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Cris E. Haltom; Tate F. Halverson – Journal of American College Health, 2024
Objective: This study examined relationships between eating disorder risk (EDR), lifestyle variables (e.g., exposure to healthy eating media), and differences among male and female college students. Participants: College students (N = 323) completed survey questionnaires (Fall, 2016). Fifty-three participants retook the survey at a later time.…
Descriptors: Eating Disorders, Life Style, At Risk Students, Gender Differences
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Krenzke, Tom; Mohadjer, Leyla; Li, Jianzhu; Erciulescu, Andreea; Fay, Robert; Ren, Weijia; Van de Kerckhove, Wendy; Li, Lin; Rao, J. N. K. – National Center for Education Statistics, 2020
The Program for the International Assessment of Adult Competencies (PIAAC) is a multicycle survey of adult skills and competencies sponsored by the Organization for Economic Cooperation and Development (OECD). The survey examines a range of basic skills in the information age and assesses these adult skills consistently across participating…
Descriptors: Adults, Surveys, Statistical Analysis, Computation
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Silva-Lugo, Jose L.; Warner, Laura A.; Galindo, Sebastian – Journal of Agricultural Education and Extension, 2022
Purpose: A literature research conducted in education and agricultural education journals published during a period of 10 years revealed that 98% of the studies used parametric analyses. In general, model assumptions were not tested, and statistical criteria were not followed to apply the parametric approach. The objective of this paper is to…
Descriptors: Agricultural Education, Nonparametric Statistics, Educational Research, Models
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Alalouch, Chaham – Education Sciences, 2021
Cognitive styles affect the learning process positively if tasks are matched to the cognitive style of learners. This effect becomes more pronounced in complex education, such as in engineering. We attempted to critically assess the effect of cognitive styles and gender on students' academic performance in eight engineering majors to understand…
Descriptors: Cognitive Style, Gender Differences, Academic Achievement, Engineering Education
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Cyrenne, Philippe; Chan, Alan – Canadian Journal of Higher Education, 2022
The ability of universities and colleges to predict the success of admitted students continues to be a key concern of higher education officials. Apart from a desire to see students have successful academic careers, there is also the fiscal reality of greater tuition revenues providing needed support for university budgets. Using administrative…
Descriptors: College Students, Academic Achievement, Predictor Variables, Statistical Analysis
Carter, Rose A. – ProQuest LLC, 2022
This study aimed to assess the effectiveness of existing insolvency predictive models employed for non-profit Higher Education Institutions (HEIs) and test a proposed predictive model utilizing statistical and ratio analysis by comparing HEIs in operations with those that closed from 2017 to 2020. The researcher incorporated a non-experimental,…
Descriptors: Prediction, Models, Higher Education, Nonprofit Organizations
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Sorjonen, Kimmo; Melin, Bo; Ingre, Michael – Educational and Psychological Measurement, 2019
The present simulation study indicates that a method where the regression effect of a predictor (X) on an outcome at follow-up (Y1) is calculated while adjusting for the outcome at baseline (Y0) can give spurious findings, especially when there is a strong correlation between X and Y0 and when the test-retest correlation between Y0 and Y1 is…
Descriptors: Predictor Variables, Regression (Statistics), Correlation, Error of Measurement
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Enders, Craig K.; Du, Han; Keller, Brian T. – Grantee Submission, 2019
Despite the broad appeal of missing data handling approaches that assume a missing at random (MAR) mechanism (e.g., multiple imputation and maximum likelihood estimation), some very common analysis models in the behavioral science literature are known to cause bias-inducing problems for these approaches. Regression models with incomplete…
Descriptors: Hierarchical Linear Modeling, Regression (Statistics), Predictor Variables, Bayesian Statistics
Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2023
We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random…
Descriptors: Maximum Likelihood Statistics, Hierarchical Linear Modeling, Error of Measurement, Statistical Distributions
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Reddy, Pritika; Chaudhary, Kaylash; Sharma, Bibhya; Chand, Ronil – Electronic Journal of e-Learning, 2022
The individuals living in the 21st century have become the consumers of digital innovations and have to adapt, adopt and adapt to the new norm of surviving and thriving in the digital society. Familiarity with the latest technologies is not the only requirement for survival. One also needs to have relevant digital competencies to complete tasks…
Descriptors: Digital Literacy, Evaluation, Predictor Variables, Higher Education
Najib A. Mozahem – Sage Research Methods Cases, 2021
The internet has had a vast and pervasive effect on many industries. It has resulted in the creation of new industries and has overhauled the dynamics that governed existing industries. One of the most traditional industries that is now struggling to cope with the changes brought on by the internet is the industry of higher education. Students can…
Descriptors: Social Sciences, Electronic Learning, Learning Management Systems, Higher Education
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Tanner, Sean; Terrell, Jenna; Vislosky, Emily; Gellar, Jonathan; Gill, Brian – Regional Educational Laboratory Mid-Atlantic, 2021
Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both. Inaccurate predictions can disrupt learning as districts adjust to enrollment fluctuations by reshuffling teachers and students well into the fall semester. This…
Descriptors: Enrollment, Enrollment Projections, School Districts, Statistical Analysis
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Regional Educational Laboratory Mid-Atlantic, 2021
Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both. Inaccurate predictions can disrupt learning as districts adjust to enrollment fluctuations by reshuffling teachers and students well into the fall semester. This…
Descriptors: Enrollment, Enrollment Projections, School Districts, Statistical Analysis
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Regional Educational Laboratory Mid-Atlantic, 2021
Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both. Inaccurate predictions can disrupt learning as districts adjust to enrollment fluctuations by reshuffling teachers and students well into the fall semester. The…
Descriptors: Enrollment, Enrollment Projections, School Districts, Statistical Analysis
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Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – AERA Open, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Identification, Two Year College Students, Community Colleges
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