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Autor(en): 
  • Raykov Tenko
  • Marcoulides George A.
  • A First Course in Structural Equation Modeling 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  März 2006  
    Genre:  Wirtschaft / Recht 
     
    behavioral data analysis / introductory latent variable modeling / MATHEMATICS / Probability & Statistics / General / Mplus software applications / multivariate statistics / path analysis techniques / Probability & statistics / Probability and statistics
    ISBN:  9780805855883 
    EAN-Code: 
    9780805855883 
    Verlag:  Taylor and Francis 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 229 mm / B 152 mm / D 14 mm 
    Gewicht:  800 gr 
    Seiten:  248 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the Second Edition include: ¿ Review of latent change (growth) analysis models at an introductory level ¿ Coverage of the popular Mplus program ¿ Updated examples of LISREL and EQS ¿ Downloadable resources that contains all of the text's LISREL, EQS, and Mplus examples. A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

      



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