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LDR | 03546nam 2200493 4500 | ||
001 | 0100803711▲ | ||
005 | 20240329141809▲ | ||
006 | m o d ▲ | ||
007 | cr#unu||||||||▲ | ||
008 | 240116s2023 us |||||||||||||||c||eng d▲ | ||
020 | ▼a9798380111591▲ | ||
035 | ▼a(MiAaPQ)AAI30522294▲ | ||
040 | ▼aMiAaPQ▼cMiAaPQ▲ | ||
082 | 0 | ▼a151▲ | |
100 | 1 | ▼aSavord, Andrea.▲ | |
245 | 1 | 0 | ▼aEvaluation of Univariate and Multivariate Dynamic Structural Equation Models With Categorical Outcomes▼h[electronic resource]▲ |
260 | ▼a[S.l.]: ▼bArizona State University. ▼c2023▲ | ||
260 | 1 | ▼aAnn Arbor : ▼bProQuest Dissertations & Theses, ▼c2023▲ | |
300 | ▼a1 online resource(269 p.)▲ | ||
500 | ▼aSource: Dissertations Abstracts International, Volume: 85-02, Section: B.▲ | ||
500 | ▼aAdvisor: McNeish, Daniel.▲ | ||
502 | 1 | ▼aThesis (Ph.D.)--Arizona State University, 2023.▲ | |
506 | ▼aThis item must not be sold to any third party vendors.▲ | ||
520 | ▼aThe proliferation of intensive longitudinal datasets has necessitated the development of analytical techniques that are flexible and accessible to researchers collecting dyadic or individual data. Dynamic structural equation models (DSEMs), as implemented in Mplus, provides the flexibility researchers require by combining components from multilevel modeling, structural equation modeling, and time series analyses. This dissertation project presents a simulation study that evaluates the performance of categorical DSEM using a probit link function across different numbers of clusters (N = 50 or 200), timepoints (T = 14, 28, or 56), categories on the outcome (2, 3, or 5), and distribution of responses on the outcome (symmetric/approximate normal, skewed, or uniform) for both univariate and multivariate models (representing individual data and dyadic longitudinal Actor-Partner Interdependence Model data, respectively). The 3- and 5-category model conditions were also evaluated as continuous DSEMs across the same cluster, timepoint, and distribution conditions to evaluate to what extent ignoring the categorical nature of the outcome impacted model performance. Results indicated that previously-suggested minimums for number of clusters and timepoints from studies evaluating continuous DSEM performance with continuous outcomes are not large enough to produce unbiased and adequately powered models in categorical DSEM. The distribution of responses on the outcome did not have a noticeable impact in model performance for categorical DSEM, but did affect model performance when fitting a continuous DSEM to the same datasets. Ignoring the categorical nature of the outcome lead to underestimated effects across parameters and conditions, and showed large Type-I error rates in the N = 200 cluster conditions.▲ | ||
590 | ▼aSchool code: 0010.▲ | ||
650 | 4 | ▼aQuantitative psychology.▲ | |
650 | 4 | ▼aPsychology.▲ | |
650 | 4 | ▼aBehavioral psychology.▲ | |
653 | ▼aCategorical outcomes▲ | ||
653 | ▼aDyadic data▲ | ||
653 | ▼aDynamic structural equation models▲ | ||
653 | ▼aSimulation study▲ | ||
690 | ▼a0632▲ | ||
690 | ▼a0621▲ | ||
690 | ▼a0384▲ | ||
710 | 2 | 0 | ▼aArizona State University.▼bPsychology.▲ |
773 | 0 | ▼tDissertations Abstracts International▼g85-02B.▲ | |
773 | ▼tDissertation Abstract International▲ | ||
790 | ▼a0010▲ | ||
791 | ▼aPh.D.▲ | ||
792 | ▼a2023▲ | ||
793 | ▼aEnglish▲ | ||
856 | 4 | 0 | ▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932959▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.▲ |
Evaluation of Univariate and Multivariate Dynamic Structural Equation Models With Categorical Outcomes[electronic resource]
Document Type
국외eBook
Title
Evaluation of Univariate and Multivariate Dynamic Structural Equation Models With Categorical Outcomes [electronic resource]
Author
Corporate Name
Publication
[S.l.] : Arizona State University. 2023 Ann Arbor : ProQuest Dissertations & Theses , 2023
Physical Description
1 online resource(269 p.)
General Note
Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Advisor: McNeish, Daniel.
Advisor: McNeish, Daniel.
Dissertation Note
Thesis (Ph.D.)--Arizona State University, 2023.
Summary Note
The proliferation of intensive longitudinal datasets has necessitated the development of analytical techniques that are flexible and accessible to researchers collecting dyadic or individual data. Dynamic structural equation models (DSEMs), as implemented in Mplus, provides the flexibility researchers require by combining components from multilevel modeling, structural equation modeling, and time series analyses. This dissertation project presents a simulation study that evaluates the performance of categorical DSEM using a probit link function across different numbers of clusters (N = 50 or 200), timepoints (T = 14, 28, or 56), categories on the outcome (2, 3, or 5), and distribution of responses on the outcome (symmetric/approximate normal, skewed, or uniform) for both univariate and multivariate models (representing individual data and dyadic longitudinal Actor-Partner Interdependence Model data, respectively). The 3- and 5-category model conditions were also evaluated as continuous DSEMs across the same cluster, timepoint, and distribution conditions to evaluate to what extent ignoring the categorical nature of the outcome impacted model performance. Results indicated that previously-suggested minimums for number of clusters and timepoints from studies evaluating continuous DSEM performance with continuous outcomes are not large enough to produce unbiased and adequately powered models in categorical DSEM. The distribution of responses on the outcome did not have a noticeable impact in model performance for categorical DSEM, but did affect model performance when fitting a continuous DSEM to the same datasets. Ignoring the categorical nature of the outcome lead to underestimated effects across parameters and conditions, and showed large Type-I error rates in the N = 200 cluster conditions.
Subject
ISBN
9798380111591
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