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Grochowalski, Joseph H. – ProQuest LLC, 2015
Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…
Descriptors: Computation, Psychometrics, Profiles, Scores
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Sang, Jane – Journal of Education and Practice, 2016
The purpose of this study to assess communication channels as implementation determinants of performance management framework In Kenya at Moi Teaching and Referral Hospital (MTRH). The communication theory was used to inform the study. This study adopted an explanatory design. The target sampled 510 respondents through simple random and stratified…
Descriptors: Foreign Countries, Program Implementation, Models, Multivariate Analysis
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Nguyen, ThuyUyen H.; Charity, Ian; Robson, Andrew – Studies in Higher Education, 2016
This study investigates students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement in higher education. Guided by learning environments concepts and attitudinal theory, a theoretical model was proposed with two instruments, one for measuring the learning environment and…
Descriptors: Foreign Countries, Higher Education, Student Attitudes, Academic Achievement
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Ananda B. W. Manage; Stephen M. Scariano – Journal of Statistics Education, 2013
Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…
Descriptors: Factor Analysis, Multivariate Analysis, Data Analysis, Student Interests
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Niculescu, Alexandra C.; Templelaar, Dirk; Leppink, Jimmie; Dailey-Hebert, Amber; Segers, Mien; Gijselaers, Wim – Electronic Journal of Research in Educational Psychology, 2015
Introduction: This study examined the predictive value of four learning-related emotions--Enjoyment, Anxiety, Boredom and Hopelessness for achievement outcomes in the first year of study at university. Method: We used a large sample (N = 2337) of first year university students enrolled over three consecutive academic years in a mathematics and…
Descriptors: College Freshmen, College Mathematics, Psychological Patterns, Predictor Variables
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Tarasi, Crina O.; Wilson, J. Holton; Puri, Cheenu; Divine, Richard L. – Journal of Marketing Education, 2013
Marketing students are known as less likely to have an affinity for the quantitative aspects of the marketing discipline. In this article, we study the reasons why this might be true and develop a parsimonious 20-item scale for measuring quantitative affinity in undergraduate marketing students. The scale was administered to a sample of business…
Descriptors: Undergraduate Students, Marketing, Business Administration Education, Majors (Students)
Polito, Vincent A., Jr. – ProQuest LLC, 2010
The objective of this research was to explore the possibilities of identifying knowledge style factors that could be used as central elements of a professional business analyst's (PBA) performance attributes at work for those decision makers that use advanced analytical technologies on decision making tasks. Indicators of knowledge style were…
Descriptors: Cognitive Style, Personality, Scoring, Multivariate Analysis
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Strang, Kenneth David – Practical Assessment, Research & Evaluation, 2009
This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…
Descriptors: Multicultural Education, Computer Software, Multiple Regression Analysis, Multidimensional Scaling