Growth Curve Analysis in Contemporary Psychological Research
By J. Mcardle, J. Nesselroade. 2003
294 citations recorded by the source corpus.
Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/c07dfde1ccb3d4859e388e04ad899f1f3be0324a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Experimental and Cognitive Psychology
- Huy P Phan
- M. Voelkle
- Steve W. J. Kozlowski
- J. Mcardle
- S. Scheibe
- Lijuan Wang
- Nilam Ram
- K. Schweizer
In Management Science and Operations Research
- M. Voelkle
- Huy P Phan
- Steve W. J. Kozlowski
- Nilam Ram
- Kevin J. Grimm
- J. Mcardle
- J. Nesselroade
- Lijuan Wang
Citations in the corpus
- Trajectories of brain aging in middle-aged and older adults: Regional and individual differences (2010, unknown): “Although statistical methods for evaluating change in the context of individual differences such as latent difference score models (McArdle, 2009; McArdle and Nesselroade, 2003) are widely available, only a few studies of brain aging have benefited from them (McArdle et al.”
- Bayesian analysis of longitudinal data using growth curve models (2007, methodology): “Illustrative is fixing the first and second coefficients to be 0 and 1 and estimating the other basis coefficients (McArdle & Nesselroade, 2003).”
- Emotional Experience Improves With Age: Evidence Based on Over 10 Years of Experience Sampling (2011, methodology): “To accommodate the nested aspect of the data (waves within persons), we ran a series of multilevel models of change (McArdle & Nesselroade, 2003; Singer & Willet, 2003).”
- Latent growth curve modeling as an integrative approach to the analysis of change (2007, methodology): “LGCmodels have their roots in factor analysis, going back to Tucker (1958) and Rao (1958) who proposed the application of factor analytic techniques to the organization of individual growth curves (see McArdle & Nesselroade, 2002, or Bollen & Curran, 2006, for a brief history of LGCM).”
- Planned missing designs to optimize the efficiency of latent growth parameter estimates (2014, methodology): “Latent growth curve models Latent growth curve models (LGMs), (e.g., McArdle & Epstein, 1987; McArdle & Nesselroade, 2003; Preacher, Wichman, MacCallum & Briggs, 2008) are some of the most popular models for longitudinal data, because they allow researchers to simultaneously model group trends in…”
- Relations between informational sources, self-efficacy and academic achievement: a developmental approach (2012, methodology): “…al., 2008; Odgers et al., 2009) have, for example, used complex causal modelling procedures, such as latent growth modelling (Bollen & Curran, 2006; McArdle & Nesselroade, 2003; Muthén & Curran, 1997), to explore the developmental course of various cognitive-motivational processes and self-beliefs…”
- Advancing research on team process dynamics (2015, methodology): “However, relevant techniques include multilevel random coefficient modeling and latent growth modeling (MacCallum & Austin, 2000; McArdle & Nesselroade, 2003; Raudenbush, 2001), network analysis (Carley, 2003; Contractor, Wasserman, & Faust, 2006), and latent vector autoregressive and system…”
- The Development of English and Mathematics Self-Efficacy: A Latent Growth Curve Analysis (2012, methodology): “Methodologically, I included the use of LGM procedures (Bollen & Curran, 2006; Duncan et al., 1999; Hancock & Lawrence, 2006; McArdle & Nesselroade, 2003) to trace the developmental course in relationships between the four major sources of information and self-efficacy over time.”
- A Simulation Study Comparison of Bayesian Estimation With Conventional Methods for Estimating Unknown Change Points (2008, methodology): “…Wang (in press) illustrated the use of three estimation methods including the profile likelihood estimation method, the first-order Taylor expansion method, and the Bayesian estimation method to estimate age-based change points in the Bradway–McArdle study (see, e.g., McArdle & Nesselroade, 2003).”
- Investigating Experimental Effects Within the Framework of Structural Equation Modeling: An Example With Effects on Both Error Scores and Reaction Times (2008, background): “This characteristic is shared with latent curve (or latent growth curve) models (Duncan & Duncan, 1995, 2004; McArdle, 1986, 1988; McArdle & Epstein, 1987; Meredith & Tisak, 1984, 1990).”