Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)
Added by: mortezaactive2 | Karma: 28.55 | Black Hole | 6 January 2015
1
Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)
Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation
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Added by: zheka1995-1995 | Karma: 9357.69 | Black Hole | 27 November 2014
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Academic Writing. A practical guide for students
Published in 2003 by Nelson Thornes Ltd. 192 pages. Part 1: The Writing Process. Part 2: Elements of Writing. Part 3: Accuracy in Writing. Part 4: Writing models.
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Added by: Starcn | Karma: 199.38 | Black Hole | 31 July 2014
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The Business Book (Big Ideas Simply Explained)
Packed with innovative graphics and simple explanations of business concepts, from managing risk and alternative business models to effective leadership and thinking outside the box, The Business Book covers every facet of business management.
Big ideas make great business thinkers and leaders. From Adam Smith and Andrew Carnegie to Bill Gates and Warren Buffett, The Business Book is perfect for college students, would-be entrepreneurs, or anyone interested in how business works.
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The interdisciplinary linguistic attractor model portrays language processing as linked sequences of fractal sets, and examines the changing dynamics of such sets for individuals as well as the speech community they comprise. Its motivation stems from human anatomic constraints and several artificial neural network approaches. It uses general computation theory to: (1) demonstrate the capacity of Cantor-like fractal sets to perform as Turing Machines; (2) better distinguish between models that simply match outputs ("emulation") and models that match both outputs and internal dynamics ("simulation"); and (3) relate language processing to essential computation steps executed in parallel. .
