Distinguishing state variability from trait change in longitudinal data: The role of measurement (non)invariance in latent state-trait analyses
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Distinguishing state variability from trait change in longitudinal data : The role of measurement (non)invariance in latent state-trait analyses. / Geiser, Christian; Keller, Brian T.; Lockhart, Ginger et al.
In: Behavior Research Methods, Vol. 47, No. 1, 03.2015, p. 172-203.Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Distinguishing state variability from trait change in longitudinal data
T2 - The role of measurement (non)invariance in latent state-trait analyses
AU - Geiser, Christian
AU - Keller, Brian T.
AU - Lockhart, Ginger
AU - Eid, Michael
AU - Cole, David A.
AU - Koch, Tobias
PY - 2015/3
Y1 - 2015/3
N2 - Researchers analyzing longitudinal data often want to find out whether the process they study is characterized by (1) short-term state variability, (2) long-term trait change, or (3) a combination of state variability and trait change. Classical latent state-trait (LST) models are designed to measure reversible state variability around a fixed set-point or trait, whereas latent growth curve (LGC) models focus on long-lasting and often irreversible trait changes. In the present article, we contrast LST and LGC models from the perspective of measurement invariance testing. We show that establishing a pure state-variability process requires (1) the inclusion of a mean structure and (2) establishing strong factorial invariance in LST analyses. Analytical derivations and simulations demonstrate that LST models with noninvariant parameters can mask the fact that a trait-change or hybrid process has generated the data. Furthermore, the inappropriate application of LST models to trait change or hybrid data can lead to bias in the estimates of consistency and occasion specificity, which are typically of key interest in LST analyses. Four tips for the proper application of LST models are provided.
AB - Researchers analyzing longitudinal data often want to find out whether the process they study is characterized by (1) short-term state variability, (2) long-term trait change, or (3) a combination of state variability and trait change. Classical latent state-trait (LST) models are designed to measure reversible state variability around a fixed set-point or trait, whereas latent growth curve (LGC) models focus on long-lasting and often irreversible trait changes. In the present article, we contrast LST and LGC models from the perspective of measurement invariance testing. We show that establishing a pure state-variability process requires (1) the inclusion of a mean structure and (2) establishing strong factorial invariance in LST analyses. Analytical derivations and simulations demonstrate that LST models with noninvariant parameters can mask the fact that a trait-change or hybrid process has generated the data. Furthermore, the inappropriate application of LST models to trait change or hybrid data can lead to bias in the estimates of consistency and occasion specificity, which are typically of key interest in LST analyses. Four tips for the proper application of LST models are provided.
KW - Sociology
KW - state variability versus trait change
KW - Latent stat-trait analysis
KW - measurement invariance
KW - Latent growth curve models
KW - Model misspecification
UR - http://www.scopus.com/inward/record.url?scp=84896419003&partnerID=8YFLogxK
U2 - 10.3758/s13428-014-0457-z
DO - 10.3758/s13428-014-0457-z
M3 - Journal articles
C2 - 24652650
AN - SCOPUS:84896419003
VL - 47
SP - 172
EP - 203
JO - Behavior Research Methods
JF - Behavior Research Methods
SN - 1554-351X
IS - 1
ER -