Name: An Introduction to Multilevel Modeling Techniques: Mlm and sem Approaches (Quantitative Methodology Series) (en Inglés)
SKU: 52694518
Price: 169.76 USD
Availability: InStock
Author: Ronald H. Heck; Scott L. Thomas

portada An Introduction to Multilevel Modeling Techniques: Mlm and sem Approaches (Quantitative Methodology Series) (en Inglés)

An Introduction to Multilevel Modeling Techniques: Mlm and sem Approaches (Quantitative Methodology Series) (en Inglés)

Formato

Libro Físico

Editorial

Routledge

Autor

Ronald H. Heck; Scott L. Thomas

Año

2020

Idioma

Inglés

N° páginas

404

Encuadernación

Tapa Dura

ISBN13

9780367182427

N° edición

Categorías

Econometría

An Introduction to Multilevel Modeling Techniques: Mlm and sem Approaches (Quantitative Methodology Series) (en Inglés)

Ronald H. Heck; Scott L. Thomas (Autor) · Routledge · Tapa Dura

Libro Nuevo Importado

Envío: 23 a 30 días háb.

$ 339.51$ 169.76

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 44 unidades

$ 169.76

Llega entre el 23 Sep y el 06 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "An Introduction to Multilevel Modeling Techniques: Mlm and sem Approaches (Quantitative Methodology Series) (en Inglés)"

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.