TY - JOUR
T1 - Measurement in Intensive Longitudinal Data
AU - McNeish, Daniel
AU - Mackinnon, David P.
AU - Marsch, Lisa A.
AU - Poldrack, Russell A.
N1 - Funding Information:
This work was supported by the National Institutes of Health (NIH) Science of Behavior Change Common Fund Program through an award administered by the National Institute for Drug Abuse (NIDA) (UH2/UH3DA041713).
Funding Information:
This work was supported by the National Institutes of Health (NIH) Science of Behavior Change Common Fund Program through an award administered by the National Institute on Drug Abuse (NIDA) (UH2/UH3DA041713) and by an an award administered by the National Institute on Drug abuse [R37DA09757].
Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - EMA
KW - Measurement invariance
KW - cross-classified factor analysis
KW - dynamic structural equation modelling
KW - time-series analysis
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U2 - 10.1080/10705511.2021.1915788
DO - 10.1080/10705511.2021.1915788
M3 - Article
AN - SCOPUS:85106272521
SN - 1070-5511
VL - 28
SP - 807
EP - 822
JO - Structural Equation Modeling
JF - Structural Equation Modeling
IS - 5
ER -