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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
Enders, Craig K. – Guilford Press, 2010
Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Maximum Likelihood Statistics
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Eid, Michael; Nussbeck, Fridtjof W.; Geiser, Christian; Cole, David A.; Gollwitzer, Mario; Lischetzke, Tanja – Psychological Methods, 2008
The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Statistical Analysis, Error of Measurement
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Finch, Holmes; Monahan, Patrick – Applied Measurement in Education, 2008
This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…
Descriptors: Monte Carlo Methods, Factor Analysis, Generalization, Methods
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Kanari, Zoe; Millar, Robin – Journal of Research in Science Teaching, 2004
This study explored the understandings of data and measurement that school students draw upon, and the ways that they reason from data, when carrying out a practical science inquiry task. The two practical tasks used in the study each involved investigations of the relationships between two independent variables (IVs) and a dependent variable…
Descriptors: Science Education, Investigations, Error of Measurement, Mathematics Achievement
Searls, Donald T., Ed. – 1983
The purpose of this paper is to provide an overview of the analysis of data collected by the National Assessment of Educational Progress (NAEP). In simplest terms, the analysis can be characterized as establishing baseline estimates of the percentages of young Americans possessing certain skills, knowledge, understandings, and attitudes and…
Descriptors: Data Analysis, Data Collection, Databases, Educational Assessment
Carlson, James E.; Spray, Judith A. – 1986
This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…
Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement
Altonji, Joseph G. – 1990
This paper examines whether failure to control for family background, aptitude, high school quality, high school curriculum, and community characteristics leads to bias in estimates of the economic return resulting from postsecondary education. High school specific fixed effects were used to control for all observed and unobserved characteristics…
Descriptors: College Bound Students, Community Characteristics, Data Analysis, Educational Benefits