor analysis
  • latent Markov modeling
  • continuous-time

Cite this

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title = "Continuous-time Latent Markov Factor Analysis for exploring measurement model changes across time",
abstract = "Drawing valid inferences about daily or long-term dynamics of psychological constructs (e.g., depression) requires the measurement model (indicating which constructs are measured by which items) to be invariant within persons over time. However, it might be affected by time- or situation-specific artifacts (e.g., response styles) or substantive changes in item interpretation. To efficiently evaluate longitudinal measurement invariance, and violations thereof, we proposed Latent Markov factor analysis (LMFA), which clusters observations based on their measurement model into separate states, indicating which measures are validly comparable. LMFA is, however, tailored to “discretetime” data, where measurement intervals are equal, which is often not the case in longitudinal data. In this paper, we extend LMFA to accommodate unequally spaced intervals. The so-called “continuous-time” (CT) approach considers the measurements as snapshots of continuously evolving processes. A simulation study compares CT-LMFA parameter estimation to its discrete-time counterpart and a depression data application shows the advantages of CT-LMFA.",
keywords = "experience sampling, measurement invariance, factor analysis, latent Markov modeling, continuous-time",
author = "Vogelsmeier, {Leonie V.D.E.} and Vermunt, {Jeroen K.} and Florian B{\"o}ing-Messing and {De Roover}, Kim",
year = "2019",
doi = "10.1027/1614-2241/a000176",
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AU - Vermunt, Jeroen K.

AU - Böing-Messing, Florian

AU - De Roover, Kim

PY - 2019

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N2 - Drawing valid inferences about daily or long-term dynamics of psychological constructs (e.g., depression) requires the measurement model (indicating which constructs are measured by which items) to be invariant within persons over time. However, it might be affected by time- or situation-specific artifacts (e.g., response styles) or substantive changes in item interpretation. To efficiently evaluate longitudinal measurement invariance, and violations thereof, we proposed Latent Markov factor analysis (LMFA), which clusters observations based on their measurement model into separate states, indicating which measures are validly comparable. LMFA is, however, tailored to “discretetime” data, where measurement intervals are equal, which is often not the case in longitudinal data. In this paper, we extend LMFA to accommodate unequally spaced intervals. The so-called “continuous-time” (CT) approach considers the measurements as snapshots of continuously evolving processes. A simulation study compares CT-LMFA parameter estimation to its discrete-time counterpart and a depression data application shows the advantages of CT-LMFA.

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KW - measurement invariance

KW - factor analysis

KW - latent Markov modeling

KW - continuous-time

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