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3%OFFMcArdle, John J.; Nesselroade, John R. - Longitudinal Data Analysis Using Structural Equation Models - 9781433817151 - V9781433817151
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Longitudinal Data Analysis Using Structural Equation Models

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Description for Longitudinal Data Analysis Using Structural Equation Models Hardback. The authors identify five basic purposes of longitudinal structural equation modeling. For each purpose, they present the most useful strategies and models. Two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores. Num Pages: 424 pages. BIC Classification: JMBT. Category: (G) General (US: Trade). Dimension: 229 x 152 x 28. Weight in Grams: 939.
When determining the most appropriate method for analyzing longitudinal data, you must first consider what research question you want to answer.

In this book, McArdle and Nesselroade identify five basic purposes of longitudinal structural equation modeling. For each purpose, they present the most useful strategies and models. Two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores.

The book covers a wealth of models in a straightforward, understandable manner. Rather than overwhelm the reader with an extensive amount of algebra, the authors use path diagrams and emphasize methods that are ... Read more

Product Details

Format
Hardback
Publication date
2014
Publisher
American Psychological Association United States
Number of pages
424
Condition
New
Number of Pages
426
Place of Publication
Washington DC, United States
ISBN
9781433817151
SKU
V9781433817151
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1

About McArdle, John J.; Nesselroade, John R.
John J. (Jack) McArdle, PhD, is senior professor of psychology at the University of Southern California (USC), where he heads the Quantitative Methods Area and has been chair of the USC Research Committee.   He received a BA from Franklin & Marshall College (1973; Lancaster, PA) and both MA and PhD degrees from Hofstra University (1975, 1977; Hempstead, ... Read more

Reviews for Longitudinal Data Analysis Using Structural Equation Models
An excellent resource for graduate students and researchers.
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