Research ExplorerPublicationsClass Evolution Tree: a graphical tool to support decisions ...

Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research.

Standard

Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research. / Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa.

In: INT J REHABIL RES, Vol. 34, No. 2, 2, 2011, p. 181-185.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Kriston, L, Melchior, H, Hergert, A, Bergelt, C, Watzke, B, Schulz, H & von Wolff, A 2011, 'Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research.', INT J REHABIL RES, vol. 34, no. 2, 2, pp. 181-185. <http://www.ncbi.nlm.nih.gov/pubmed/21467944?dopt=Citation>

APA

Kriston, L., Melchior, H., Hergert, A., Bergelt, C., Watzke, B., Schulz, H., & von Wolff, A. (2011). Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research. INT J REHABIL RES, 34(2), 181-185. [2]. http://www.ncbi.nlm.nih.gov/pubmed/21467944?dopt=Citation

Vancouver

Kriston L, Melchior H, Hergert A, Bergelt C, Watzke B, Schulz H et al. Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research. INT J REHABIL RES. 2011;34(2):181-185. 2.

Bibtex

@article{f2dbfdf1c690419aaadd162c09943166,
title = "Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research.",
abstract = "The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was carried out in patients with cancer receiving an inpatient rehabilitation program to identify prototypical combinations of treatment elements. In the second study, growth mixture modeling was used to identify latent trajectory classes based on weekly symptom severity measurements during inpatient treatment of patients with mental disorders. A graphical tool, the Class Evolution Tree, was developed, and its central components were described. The Class Evolution Tree can be used in addition to statistical criteria to systematically address the issue of number of classes in explorative categorical latent variable modeling.",
keywords = "Germany, Humans, Cross-Sectional Studies, Longitudinal Studies, Combined Modality Therapy, Cooperative Behavior, Patient Care Team, Interdisciplinary Communication, *Decision Support Techniques, Computer Graphics, *Decision Trees, Health Services Research/statistics & numerical data, Mental Disorders/*rehabilitation, *Models, Statistical, Neoplasms/*rehabilitation, Outcome and Process Assessment (Health Care)/statistics & numerical data, *Patient Admission, *Rehabilitation, Rehabilitation Centers, Research/*statistics & numerical data, Germany, Humans, Cross-Sectional Studies, Longitudinal Studies, Combined Modality Therapy, Cooperative Behavior, Patient Care Team, Interdisciplinary Communication, *Decision Support Techniques, Computer Graphics, *Decision Trees, Health Services Research/statistics & numerical data, Mental Disorders/*rehabilitation, *Models, Statistical, Neoplasms/*rehabilitation, Outcome and Process Assessment (Health Care)/statistics & numerical data, *Patient Admission, *Rehabilitation, Rehabilitation Centers, Research/*statistics & numerical data",
author = "Levente Kriston and Hanne Melchior and Anika Hergert and Corinna Bergelt and Birgit Watzke and Holger Schulz and {von Wolff}, Alessa",
year = "2011",
language = "English",
volume = "34",
pages = "181--185",
journal = "INT J REHABIL RES",
issn = "0342-5282",
publisher = "Lippincott Williams and Wilkins",
number = "2",

}

RIS

TY - JOUR

T1 - Class Evolution Tree: a graphical tool to support decisions on the number of classes in exploratory categorical latent variable modeling for rehabilitation research.

AU - Kriston, Levente

AU - Melchior, Hanne

AU - Hergert, Anika

AU - Bergelt, Corinna

AU - Watzke, Birgit

AU - Schulz, Holger

AU - von Wolff, Alessa

PY - 2011

Y1 - 2011

N2 - The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was carried out in patients with cancer receiving an inpatient rehabilitation program to identify prototypical combinations of treatment elements. In the second study, growth mixture modeling was used to identify latent trajectory classes based on weekly symptom severity measurements during inpatient treatment of patients with mental disorders. A graphical tool, the Class Evolution Tree, was developed, and its central components were described. The Class Evolution Tree can be used in addition to statistical criteria to systematically address the issue of number of classes in explorative categorical latent variable modeling.

AB - The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was carried out in patients with cancer receiving an inpatient rehabilitation program to identify prototypical combinations of treatment elements. In the second study, growth mixture modeling was used to identify latent trajectory classes based on weekly symptom severity measurements during inpatient treatment of patients with mental disorders. A graphical tool, the Class Evolution Tree, was developed, and its central components were described. The Class Evolution Tree can be used in addition to statistical criteria to systematically address the issue of number of classes in explorative categorical latent variable modeling.

KW - Germany

KW - Humans

KW - Cross-Sectional Studies

KW - Longitudinal Studies

KW - Combined Modality Therapy

KW - Cooperative Behavior

KW - Patient Care Team

KW - Interdisciplinary Communication

KW - Decision Support Techniques

KW - Computer Graphics

KW - Decision Trees

KW - Health Services Research/statistics & numerical data

KW - Mental Disorders/rehabilitation

KW - Models, Statistical

KW - Neoplasms/rehabilitation

KW - Outcome and Process Assessment (Health Care)/statistics & numerical data

KW - Patient Admission

KW - Rehabilitation

KW - Rehabilitation Centers

KW - Research/statistics & numerical data

KW - Germany

KW - Humans

KW - Cross-Sectional Studies

KW - Longitudinal Studies

KW - Combined Modality Therapy

KW - Cooperative Behavior

KW - Patient Care Team

KW - Interdisciplinary Communication

KW - Decision Support Techniques

KW - Computer Graphics

KW - Decision Trees

KW - Health Services Research/statistics & numerical data

KW - Mental Disorders/rehabilitation

KW - Models, Statistical

KW - Neoplasms/rehabilitation

KW - Outcome and Process Assessment (Health Care)/statistics & numerical data

KW - Patient Admission

KW - Rehabilitation

KW - Rehabilitation Centers

KW - Research/statistics & numerical data

M3 - SCORING: Journal article

VL - 34

SP - 181

EP - 185

JO - INT J REHABIL RES

JF - INT J REHABIL RES

SN - 0342-5282

IS - 2

M1 - 2

ER -