東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

The effects of measurement non-invar...

The University of North Carolina at Chapel Hill., Psychology.

FindBook

Google Book

Amazon

博客來

The effects of measurement non-invariance on parameter estimation in latent growth models.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The effects of measurement non-invariance on parameter estimation in latent growth models./
作者:	Wirth, Robert J.
面頁冊數:	181 p.
附註:	Adviser: Patrick J. Curran.
Contained By:	Dissertation Abstracts International69-11B.
標題:	Psychology, Developmental. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3331053
ISBN:	9780549881599

The effects of measurement non-invariance on parameter estimation in latent growth models.
Wirth, Robert J.

The effects of measurement non-invariance on parameter estimation in latent growth models. - 181 p.

Adviser: Patrick J. Curran.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2008.

Researchers are increasingly taking advantage of the latent growth modeling framework to evaluate complex behaviors over time. However, many constructs in the social sciences change in definition over the course of development. These changes may include an item's relationship to a construct over time (i.e., measurement non-invariance). Unfortunately, the effect of changing measurement structures on latent growth model (LGM) parameter estimates is not well understood. This paper begins with a brief introduction to LGMs followed by the restrictions placed on the traditional measurement model used within the LGM framework. Following this, an introduction to measurement invariance is provided within the context of longitudinal confirmatory factor analysis (CFA). Methods available to incorporate flexible measurement structures within longitudinal models of growth are introduced. The various roles measurement invariance may play in the study of stability and growth are presented. The results of a Monte-Carlo simulation are provided and generally show that when complete measurement invariance was maintained, both mean and factor score-based methods recovered generating parameter values well. If at least partial lambda invariance was maintained, factor scores based on a single invariant item also provided unbiased estimates of the random effects. This result was not found using mean or factor scores based on constraining all factor loadings (within item) to equality over time. A measurement structure that changes systematically over time led to biased estimates of almost all parameters regardless of which scoring method was used as well as observed non-linear trends over time. This study did find that the use of factor scores as indicators in LGMs led to consistently positively biased fit statistics. Possible sources of this misfit are discussed. This paper concludes with a discussion of future research and the implications of these findings in applied research.

ISBN: 9780549881599Subjects--Topical Terms:

1017557
Psychology, Developmental.

The effects of measurement non-invariance on parameter estimation in latent growth models.
LDR:03114nam 2200325 a 45 001 856548
005 20100709
008 100709s2008 ||||||||||||||||| ||eng d
020 $a 9780549881599
035 $a (UMI)AAI3331053
035 $a AAI3331053
040 $a UMI $c UMI
100 1 $a Wirth, Robert J. $3 1023366
245 1 4 $a The effects of measurement non-invariance on parameter estimation in latent growth models.
300 $a 181 p.
500 $a Adviser: Patrick J. Curran.
500 $a Source: Dissertation Abstracts International, Volume: 69-11, Section: B, page: 7186.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2008.
520 $a Researchers are increasingly taking advantage of the latent growth modeling framework to evaluate complex behaviors over time. However, many constructs in the social sciences change in definition over the course of development. These changes may include an item's relationship to a construct over time (i.e., measurement non-invariance). Unfortunately, the effect of changing measurement structures on latent growth model (LGM) parameter estimates is not well understood. This paper begins with a brief introduction to LGMs followed by the restrictions placed on the traditional measurement model used within the LGM framework. Following this, an introduction to measurement invariance is provided within the context of longitudinal confirmatory factor analysis (CFA). Methods available to incorporate flexible measurement structures within longitudinal models of growth are introduced. The various roles measurement invariance may play in the study of stability and growth are presented. The results of a Monte-Carlo simulation are provided and generally show that when complete measurement invariance was maintained, both mean and factor score-based methods recovered generating parameter values well. If at least partial lambda invariance was maintained, factor scores based on a single invariant item also provided unbiased estimates of the random effects. This result was not found using mean or factor scores based on constraining all factor loadings (within item) to equality over time. A measurement structure that changes systematically over time led to biased estimates of almost all parameters regardless of which scoring method was used as well as observed non-linear trends over time. This study did find that the use of factor scores as indicators in LGMs led to consistently positively biased fit statistics. Possible sources of this misfit are discussed. This paper concludes with a discussion of future research and the implications of these findings in applied research.
590 $a School code: 0153.
650 4 $a Psychology, Developmental. $3 1017557
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0620
690 $a 0632
710 2 $a The University of North Carolina at Chapel Hill. $b Psychology. $3 1017867
773 0 $t Dissertation Abstracts International $g 69-11B.
790 $a 0153
790 1 0 $a Bauer, Daniel J. $e committee member
790 1 0 $a Chow, Sy-Miin $e committee member
790 1 0 $a Curran, Patrick J., $e advisor
790 1 0 $a Jones, Deborah J. $e committee member
790 1 0 $a Thissen, David M. $e committee member
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3331053