NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 14 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Philipp Sterner; Florian Pargent; Dominik Deffner; David Goretzko – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…
Descriptors: Structural Equation Models, Causal Models, Measurement, Predictor Variables
Peer reviewed Peer reviewed
Direct linkDirect link
Ruoxuan Li; Lijuan Wang – Grantee Submission, 2024
Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical…
Descriptors: Social Science Research, Causal Models, Formative Evaluation, Measurement
Peer reviewed Peer reviewed
Direct linkDirect link
Hilley, Chanler D.; O'Rourke, Holly P. – International Journal of Behavioral Development, 2022
Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent…
Descriptors: Behavioral Science Research, Research Methodology, Longitudinal Studies, Behavior Change
Peer reviewed Peer reviewed
Direct linkDirect link
Lee, Nick; Chamberlain, Laura – Measurement: Interdisciplinary Research and Perspectives, 2016
Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…
Descriptors: Measurement, Causal Models, Formative Evaluation, Evaluation Methods
Peer reviewed Peer reviewed
Direct linkDirect link
Wang, Jue; Engelhard, George, Jr. – Measurement: Interdisciplinary Research and Perspectives, 2016
The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral…
Descriptors: Structural Equation Models, Measurement, Causal Models, Construct Validity
Peer reviewed Peer reviewed
Direct linkDirect link
Markus, Keith A. – Measurement: Interdisciplinary Research and Perspectives, 2014
In a series of articles and comments, Kenneth Bollen and his collaborators have incrementally refined an account of structural equation models that (a) model a latent variable as the effect of several observed variables and (b) carry an interpretation of the observed variables as, in some sense, measures of the latent variable that they cause.…
Descriptors: Measurement, Structural Equation Models, Statistical Analysis, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
Peterson, Christina Hamme; Gischlar, Karen L.; Peterson, N. Andrew – Journal for Specialists in Group Work, 2017
Measures that accurately capture the phenomenon are critical to research and practice in group work. The vast majority of group-related measures were developed using the reflective measurement model rooted in classical test theory (CTT). Depending on the construct definition and the measure's purpose, the reflective model may not always be the…
Descriptors: Item Response Theory, Group Activities, Test Theory, Test Items
Peer reviewed Peer reviewed
Direct linkDirect link
Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2015
This article is a commentary on the Focus Article, "Interpretational Confounding or Confounded Interpretations of Causal Indicators?" and a commentary that was published in issue 12(4) 2014 of "Measurement: Interdisciplinary Research & Perspectives". The authors challenge two claims: (a) Bainter and Bollen argue that the…
Descriptors: Causal Models, Measurement, Data Interpretation, Structural Equation Models
Peer reviewed Peer reviewed
Direct linkDirect link
Zhao, Yu; Lei, Pui-Wa – AERA Online Paper Repository, 2016
Despite the prevalence of ordinal observed variables in applied structural equation modeling (SEM) research, limited attention has been given to model evaluation methods suitable for ordinal variables, thus providing practitioners in the field with few guidelines to follow. This study represents a first attempt to thoroughly examine the…
Descriptors: Factor Analysis, Monte Carlo Methods, Causal Models, Least Squares Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M. – Measurement: Interdisciplinary Research and Perspectives, 2016
One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…
Descriptors: Causal Models, Measurement, Research Problems, Structural Equation Models
Peer reviewed Peer reviewed
Direct linkDirect link
Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2016
In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…
Descriptors: Causal Models, Structural Equation Models, Formative Evaluation, Measurement
Peer reviewed Peer reviewed
Direct linkDirect link
Wang, Jue; Engelhard, George, Jr.; Lu, Zhenqiu – Measurement: Interdisciplinary Research and Perspectives, 2014
The authors of the focus article in this issue have emphasized the continuing confusion among some researchers regarding various indicators used in structural equation models (SEMs). Their major claim is that causal indicators are not inherently unstable, and even if they are unstable they are at least not more unstable than other types of…
Descriptors: Structural Equation Models, Measurement, Statistical Analysis, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
Landsheer, J. A. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Tetrad IV is a program designed for the specification of causal models. It is specifically designed to search for causal relations, but also offers the possibility to estimate the parameters of a structural equation model. It offers a remarkable graphical user interface, which facilitates building, evaluating, and searching for causal models. The…
Descriptors: Structural Equation Models, Causal Models, Evaluation, Mathematics
Peer reviewed Peer reviewed
Direct linkDirect link
Sovajassatakul, Thanongsak; Jitgarun, Kalayanee; Shinatrakool, Raveewan – Journal of College Teaching & Learning, 2011
The purpose of the study reported on in this paper was to identify and compare instructors' and students' perceptions of Team-Based Learning (TBL). Participants were 270 instructors and 288 fourth year students from the faculties of Industrial Education at six universities in Bangkok. The data were analyzed using factor analysis and structural…
Descriptors: Instructional Design, Industrial Education, Student Attitudes, Structural Equation Models