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 performance of model fit measure...

Zhao, Yu.

FindBook

Google Book

Amazon

博客來

The performance of model fit measures by robust weighted least squares estimators in confirmatory factor analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The performance of model fit measures by robust weighted least squares estimators in confirmatory factor analysis./
作者:	Zhao, Yu.
面頁冊數:	307 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.
Contained By:	Dissertation Abstracts International76-12A(E).
標題:	Educational tests & measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3715582
ISBN:	9781321938166

The performance of model fit measures by robust weighted least squares estimators in confirmatory factor analysis.
Zhao, Yu.

The performance of model fit measures by robust weighted least squares estimators in confirmatory factor analysis. - 307 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2015.

Despite the prevalence of ordinal observed variables in applied structural equation modeling (SEM) research, limited attention has been given to model evaluation methods suitable for ordinal variables, thus providing practitioners in the field with few guidelines to follow. This dissertation represents a first attempt to thoroughly examine the performance of five fit measures ---chi 2 statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR)---produced by the mean- and variance-corrected Weighted Least Squares (WLSMV) estimator from Mplus 7 and the Diagonally Weighted Least Squares (DWLS) estimator from LISREL 9.1, both of which are forms of Robust Weighted Least Squares (RWLS) estimator designed to accommodate ordinal and nonnormal observed variables, in Confirmatory Factor Analysis (CFA) model evaluation, under various realistic sample, data, and model conditions, especially when different types and degrees of model misspecification occur. This study also empirically examined the applicability of the most widely used cut-off criteria of the fit indices proposed by Hu and Bentler (1999) in RWLS estimation with ordinal variables.

ISBN: 9781321938166Subjects--Topical Terms:

3168483
Educational tests & measurements.

The performance of model fit measures by robust weighted least squares estimators in confirmatory factor analysis.
LDR:03534nmm a2200301 4500 001 2076391
005 20161028121120.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781321938166
035 $a (MiAaPQ)AAI3715582
035 $a AAI3715582
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhao, Yu. $3 1277443
245 1 4 $a The performance of model fit measures by robust weighted least squares estimators in confirmatory factor analysis.
300 $a 307 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.
500 $a Adviser: Pui-Wa Lei.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2015.
520 $a Despite the prevalence of ordinal observed variables in applied structural equation modeling (SEM) research, limited attention has been given to model evaluation methods suitable for ordinal variables, thus providing practitioners in the field with few guidelines to follow. This dissertation represents a first attempt to thoroughly examine the performance of five fit measures ---chi 2 statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR)---produced by the mean- and variance-corrected Weighted Least Squares (WLSMV) estimator from Mplus 7 and the Diagonally Weighted Least Squares (DWLS) estimator from LISREL 9.1, both of which are forms of Robust Weighted Least Squares (RWLS) estimator designed to accommodate ordinal and nonnormal observed variables, in Confirmatory Factor Analysis (CFA) model evaluation, under various realistic sample, data, and model conditions, especially when different types and degrees of model misspecification occur. This study also empirically examined the applicability of the most widely used cut-off criteria of the fit indices proposed by Hu and Bentler (1999) in RWLS estimation with ordinal variables.
520 $a Results showed that in evaluating the goodness-of-fit of CFA models with ordinal variables, fit measures generated by Mplus WLSMV seemed to be more effective and reliable than those produced by LISREL DWLS across studied conditions. The WLSMV fit measures generally maintained good Type I error control and were powerful enough to detect moderate model misspecification, provided that the model was not too large. The DWLS fit measures, on the other hand, were susceptible to influences of small sample size and could be largely inflated or deflated when a small sample was used to evaluate a large model. In addition, Hu and Bentler's (1999) cut-off criteria, despite of their popularity among applied SEM researchers, were not universally applicable in RWLS model evaluation, mainly because all of the fit indices examined varied systematically with the size of the proposed model. Recommendations are made by the end of the dissertation, based on the results of the current study, on practical issues pertaining to reallife CFA model evaluation with ordinal observed variables, such as minimum sample size required and how to use information provided by the RWLS fit measures to make model-data fit decisions, while taking into consideration the sample, data, and model characteristics specific to researchers' own studies.
590 $a School code: 0176.
650 4 $a Educational tests & measurements. $3 3168483
650 4 $a Educational psychology. $3 517650
650 4 $a Statistics. $3 517247
690 $a 0288
690 $a 0525
690 $a 0463
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertation Abstracts International $g 76-12A(E).
790 $a 0176
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3715582