東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A comparison of classical methods an...

Shelton, Erin Dominique.

Linked to FindBook

Google Book

Amazon

博客來

A comparison of classical methods and second order latent growth models for longitudinal data analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A comparison of classical methods and second order latent growth models for longitudinal data analysis./
Author:	Shelton, Erin Dominique.
Description:	78 p.
Notes:	Source: Masters Abstracts International, Volume: 49-01, page: 0708.
Contained By:	Masters Abstracts International49-01.
Subject:	Psychology, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1479948
ISBN:	9781124162102

A comparison of classical methods and second order latent growth models for longitudinal data analysis.
Shelton, Erin Dominique.

A comparison of classical methods and second order latent growth models for longitudinal data analysis. - 78 p.

Source: Masters Abstracts International, Volume: 49-01, page: 0708.

Thesis (M.A.)--University of Southern California, 2010.

In 1988, McArdle identified issues modeling multivariate growth using what he termed "second order latent growth curve" models. Specifically, he raised questions about which type of structural equation model to use for longitudinal data analysis. For example, "Should the growth model be fit to common factors extracted from the measured variables (CUFFS)?"; or "Should a growth model be fit to the measured variables and then the intercepts and slopes of the common factors be considered (FOCUS)?" Both SEMs can be fit to the same set of data, however, as pointed ou in McArdle (1988), these two alternative models not strictly nested and therefore statistical comparison becomes more difficult. Most recent works have utilized only the CUFFS model (Ferrer, Balluerka, & Widaman, 2008; Grimm, Pianta, & Konold, 2009; Hancock, Kuo, & Lawrence, 2001; Lui, & Flay, 2009), however, because of the close relationship of the FOUCS models to the newer dynamic multivariate modeling issues (e.g., McArdle, 2008), it is an option worth further consideration.

ISBN: 9781124162102Subjects--Topical Terms:

1018034
Psychology, General.

A comparison of classical methods and second order latent growth models for longitudinal data analysis.
LDR:02925nam 2200313 4500 001 1402551
005 20111102140032.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124162102
035 $a (UMI)AAI1479948
035 $a AAI1479948
040 $a UMI $c UMI
100 1 $a Shelton, Erin Dominique. $3 1681747
245 1 2 $a A comparison of classical methods and second order latent growth models for longitudinal data analysis.
300 $a 78 p.
500 $a Source: Masters Abstracts International, Volume: 49-01, page: 0708.
500 $a Adviser: John J. McArdle.
502 $a Thesis (M.A.)--University of Southern California, 2010.
520 $a In 1988, McArdle identified issues modeling multivariate growth using what he termed "second order latent growth curve" models. Specifically, he raised questions about which type of structural equation model to use for longitudinal data analysis. For example, "Should the growth model be fit to common factors extracted from the measured variables (CUFFS)?"; or "Should a growth model be fit to the measured variables and then the intercepts and slopes of the common factors be considered (FOCUS)?" Both SEMs can be fit to the same set of data, however, as pointed ou in McArdle (1988), these two alternative models not strictly nested and therefore statistical comparison becomes more difficult. Most recent works have utilized only the CUFFS model (Ferrer, Balluerka, & Widaman, 2008; Grimm, Pianta, & Konold, 2009; Hancock, Kuo, & Lawrence, 2001; Lui, & Flay, 2009), however, because of the close relationship of the FOUCS models to the newer dynamic multivariate modeling issues (e.g., McArdle, 2008), it is an option worth further consideration.
520 $a In the examples presented by McArdle (1988), data from the Weschler Intelligence Scale for Children (WISC) were used. The longitudinal WISC dataset was completed in 1965 by R. T. Osborne (Osborne & Suddick, 1972). Children were measured on 11 subscales of the Weschler Intelligence Scale for Children (WISC). The selected sample consists of 204 children who were repeatedly measured under standardized conditions at approximate ages of 6, 7, 9, and 11. In this research, the same data and models are used but more focus is placed on the appropriate choice of SEM. Classic and contemporary techniques for analyzing repeated measures date are compared. The focus was on the specification of second order LGMs and the advantages of these methods over the more classic MANOVA and first order LGM techniques. The key results and their implications are discussed.
590 $a School code: 0208.
650 4 $a Psychology, General. $3 1018034
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0621
690 $a 0632
710 2 $a University of Southern California. $b Psychology. $3 1273598
773 0 $t Masters Abstracts International $g 49-01.
790 1 0 $a McArdle, John J., $e advisor
790 1 0 $a John, Richard S. $e committee member
790 1 0 $a Wilcox, Rand R. $e committee member
790 $a 0208
791 $a M.A.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1479948