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Simsek, Ahmet Salih – International Journal of Assessment Tools in Education, 2023
Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and…
Descriptors: Error of Measurement, Likert Scales, Nonparametric Statistics, Statistical Analysis
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Ching, Cynthia Carter; Hagood, Danielle – Journal of Science Education and Technology, 2019
This paper connects the technological practice of activity monitor gaming to the Next Generation Science Standards (NGSS) science and engineering practice of "analyzing and interpreting data," and to the foundational constructionist idea of personal meaning. In our larger study, eighth-grade students, ages 12-14, wore physical activity…
Descriptors: Middle School Students, Grade 8, Educational Games, Academic Standards
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Park, Jungkyu; Yu, Hsiu-Ting – Educational and Psychological Measurement, 2016
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…
Descriptors: Hierarchical Linear Modeling, Nonparametric Statistics, Data Analysis, Simulation
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Pinder, Jonathan P. – Decision Sciences Journal of Innovative Education, 2014
Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…
Descriptors: Data Collection, Data Analysis, Regression (Statistics), Predictive Measurement
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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
Sarkar, Saurabh – ProQuest LLC, 2013
In the modern world information has become the new power. An increasing amount of efforts are being made to gather data, resources being allocated, time being invested and tools being developed. Data collection is no longer a myth; however, it remains a great challenge to create value out of the enormous data that is being collected. Data modeling…
Descriptors: Data Analysis, Data Collection, Error of Measurement, Research Problems
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Zhuang, Jie; Chen, Peijie; Wang, Chao; Jin, Jing; Zhu, Zheng; Zhang, Wenjie – Research Quarterly for Exercise and Sport, 2013
Purpose: The purpose of this study was to determine which method, individual information-centered (IIC) or group information-centered (GIC), is more efficient in recovering missing physical activity (PA) data. Method: A total of 2,758 Chinese children and youth aged 9 to 17 years old (1,438 boys and 1,320 girls) wore ActiGraph GT3X/GT3X+…
Descriptors: Foreign Countries, Physical Activities, Measurement Equipment, Data Analysis
Kenney McCulloch, Susan – ProQuest LLC, 2012
Many telephone surveys require interviewers to observe and record respondents' gender based solely on respondents' voice. Researchers may rely on these observations to: (1) screen for study eligibility; (2) determine skip patterns; (3) foster interviewer tailoring strategies; (4) contribute to nonresponse assessment and adjustments; (5)…
Descriptors: Telephone Surveys, Gender Differences, Acoustics, Observation
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Claassen, Cynthia A.; Yip, Paul S.; Corcoran, Paul; Bossarte, Robert M.; Lawrence, Bruce A.; Currier, Glenn W. – Suicide and Life-Threatening Behavior, 2010
Durkheim's nineteenth-century analysis of national suicide rates dismissed prior concerns about mortality data fidelity. Over the intervening century, however, evidence documenting various types of error in suicide data has only mounted, and surprising levels of such error continue to be routinely uncovered. Yet the annual suicide rate remains the…
Descriptors: Suicide, Data Analysis, Data Interpretation, Models
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Ingels, Steven J.; Pratt, Daniel J.; Herget, Deborah R.; Dever, Jill A.; Fritch, Laura Burns; Ottem, Randolph; Rogers, James E.; Kitmitto, Sami; Leinwand, Steve – National Center for Education Statistics, 2013
This manual has been produced to familiarize data users with the design, and the procedures followed for data collection and processing, in the base year and first follow-up of the High School Longitudinal Study of 2009 (HSLS:09), with emphasis on the first follow-up. It also provides the necessary documentation for use of the public-use data…
Descriptors: High School Students, Longitudinal Studies, Annual Reports, Followup Studies
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Ingels, Steven J.; Pratt, Daniel J.; Herget, Deborah R.; Dever, Jill A.; Fritch, Laura Burns; Ottem, Randolph; Rogers, James E.; Kitmitto, Sami; Leinwand, Steve – National Center for Education Statistics, 2013
The manual that accompanies these appendices was produced to familiarize data users with the design, and the procedures followed for data collection and processing, in the base year and first follow-up of the High School Longitudinal Study of 2009 (HSLS:09), with emphasis on the first follow-up. It also provides the necessary documentation for use…
Descriptors: High School Students, Longitudinal Studies, Annual Reports, Followup Studies
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Lee, In Heok – Career and Technical Education Research, 2012
Researchers in career and technical education often ignore more effective ways of reporting and treating missing data and instead implement traditional, but ineffective, missing data methods (Gemici, Rojewski, & Lee, 2012). The recent methodological, and even the non-methodological, literature has increasingly emphasized the importance of…
Descriptors: Vocational Education, Data Collection, Maximum Likelihood Statistics, Educational Research
Enders, Craig K. – Guilford Press, 2010
Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Maximum Likelihood Statistics
Lewin, Keith M. – Open Society Foundations, 2015
The purpose of this paper is to review recent developments related to the development of indicators of educational progress in the context of the Post 2015 deliberations to generate a new international architecture for educational investment through to 2030. There have been a plethora of suggestions and several parallel consultation processes…
Descriptors: Educational Indicators, Educational Objectives, Goal Orientation, Preschool Education
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Tourangeau, Karen; Nord, Christine; Lê, Thanh; Wallner-Allen, Kathleen; Vaden-Kiernan, Nancy; Blaker, Lisa; Najarian, Michelle – National Center for Education Statistics, 2018
This manual provides guidance and documentation for users of the longitudinal kindergarten-fourth grade (K-4) public-use data file of the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011), which includes the first release of the public version of the third-grade data. This manual mainly provides information specific…
Descriptors: Longitudinal Studies, Children, Surveys, Kindergarten
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