1.

Record Nr.

UNINA9910823157003321

Titolo

Applied quantitative analysis in education and the social sciences / / edited by Yaacov Petscher, Christopher Schatschneider, Donald L. Compton

Pubbl/distr/stampa

New York : , : Routledge, , 2013

ISBN

1-136-26632-1

0-203-10855-8

1-299-27868-X

1-136-26633-X

Descrizione fisica

1 online resource (389 p.)

Altri autori (Persone)

ComptonDonald L. <1960->

PetscherYaacov M

SchatschneiderChristopher

Disciplina

519.5/36

Soggetti

Regression analysis

Mathematical statistics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Extending conditional means modeling: an introduction to quantile regression / Yaacov Petscher, Jessica A.R. Logan, and Chengfu Zhou -- Using dominance analysis to estimate predictor importance in multiple regression / Razia Azen -- I am ROC curves (and so can you)! / Christopher Schatschneider -- Multilevel modeling: practical examples to illustrate a special case of SEM / Lee Branum-Martin -- Linear and quadratic growth models for continuous and dichotomous outcomes / Ann A. O'Connell, Jessica A. R. Logan, Jill Pentimonti, and D. Betsy McCoach -- Exploratory and confirmatory factor analysis / Rex Kline -- Factor analysis with categorical indicators: demonstration of item response theory / R.J. de Ayala -- Introduction to structural equation modeling / Richard Lomax -- Latent growth curve modeling using structural equation modeling / Ryan Bowles and Janelle J. Montroy -- Latent class/profile analysis / Karen Samuelsen and Katherine Raczynski -- n-level structural equation modeling / Paras Mehta.

Sommario/riassunto

To say that complex data analyses are ubiquitous in the education and



social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model