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Direct linkEndler Marcel Borges – Journal of Chemical Education, 2023
An understanding of statistical concepts is necessary for a chemist with a complete education. Here, statistical tests were taught using the R Commander and the Factoshiny packages. These packages run on R software and have a graphical user interface (GUI), which allows students to do statistical tests quickly and easily. These packages were…
Descriptors: Statistics Education, Programming Languages, Chemistry, Science Instruction
Vaske, Jerry J. – Sagamore-Venture, 2019
Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the…
Descriptors: Surveys, Theories, Hypothesis Testing, Effect Size
Cho, Jaehee; Lee, H. Erin; Quinlan, Margaret – Journal of American College Health, 2017
Objective: Based on the technology acceptance model (TAM), we explored the nationally-bounded roles of four predictors (subjective norms, entertainment, recordability, and networkability) in determining the TAM variables of perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) to use diet/fitness apps on…
Descriptors: Cross Cultural Studies, Telecommunications, Handheld Devices, College Students
Shin, Dong-Hee; An, Hyeri; Kim, Jang Hyun – Interactive Learning Environments, 2016
The use of a second screen can enhance information processing and the execution of search tasks within a given period. In this study, we examined the learner's attentional shift (AS) between two screens and controlled secondary tasks (STs) in the media multitasking setting and its effect on the learning process. In particular, we analyzed how…
Descriptors: Cognitive Processes, Search Strategies, Attention Control, Learning Processes
Sommer, Lutz – Electronic Journal of Research in Educational Psychology, 2013
Introduction: The paper at hand examines the influence of Cognitive Style (CS) and International Experience (IE) on International Entrepreneurial Self-Efficacy (IESE) and International Entrepreneurial Intention (IEI) as well as the role of Academic Education (AE) in this context. Method: Based on a survey that was conducted among 111 students the…
Descriptors: Entrepreneurship, Cognitive Style, Academic Education, Models
DiStefano, Christine; Zhu, Min; Mindrila, Diana – Practical Assessment, Research & Evaluation, 2009
Following an exploratory factor analysis, factor scores may be computed and used in subsequent analyses. Factor scores are composite variables which provide information about an individual's placement on the factor(s). This article discusses popular methods to create factor scores under two different classes: refined and non-refined. Strengths and…
Descriptors: Factor Structure, Factor Analysis, Researchers, Scores
Peer reviewedSchurr, K. Terry; Henriksen, L. W. – Educational and Psychological Measurement, 1984
Provided is a description of three methods for testing certain types of a priori hypotheses about differences among covariance matrices. Briefly outlined are procedures for using two computer programs, COFAMM and LISREL, for testing such hypotheses. Also provided are examples of application of the methods to a meaningful data set. (Author/BW)
Descriptors: Analysis of Covariance, Computer Software, Factor Analysis, Hypothesis Testing
Download full textStamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
