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Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes.

• Authors : Leite, WalterL. (AUTHOR); Stapleton, LauraM. (AUTHOR)

Subjects: LINEAR statistical models; MONTE Carlo method; NONLINEAR theories

• Source: Journal of Experimental Education. 2011, Vol. 79 Issue 4, p361-381. 21p. 2 Charts, 7 Graphs.

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Interpreting Hierarchical Linear and Hierarchical Generalized Linear Models With Slopes as Outcomes.

• Authors : Tate, Richard

Subjects: LINEAR statistical models; MATHEMATICAL models; NONLINEAR statistical models

• Source: Journal of Experimental Education. Fall2004, Vol. 73 Issue 1, p71-95. 25p. 3 Graphs.

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Reasons for Hierarchical Linear Modeling: A Reminder.

• Authors : Wang, Jianjun

Subjects: LINEAR statistical models; MATHEMATICAL statistics

• Source: Journal of Experimental Education. Fall99, Vol. 68 Issue 1, p89. 5p.

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Using SAS PROC MIXED to demystify the hierarchical linear model.

• Authors : Wang, Jianjun

Subjects: LINEAR statistical models; STATISTICS; MATHEMATICAL models

• Source: Journal of Experimental Education. Fall97, Vol. 66 Issue 1, p84. 10p. 5 Charts.

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Education Full Text (H.W. Wilson)

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Education Full Text (H.W. Wilson)

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Education Full Text (H.W. Wilson)

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Education Full Text (H.W. Wilson)

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Education Full Text (H.W. Wilson)

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Education Full Text (H.W. Wilson)

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