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Tomek, Sara; Robinson, Cecil – Measurement: Interdisciplinary Research and Perspectives, 2021
Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to…
Descriptors: Growth Models, Statistical Analysis, Hierarchical Linear Modeling, Computation
Bentler, Peter M. – Measurement: Interdisciplinary Research and Perspectives, 2016
The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…
Descriptors: Causal Models, Factor Analysis, Prediction, Scores
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
Rhemtulla, Mijke; van Bork, Riet; Borsboom, Denny – Measurement: Interdisciplinary Research and Perspectives, 2015
In this commentary, Mijke Rhemtulla, Riet van Bork, and Denny Borsboom write that they were delighted to see Bainter and Bollen's paper as a focus article in "Measurement." In their view, psychological researchers who use SEM rely too reflexively on reflective measurement, without sufficiently considering whether their indicators are…
Descriptors: Causal Models, Measurement, Data Interpretation, Statistical Data
Bainter, Sierra A.; Bollen, Kenneth A. – Measurement: Interdisciplinary Research and Perspectives, 2014
In measurement theory, causal indicators are controversial and little understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning…
Descriptors: Measurement, Statistical Analysis, Data Interpretation, Causal Models
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
Howell, Roy D. – Measurement: Interdisciplinary Research and Perspectives, 2014
Building on the work of Bollen (2007) and Bollen & Bauldry (2011), Bainter and Bollen (this issue) clarifies several points of confusion in the literature regarding causal indicator models. This author would certainly agree that the effect indicator (reflective) measurement model is inappropriate for some indicators (such as the social…
Descriptors: Statistical Analysis, Measurement, Causal Models, Data Interpretation
Thissen, David – Measurement: Interdisciplinary Research and Perspectives, 2015
In "Using Learning Progressions to Design Vertical Scales that Support Coherent Inferences about Student Growth" (hereafter ULR), Briggs and Peck suggest that learning progressions could be used as the basis of vertical scales with naturally benchmarked descriptions of student proficiency. They propose and provide a single example of a…
Descriptors: Academic Achievement, Achievement Gains, Achievement Rating, Psychometrics
Martineau, Joseph A.; Wyse, Adam E. – Measurement: Interdisciplinary Research and Perspectives, 2015
This article is a commentary of a paper by Derek C. Briggs and Frederick A. Peck, "Using Learning Progressions to Design Vertical Scales That Support Coherent Inferences about Student Growth," which describes an elegant potential framework for at least beginning to address three priorities in large-scale assessment that have not been…
Descriptors: Performance Factors, Barriers, Program Implementation, Group Testing
Hamilton, Laura S. – Measurement: Interdisciplinary Research and Perspectives, 2011
Cynthia Coburn and Erica Turner have made an important contribution by developing a framework to synthesize the various strands of research and theory related to data use in schools. The framework illustrates the complexity of the pathways between the adoption of a data-use intervention and the attainment of desired outcomes, and it clarifies the…
Descriptors: Learner Engagement, Learning Activities, Educational Environment, Educational Experience
Rose, L. Todd; Fischer, Kurt W. – Measurement: Interdisciplinary Research and Perspectives, 2011
The focus article by Coburn and Turner (this issue) seeks to provide a comprehensive framework for understanding data use in the context of data-use interventions. This commentary focuses on what the authors see as a glaring omission in what is otherwise a valuable framework: the issue of "useful data." It is their contention that the usefulness…
Descriptors: Decision Making, Data, Data Analysis, Data Interpretation
Coburn, Cynthia E.; Turner, Erica O. – Measurement: Interdisciplinary Research and Perspectives, 2011
One of the central lessons from research on data use in schools and school districts is that assessments, student tests, and other forms of data are only as good as how they are used. But what influences how they are used? This relatively straightforward question turns out to be fairly complex to answer. Data use implicates a number of processes,…
Descriptors: Data, Information Utilization, Public Schools, School Districts