Back to Search
Start Over
Developing and Applying IR-Tree Models: Guidelines, Caveats, and an Extension to Multiple Groups
- Source :
- Organizational Research Methods
- Publication Year :
- 2020
- Publisher :
- SAGE Publications, 2020.
-
Abstract
- IR-tree models assume that categorical item responses can best be explained by multiple response processes. In the present article, guidelines are provided for the development and interpretation of IR-tree models. In more detail, the relationship between a tree diagram, the model equations, and the analysis on the basis of pseudo-items is described. Moreover, it is shown that IR-tree models do not allow conclusions about the sequential order of the processes, and that mistakes in the model specification can have serious consequences. Furthermore, multiple-group IR-tree models are presented as a novel extension of IR-tree models to data from heterogeneous units. This allows, for example, to investigate differences across countries or organizations with respect to core parameters of the IR-tree model. Finally, an empirical example on organizational commitment and response styles is presented.
- Subjects :
- Computer science
Strategy and Management
General Decision Sciences
Umfrageforschung
Organizational commitment
Antwortverhalten
Modell
computer.software_genre
Interpretation (model theory)
0504 sociology
survey research
Management of Technology and Innovation
0502 economics and business
Work Orientations IV - ISSP 2015 (ZA6770) [item response theory (IRT)
IR-tree model
response style
organizational commitment
multiple-group model
International Social Survey Programme]
survey
response behavior
Social sciences, sociology, anthropology
Categorical variable
Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Sozialwissenschaften, Soziologie
model
ISSP
business.industry
05 social sciences
050401 social sciences methods
Befragung
Extension (predicate logic)
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Tree (data structure)
Multiple response
ddc:300
Artificial intelligence
business
computer
050203 business & management
Natural language processing
Subjects
Details
- ISSN :
- 15527425 and 10944281
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- Organizational Research Methods
- Accession number :
- edsair.doi.dedup.....cc9ebe10fa4d08515380d62474550fa8
- Full Text :
- https://doi.org/10.1177/1094428120911096