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Introduction to Multilevel Modeling Techniques (Record no. 1574)

MARC details
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fixed length control field 02638 a2200397 4500
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control field 0429595662
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250317100404.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250312042020GB 118 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780429595660
037 ## - SOURCE OF ACQUISITION
Source of stock number/acquisition Taylor & Francis
Terms of availability GBP 54.99
Form of issue BB
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Original cataloging agency 01
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Language code of text/sound track or separate title eng
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Subject category code EDU027000
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Subject category code 001.422
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100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Ronald Heck
245 10 - TITLE STATEMENT
Title Introduction to Multilevel Modeling Techniques
Remainder of title MLM and SEM Approaches
250 ## - EDITION STATEMENT
Edition statement 4
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Oxford
Name of publisher, distributor, etc. Routledge
Date of publication, distribution, etc. 20200324
300 ## - PHYSICAL DESCRIPTION
Extent 404 p
520 ## - SUMMARY, ETC.
Expansion of summary note Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online. This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Scott L. Thomas
Relationship A01

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