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Shear, Benjamin R.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013
Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…
Descriptors: Error of Measurement, Multiple Regression Analysis, Data Analysis, Computer Simulation
Ciorba, Charles R.; Russell, Brian E. – Journal of Research in Music Education, 2014
The purpose of this study was to test a hypothesized model that proposes a causal relationship between motivation and academic achievement on the acquisition of jazz theory knowledge. A reliability analysis of the latent variables ranged from 0.92 to 0.94. Confirmatory factor analyses of the motivation (standardized root mean square residual…
Descriptors: Music Education, Hypothesis Testing, Causal Models, Student Motivation
Miciak, Jeremy; Williams, Jacob L.; Taylor, W. Pat; Cirino, Paul T.; Fletcher, Jack M.; Vaughn, Sharon – Journal of Educational Psychology, 2016
No previous empirical study has investigated whether the learning disabilities (LD) identification decisions of proposed methods to operationalize processing strengths and weaknesses approaches for LD identification are associated with differential treatment response. We investigated whether the identification decisions of the…
Descriptors: Learning Disabilities, Disability Identification, Predictor Variables, Intervention
van Rens, Lisette; Hermarij, Philip; Pilot, Albert; Beishuizen, Jos; Hofman, Herman; Wal, Marjolein – International Journal of Science Education, 2014
Peer review is a significant component in scientific research. Introducing peer review into inquiry processes may be regarded as an aim to develop student understanding regarding quality in inquiries. This study examines student understanding in inquiry peer reviews among pre-university chemistry students, aged 16-17, when they enact a design of a…
Descriptors: Peer Evaluation, Chemistry, Secondary School Students, Secondary School Science
Kang, Minseok; Shin, Won sug – Journal of Educational Computing Research, 2015
This study proposes an extended technology acceptance model to predict acceptance of synchronous e-learning by examining relationships among variables associated with factors influencing the technology acceptance of synchronous e-learning. Learners at an online university participated through an online survey; there were 251 respondents in all.…
Descriptors: Foreign Countries, Graduate Students, Student Attitudes, Adoption (Ideas)
Gillham, Andrew D. – ProQuest LLC, 2009
Coaching success is often defined in terms of won-loss records. However, the effort to operationalize more comprehensive strategies for examining coaching success, particularly the development of self-report measures, dates back three decades (e.g., Smith, Smoll, & Curtis, 1979). This project for developing the Coaching Success Questionnaire-2…
Descriptors: Athletes, College Athletics, Athletic Coaches, Questionnaires
Takeuchi, Riki; Lepak, David P.; Wang, Heli; Takeuchi, Kazuo – Journal of Applied Psychology, 2007
The resource-based view of the firm and social exchange perspectives are invoked to hypothesize linkages among high-performance work systems, collective human capital, the degree of social exchange in an establishment, and establishment performance. The authors argue that high-performance work systems generate a high level of collective human…
Descriptors: Human Capital, Social Exchange Theory, Hypothesis Testing, Foreign Countries
Maxwell, Scott E. – 1979
Arguments have recently been put forth that standard textbook procedures for determining the sample size necessary to achieve a certain level of power in a completely randomized design are incorrect when the dependent variable is fallible because they ignore measurement error. In fact, however, there are several correct procedures, one of which is…
Descriptors: Hypothesis Testing, Mathematical Formulas, Power (Statistics), Predictor Variables

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