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 behaviors of robust weighted lea...

Navruz, Bilgin.

FindBook

Google Book

Amazon

博客來

The behaviors of robust weighted least squares estimation techniques for categorical/ordinal data in multilevel CFA models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The behaviors of robust weighted least squares estimation techniques for categorical/ordinal data in multilevel CFA models./
作者:	Navruz, Bilgin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	269 p.
附註:	Source: Dissertations Abstracts International, Volume: 78-06, Section: A.
Contained By:	Dissertations Abstracts International78-06A.
標題:	Educational tests & measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10291008
ISBN:	9781369334128

The behaviors of robust weighted least squares estimation techniques for categorical/ordinal data in multilevel CFA models.
Navruz, Bilgin.

The behaviors of robust weighted least squares estimation techniques for categorical/ordinal data in multilevel CFA models. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 269 p.

Source: Dissertations Abstracts International, Volume: 78-06, Section: A.

Thesis (Ph.D.)--Texas A&M University, 2016.

This item is not available from ProQuest Dissertations & Theses.

This present Monte Carlo study was conducted to investigate the behaviors of weighted least square mean (WLSM) and weighted least square mean and variance (WLSMV) estimation techniques with categorical/ordinal data in multilevel confirmatory factor analysis (CFA) models under various conditions. Specifically, the study focused on how well WLSM and WLSMV estimation techniques work for multilevel CFA models with categorical/ordinal data by examining parameter estimates, standard error estimates of the parameters, and some of the fit indices. Also, the performance of commonly used fit indices for misspecified two-level CFA models for categorical/ordinal variables with the WLSM and WLSMV estimations was examined using traditional cutoff values for the fit indices. Simulation results showed that WLSM and WLSMV estimated biased factor pattern coefficients and the factor correlation in the between-level regardless of the simulation design factors. In the within-level model, the factor pattern coefficients and correlation were unbiased when a large number of cluster size (CS) and a large number of clusters (NC) were used; but these parameters were biased when a small NC and CS were used. Standard error estimates of the parameters in both within- and between-levels were biased regardless of the design conditions, so parameters should not be evaluated based on statistical significance test results even when these parameter estimates are unbiased. The chi-square overall model test statistics were poorly estimated. WLSM or WLSMV based overall model chi-square test statistics did not follow the traditional chisquare test distribution. Because of that, lower power rates were obtained when factor pattern coefficients were misspecified in the between- or within-level models. Generally, CFI, TLI, RMSEA, SRMR for the between-level model (SRMR-B), and SRMR for the within-level model (SRMR-W) performed poorly for detecting misspecifications. As a final remark, it is better not to use robust WLSM or WLSMV estimation techniques in the multilevel CFA models when clustered categorical/ordinal data are present.

ISBN: 9781369334128Subjects--Topical Terms:

3168483
Educational tests & measurements.
Subjects--Index Terms:

Categorical/ordinal data

The behaviors of robust weighted least squares estimation techniques for categorical/ordinal data in multilevel CFA models.
LDR:03646nmm a2200433 4500 001 2274495
005 20201202130008.5
008 220629s2016 ||||||||||||||||| ||eng d
020 $a 9781369334128
035 $a (MiAaPQ)AAI10291008
035 $a (MiAaPQ)0803vireo:17415Navruz
035 $a AAI10291008
040 $a MiAaPQ $c MiAaPQ
100 1 $a Navruz, Bilgin. $3 3551987
245 1 4 $a The behaviors of robust weighted least squares estimation techniques for categorical/ordinal data in multilevel CFA models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 269 p.
500 $a Source: Dissertations Abstracts International, Volume: 78-06, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Thompson, Bruce.
502 $a Thesis (Ph.D.)--Texas A&M University, 2016.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a This present Monte Carlo study was conducted to investigate the behaviors of weighted least square mean (WLSM) and weighted least square mean and variance (WLSMV) estimation techniques with categorical/ordinal data in multilevel confirmatory factor analysis (CFA) models under various conditions. Specifically, the study focused on how well WLSM and WLSMV estimation techniques work for multilevel CFA models with categorical/ordinal data by examining parameter estimates, standard error estimates of the parameters, and some of the fit indices. Also, the performance of commonly used fit indices for misspecified two-level CFA models for categorical/ordinal variables with the WLSM and WLSMV estimations was examined using traditional cutoff values for the fit indices. Simulation results showed that WLSM and WLSMV estimated biased factor pattern coefficients and the factor correlation in the between-level regardless of the simulation design factors. In the within-level model, the factor pattern coefficients and correlation were unbiased when a large number of cluster size (CS) and a large number of clusters (NC) were used; but these parameters were biased when a small NC and CS were used. Standard error estimates of the parameters in both within- and between-levels were biased regardless of the design conditions, so parameters should not be evaluated based on statistical significance test results even when these parameter estimates are unbiased. The chi-square overall model test statistics were poorly estimated. WLSM or WLSMV based overall model chi-square test statistics did not follow the traditional chisquare test distribution. Because of that, lower power rates were obtained when factor pattern coefficients were misspecified in the between- or within-level models. Generally, CFI, TLI, RMSEA, SRMR for the between-level model (SRMR-B), and SRMR for the within-level model (SRMR-W) performed poorly for detecting misspecifications. As a final remark, it is better not to use robust WLSM or WLSMV estimation techniques in the multilevel CFA models when clustered categorical/ordinal data are present.
590 $a School code: 0803.
650 4 $a Educational tests & measurements. $3 3168483
650 4 $a Statistics. $3 517247
650 4 $a Educational psychology. $3 517650
653 $a Categorical/ordinal data
653 $a Multilevel CFA
653 $a WLSM
653 $a WLSMV
653 $a Weighted least square mean
653 $a Weighted least square mean and variance
690 $a 0288
690 $a 0463
690 $a 0525
710 2 $a Texas A&M University. $b Educational Psychology. $3 2096773
773 0 $t Dissertations Abstracts International $g 78-06A.
790 $a 0803
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10291008