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Mikheeva, Ekaterina, Ed.; Meyer, Sebastian, Ed. – International Association for the Evaluation of Educational Achievement, 2020
IEA's International Computer and Information Literacy Study (ICILS) 2018 is designed to assess how well students are prepared for study, work, and life in a digital world. The study measures international differences in students' computer and information literacy (CIL): their ability to use computers to investigate, create, participate, and…
Descriptors: International Assessment, Computer Literacy, Information Literacy, Computer Assisted Testing
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Enders, Craig K. – Grantee Submission, 2017
The last 20 years has seen an uptick in research on missing data problems, and most software applications now implement one or more sophisticated missing data handling routines (e.g., multiple imputation or maximum likelihood estimation). Despite their superior statistical properties (e.g., less stringent assumptions, greater accuracy and power),…
Descriptors: Data Analysis, Computer Software, Computation, Statistical Analysis
Fraillon, Julian, Ed.; Ainley, John, Ed.; Schulz, Wolfram, Ed.; Friedman, Tim, Ed.; Duckworth, Daniel, Ed. – International Association for the Evaluation of Educational Achievement, 2020
IEA's International Computer and Information Literacy Study (ICILS) 2018 investigated how well students are prepared for study, work, and life in a digital world. ICILS 2018 measured international differences in students' computer and information literacy (CIL): their ability to use computers to investigate, create, participate, and communicate at…
Descriptors: International Assessment, Computer Literacy, Information Literacy, Computer Assisted Testing
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Pampaka, Maria; Hutcheson, Graeme; Williams, Julian – International Journal of Research & Method in Education, 2016
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
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Ghergulescu, Ioana; Muntean, Cristina Hava – International Journal of Artificial Intelligence in Education, 2016
Engagement influences participation, progression and retention in game-based e-learning (GBeL). Therefore, GBeL systems should engage the players in order to support them to maximize their learning outcomes, and provide the players with adequate feedback to maintain their motivation. Innovative engagement monitoring solutions based on players'…
Descriptors: Case Studies, Questionnaires, Electronic Learning, Educational Games
OECD Publishing, 2013
The Programme for the International Assessment of Adult Competencies (PIAAC) has been planned as an ongoing program of assessment. The first cycle of the assessment has involved two "rounds." The first round, which is covered by this report, took place over the period of January 2008-October 2013. The main features of the first cycle of…
Descriptors: International Assessment, Adults, Skills, Test Construction
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Ray, Darrell L. – American Biology Teacher, 2013
Students often enter biology programs deficient in the math and computational skills that would enhance their attainment of a deeper understanding of the discipline. To address some of these concerns, I developed a series of spreadsheet simulation exercises that focus on some of the mathematical foundations of scientific inquiry and the benefits…
Descriptors: Science Instruction, Mathematics Skills, Educational Technology, Spreadsheets
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Gottschall, Amanda C.; West, Stephen G.; Enders, Craig K. – Multivariate Behavioral Research, 2012
Behavioral science researchers routinely use scale scores that sum or average a set of questionnaire items to address their substantive questions. A researcher applying multiple imputation to incomplete questionnaire data can either impute the incomplete items prior to computing scale scores or impute the scale scores directly from other scale…
Descriptors: Questionnaires, Data Analysis, Computation, Monte Carlo Methods
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McPhee, C.; Bielick, S.; Masterton, M.; Flores, L.; Parmer, R.; Amchin, S.; Stern, S.; McGowan, H. – National Center for Education Statistics, 2015
The 2012 National Household Education Surveys Program (NHES:2012) Data File User's Manual provides documentation and guidance for users of the NHES:2012 data files. The manual provides information about the purpose of the study, the sample design, data collection procedures, data processing procedures, response rates, imputation, weighting and…
Descriptors: Early Childhood Education, Family Involvement, Parent Participation, Family School Relationship
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Feldman, Betsy J.; Masyn, Katherine E.; Conger, Rand D. – Developmental Psychology, 2009
Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the…
Descriptors: Structural Equation Models, Behavior Problems, Student Behavior, Adolescents
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Tourkin, Steven; Thomas, Teresa; Swaim, Nancy; Cox, Shawna; Parmer, Randall; Jackson, Betty; Cole, Cornette; Zhang, Bei – National Center for Education Statistics, 2010
The Schools and Staffing Survey (SASS) is conducted by the National Center for Education Statistics (NCES) on behalf of the United States Department of Education in order to collect extensive data on American public and private elementary and secondary schools. SASS provides data on the characteristics and qualifications of teachers and…
Descriptors: Elementary Secondary Education, National Surveys, Public Schools, Private Schools
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Ingels, Steven J.; Pratt, Daniel J.; Wilson, David; Burns, Laura J.; Currivan, Douglas; Rogers, James E.; Hubbard-Bednasz, Sherry – National Center for Education Statistics, 2007
The Data File Documentation reports on the procedures and methodologies employed during the Education Longitudinal Study of 2002 base year, first, and second follow-ups, with special emphasis on the second follow-up (2006). The document is designed to provide guidance for users of the restricted-use data as released in Electronic Codebook (ECB)…
Descriptors: Longitudinal Studies, Research Methodology, High School Students, Data Collection