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Jeffrey Matayoshi; Shamya Karumbaiah – Journal of Educational Data Mining, 2024
Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have…
Descriptors: Models, Statistical Bias, Data Analysis, Simulation
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Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria – Psychological Methods, 2012
A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…
Descriptors: Factor Analysis, Computation, Simulation, Sample Size
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Teasley, C.E. Wynn; Hornyak, Martin – American Journal of Business Education, 2010
The 2009 college football season is here, but there has been a continuing controversy swirling over how the Football Bowl Subdivision (FBS) selects its national champion. College football uses a multi-criterion decision matrix (MCDM) evaluation technique to determine which two teams will play for the national championship. We analyzed the BCS…
Descriptors: Business Administration, Business Administration Education, Team Sports, College Athletics
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McKenzie, Robert G. – Learning Disability Quarterly, 2009
The assessment procedures within Response to Intervention (RTI) models have begun to supplant the use of traditional, discrepancy-based frameworks for identifying students with specific learning disabilities (SLD). Many RTI proponents applaud this shift because of perceived shortcomings in utilizing discrepancy as an indicator of SLD. However,…
Descriptors: Intervention, Learning Disabilities, Error of Measurement, Psychometrics
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Worts, Diana; Sacker, Amanda; McDonough, Peggy – Social Indicators Research, 2010
This paper addresses a key methodological challenge in the modeling of individual poverty dynamics--the influence of measurement error. Taking the US and Britain as case studies and building on recent research that uses latent Markov models to reduce bias, we examine how measurement error can affect a range of important poverty estimates. Our data…
Descriptors: Poverty, Measurement, Error of Measurement, Probability
Boyd, Don; Grossman, Pam; Lankford, Hamp; Loeb, Susanna; Wyckoff, Jim – National Center for Analysis of Longitudinal Data in Education Research, 2008
The use of value-added models in education research has expanded rapidly. These models allow researchers to explore how a wide variety of policies and measured school inputs affect the academic performance of students. An important question is whether such effects are sufficiently large to achieve various policy goals. Judging whether a change in…
Descriptors: Academic Achievement, Measures (Individuals), Measurement, Error of Measurement
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Meyer, Kevin D.; Foster, Jeff L. – International Journal of Testing, 2008
With the increasing globalization of human resources practices, a commensurate increase in demand has occurred for multi-language ("global") personality norms for use in selection and development efforts. The combination of data from multiple translations of a personality assessment into a single norm engenders error from multiple sources. This…
Descriptors: Global Approach, Cultural Differences, Norms, Human Resources
McCaffrey, Daniel F.; Lockwood, J. R.; Koretz, Daniel M.; Hamilton, Laura S. – RAND Corporation, 2003
Value-added modeling (VAM) to estimate school and teacher effects is currently of considerable interest to researchers and policymakers. Recent reports suggest that VAM demonstrates the importance of teachers as a source of variance in student outcomes. Policymakers see VAM as a possible component of education reform through improved teacher…
Descriptors: Educational Change, Accountability, Inferences, Models