Modeling Contextual Effects in Longitudinal Studies by Todd D. Little, James A. Bovaird, Noel A. Card

By Todd D. Little, James A. Bovaird, Noel A. Card

This quantity experiences the demanding situations and substitute ways to modeling how participants switch throughout time and offers methodologies and knowledge analytic innovations for behavioral and social technology researchers. This available advisor offers concrete, transparent examples of the way contextual elements should be integrated in such a lot examine experiences. each one bankruptcy may be understood independently, permitting readers to first specialise in components such a lot proper to their paintings. the hole bankruptcy demonstrates some of the methods contextual elements are represented—as covariates, predictors, results, moderators, mediators, or mediated results. Succeeding chapters assessment "best perform" innovations for treating lacking information, making version comparisons, and scaling throughout developmental age levels. different chapters concentrate on particular statistical options akin to multilevel modeling and multiple-group and multilevel SEM, and the way to include exams of mediation, moderation, and moderated mediation. serious dimension and theoretical matters are mentioned, relatively how age could be represented and the ways that context will be conceptualized. the ultimate bankruptcy offers a compelling name to incorporate contextual components in theorizing and learn. This booklet will attract researchers and complicated scholars accomplishing developmental, social, medical, or academic examine, in addition to these in comparable parts similar to psychology and linguistics.

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With MAR missingness, although there is bias when the causes of missingness are not included in the model, the bias is much less of a problem than previously thought (Graham, Cumsille, & Elek-Fisk, 2003), even preferring MAR-based methods to alternatives for non-ignorable missingness: “. . The MAR assumption has been found to yield more accurate predictions of the missing values than methods based on the more natural NMAR mechanism” (Little & Rubin, 2002, p. 19). Within longitudinal research, however, one must consider not only which events or processes related may be related to nonresponse but more specifically, the timing and spacing of the measurements of those processes.

The MI procedure accounts for missing data in an initial step, referred to as the missing data model. The missing data model need not be the same as the substantive model of interest, and should contain all covariates believed to relate to the probability of missingness across variables. Missing values are “filled in” or imputed based on regression-predicted values (in which all other variables in the missing data model serve as predictors) along with a random error term. A series of data imputations is performed, in which multiple “complete” data sets are generated from the missing data model.

Washington, DC: American Psychological Association. Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100. Graham, J. , Cumsille, P. , & Elek-Fisk, E. (2003). Methods for handling missing data. In J. A. Schinka & W. F. ), Research methods in psychology (pp. 87-114). New York: Wiley. Graham, J. , & Hofer, S. M. (1993). EXE user’s guide. textupUnpublished manuscript. Graham, J. , & Hofer, S. M. (2000). Multiple imputation in multivariate research.

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