Using simple and complex growth models to articulate developmental change: Matching theory to method

By Nilam Ram, Kevin J. Grimm. 2007

298 citations recorded by the source corpus.

Subfields: Education, Safety Research, Social Psychology

View Paper: https://www.semanticscholar.org/paper/cfe9dbe00768272cc4dd55c8b19e43da03ea6db9

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Citations in the corpus

Taking a “Peak” at Leisure Travelers’ Positive Emotions (2012, methodology): “To examine patterns of change in accordance with the peak model, we used multiphase growth curves (see e.g., Ram & Grimm, 2007).”
Modeling Longitudinal Data from Research on Adolescence (2009, background): “In fact, we will consistently echo Collins ’ s ( 2006 ) call for integrating the theoretical model, the temporal design, and the statistical model (see also Ram & Grimm, 2007 ) .”
Residual Structures in Latent Growth Curve Modeling (2010, background): “For example, change during kindergarten might be fundamentally different from changes after kindergarten, so a spline or multiphase model (Cudeck & Klebe, 2002; Ram & Grimm, 2007) that allows for different phases of development might be more appropriate.”
Studying Changes in Life Circumstances and Personality: It's about Time (2014, methodology): “By using statistical techniques such as growth curve modelling, it is relatively straightforward to model non-linear change (Grimm & Ram, 2009; Ram & Grimm, 2007), discontinuous (or piecewise) change (Kim & Kim, 2012; Singer & Willett, 2003), and the combination of the two.”
Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups (2009, methodology): “As a companion piece to our introduction to simple and complex growth curves (Ram & Grimm, 2007), we illustrate how those same data can be approached from another perspective.”
Mixture Modeling for Lifespan Developmental Research (2019, background): “A special attention will be given to longitudinal mixture models allowing to capture interindividual differences in intraindividual developmental processes (Ram & Grimm, 2007).”
An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists (2015, methodology): “…missing data treated via full information maximum likelihood or multiple imputation, and allow for the estimation of growth trends from latent variables with various invariance constraints (see Diallo & Morin, in press; Diallo, Morin, & Parker, in press; Ram & Grimm, 2007; Ram & Gerstorf, 2009).”