東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Mixed-effects location-scale models ...

Walters, Ryan W.

Linked to FindBook

Google Book

Amazon

博客來

Mixed-effects location-scale models for conditionally normally distributed repeated-measures data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mixed-effects location-scale models for conditionally normally distributed repeated-measures data./
Author:	Walters, Ryan W.
Description:	234 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
Contained By:	Dissertation Abstracts International76-12B(E).
Subject:	Quantitative psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3717977
ISBN:	9781321976328

Mixed-effects location-scale models for conditionally normally distributed repeated-measures data.
Walters, Ryan W.

Mixed-effects location-scale models for conditionally normally distributed repeated-measures data. - 234 p.

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

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2015.

Hypotheses about psychological processes are most frequently dedicated to individual mean differences, but individual differences in variability are likely to be important as well. The mixed-effects location-scale model estimates individual differences in both mean level and variability in a single model, and represents an important advance in testing variability-related hypotheses. However, the mixed-effects location-scale model remains relatively novel to empirical scientists as statistical software is often handicapped by more complex models and a paucity of methodological studies exist examining the statistical properties of this model.

ISBN: 9781321976328Subjects--Topical Terms:

2144748
Quantitative psychology.

Mixed-effects location-scale models for conditionally normally distributed repeated-measures data.
LDR:03530nmm a2200325 4500 001 2076393
005 20161028121121.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781321976328
035 $a (MiAaPQ)AAI3717977
035 $a AAI3717977
040 $a MiAaPQ $c MiAaPQ
100 1 $a Walters, Ryan W. $3 3191844
245 1 0 $a Mixed-effects location-scale models for conditionally normally distributed repeated-measures data.
300 $a 234 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
500 $a Advisers: Lesa Hoffman; Jonathan Templin.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2015.
520 $a Hypotheses about psychological processes are most frequently dedicated to individual mean differences, but individual differences in variability are likely to be important as well. The mixed-effects location-scale model estimates individual differences in both mean level and variability in a single model, and represents an important advance in testing variability-related hypotheses. However, the mixed-effects location-scale model remains relatively novel to empirical scientists as statistical software is often handicapped by more complex models and a paucity of methodological studies exist examining the statistical properties of this model.
520 $a This dissertation investigates the mixed-effects location-scale model through the development of open-source software for its estimation and through simulation and empirical studies. First, the theoretical framework for the mixed-effects location-scale model is presented followed by a description of the Metropolis-Hastings algorithm developed to estimate this model. Then, two simulation studies are presented evaluating the power to detect and predict individual differences in variability as well as identify the consequences of model misspecification. Finally, results of an empirical analysis examining individual differences in mean level and variability of unstructured movements from a sample of older adults with and without probable mild Alzheimer's disease is presented.
520 $a Results of the power investigation simulation study indicated that the power to detect the scale-model random intercept variance and the effect of an individual-level predictor of residual variability increased with greater numbers of individuals and occasions, and that failing to detect the scale-model random intercept variance essentially precluded the detection of systematically varying fixed effects for an individual-level predictor of residual heterogeneity. Results of the misspecification simulation study indicated that misspecifying the location model and/or scale model for the residual variance had consequences only for fixed and random effects on the same side of the model. Finally, results of the empirical data analysis indicated individuals with probable mild Alzheimer's disease averaged less movement compared to healthy individuals, but did not differ in the variability of their unstructured movements.
520 $a In sum, this dissertation provides information useful to empirical scientists as they progress from study design through analysis, interpretation, and reporting for publication.
590 $a School code: 0138.
650 4 $a Quantitative psychology. $3 2144748
650 4 $a Statistics. $3 517247
650 4 $a Psychology. $3 519075
690 $a 0632
690 $a 0463
690 $a 0621
710 2 $a The University of Nebraska - Lincoln. $b Psychology. $3 1020482
773 0 $t Dissertation Abstracts International $g 76-12B(E).
790 $a 0138
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3717977