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Factorial Invariance Within Longitudinal Structural Equation Models: Measuring the Same Construct Across Time
Author(s) -
Widaman Keith F.,
Ferrer Emilio,
Conger Rand D.
Publication year - 2010
Publication title -
child development perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3
H-Index - 71
eISSN - 1750-8606
pISSN - 1750-8592
DOI - 10.1111/j.1750-8606.2009.00110.x
Subject(s) - construct (python library) , factorial , measurement invariance , latent variable , psychology , metric (unit) , structural equation modeling , measure (data warehouse) , latent variable model , longitudinal study , function (biology) , scale (ratio) , cognitive psychology , confirmatory factor analysis , social psychology , statistics , mathematics , computer science , mathematical analysis , data mining , operations management , physics , quantum mechanics , evolutionary biology , biology , economics , programming language
— Charting change in behavior as a function of age and investigating longitudinal relations among constructs are primary goals of developmental research. Traditionally, researchers rely on a single measure (e.g., scale score) for a given construct for each person at each occasion of measurement, assuming that measure reflects the same construct at each occasion. With multiple indicators of a latent construct at each time of measurement, the researcher can evaluate whether factorial invariance holds. If factorial invariance constraints are satisfied, latent variable scores at each time of measurement are on the same metric and stronger conclusions are warranted. This article discusses factorial invariance in longitudinal studies, contrasting analytic approaches and highlighting strengths of the multiple‐indicator approach to modeling developmental processes.