Search Results

Source: Psychological Methods
Resulting in 2 citations.
1. Biesanz, Jeremy C.
Deeb-Sossa, Natalia
Papadakis, Alison A.
Bollen, Kenneth A.
Curran, Patrick J.
The Role of Coding Time in Estimating and Interpreting Growth Curve Models
Psychological Methods 9,1 (March 2004): 30-52.
Also: http://psycnet.apa.org/journals/met/9/1/30/
Cohort(s): Children of the NLSY79
Publisher: American Psychological Association (APA)
Keyword(s): Modeling, Growth Curve/Latent Trajectory Analysis; Weight

The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of recoding time in growth curve models. Linear and quadratic growth model examples are provided, and the interpretation of estimates given a particular coding of time is illustrated. How and why the precision and statistical power of predictors of lower order growth curve components changes over time is illustrated and discussed. Recommendations include coding time to produce readily interpretable estimates and graphing lower order effects across time with appropriate confidence intervals to help illustrate and understand the growth process.
Bibliography Citation
Biesanz, Jeremy C., Natalia Deeb-Sossa, Alison A. Papadakis, Kenneth A. Bollen and Patrick J. Curran. "The Role of Coding Time in Estimating and Interpreting Growth Curve Models." Psychological Methods 9,1 (March 2004): 30-52.
2. Ganzach, Yoav
Misleading Interaction and Curvilinear Terms
Psychological Methods 2,3 (September 1997): 235-247.
Also: http://psycnet.apa.org/journals/met/2/3/235/
Cohort(s): NLSY79
Publisher: American Psychological Association (APA)
Keyword(s): Educational Aspirations/Expectations; Fathers, Influence; Modeling; Modeling, Nonparametric Regression; Mothers, Education; Parental Influences

This article examines the relationships between interaction (product) terms and curvilinear (quadratic) terms in regression models in which the independent variables are correlated. The author uses 2 substantive examples to demonstrate the following outcomes: (a) If the appropriate quadratic terms are not added to the estimated model, then the observed interaction may indicate a synergistic (offsetting) relationship between the independent variables, whereas the true relationship is, in fact, offsetting (synergistic). (b) If the appropriate product terms are not added to the equation, then the estimated model may indicate concave (convex) relationships between the independent variables and the dependent variable, whereas the true relationship is, in fact, convex (concave). (c) If the appropriate product and quadratic terms are not examined simultaneously, then the observed interactive or curvilinear relationships may be nonsignificant when such relationships exist. The implications of these results for the examination of interaction and quadratic effects in multiple regression analysis are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)
Bibliography Citation
Ganzach, Yoav. "Misleading Interaction and Curvilinear Terms." Psychological Methods 2,3 (September 1997): 235-247.