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Title: Outlying Observation Diagnostics in Growth Curve Modeling
Resulting in 1 citation.
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Tong, Xin Zhang, Zhiyong |
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Outlying Observation Diagnostics in Growth Curve Modeling Multivariate Behavioral Research 52,6 (2017): 768-788. Also: http://www.tandfonline.com/doi/full/10.1080/00273171.2017.1374824 Cohort(s): NLSY97 Publisher: Taylor & Francis Keyword(s): Modeling, Growth Curve/Latent Trajectory Analysis; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis Growth curve models are widely used for investigating growth and change phenomena. Many studies in social and behavioral sciences have demonstrated that data without any outlying observation are rather an exception, especially for data collected longitudinally. Ignoring the existence of outlying observations may lead to inaccurate or even incorrect statistical inferences. Therefore, it is crucial to identify outlying observations in growth curve modeling. This study comparatively evaluates six methods in outlying observation diagnostics through a Monte Carlo simulation study on a linear growth curve model, by varying factors of sample size, number of measurement occasions, as well as proportion, geometry, and type of outlying observations. It is suggested that the greatest chance of success in detecting outlying observations comes from use of multiple methods, comparing their results and making a decision based on research purposes. A real data analysis example is also provided to illustrate the application of the six outlying observation diagnostic methods. |
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Bibliography Citation
Tong, Xin and Zhiyong Zhang. "Outlying Observation Diagnostics in Growth Curve Modeling." Multivariate Behavioral Research 52,6 (2017): 768-788.
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