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Zita Lysaght; Gemma Cherry – Sage Research Methods Cases, 2022
An advantage of well-designed survey research is that, typically, it yields large volumes of quantitative and qualitative data. A key challenge researchers face is mining and reporting these data appropriately and promptly to inform research publications and presentations. When mixed methods are employed, and researchers attempt to master computer…
Descriptors: Mixed Methods Research, Research Methodology, Research Problems, Barriers
Dauter, Luke; Olivieri, Samantha – Education Cities, 2018
The Education Equality Index (EEI), created through a unique collaboration of GreatSchools and Education Cities, is a nationally comparative measure that looks at how well students from low-income families are achieving in schools, cities, and states across the country and The EEI taps GreatSchools' national school information database and…
Descriptors: Educational Quality, Measures (Individuals), Low Income Students, Academic Achievement
Arlo Kempf – Sage Research Methods Cases, 2016
From 2011 to 2013, I accidentally studied US and Canadian teacher perceptions of standardized testing and its effects in the classroom. I had intended to study the role of equity issues in teacher practice; however, in conversation with teachers during initial semi-structured interviews in the United States, it became clear that understanding the…
Descriptors: Mixed Methods Research, Teacher Attitudes, Standardized Tests, Foreign Countries
Volkwein, J. Fredericks; Yin, Alexander C. – New Directions for Institutional Research, 2010
This chapter summarizes ten selected issues and common problems that arise in most assessment research projects. These include: (1) the uses of grades in assessment; (2) institutional review boards; (3) research design as a compromise; (4) standardized testing; (5) self-reported measures; (6) missing data; (7) weighting data; (8) conditional…
Descriptors: Research Design, Research Methodology, Standardized Tests, Least Squares Statistics
Blaich, Charles F.; Wise, Kathleen S. – New Directions for Institutional Research, 2010
Most assessment arguments are about measurement. When is it better to use direct versus indirect measures of student learning? Is one standardized test of critical thinking better than another? Is applying rubrics to student work better than using standardized tests? How valid are self-reported measures of learning? Although these arguments are…
Descriptors: Standardized Tests, Academic Achievement, Program Effectiveness, Liberal Arts
Johns, Jerry L. – Illinois School Research, 1975
Article investigated the effectiveness of a procedure designed to accurately reflect a student's reading level. (Editor/RK)
Descriptors: Data Analysis, Diagrams, Reading Instruction, Reading Level
Greene, Jay P.; And Others – 1996
In 1990, Milwaukee (Wisconsin) became the site of the first publicly funded school-choice program providing low-income parents with vouchers that could be used to send their children to secular, private schools. An evaluation of Milwaukee's school-choice experiment was conducted by a team of researchers, headed by John Witte at the University of…
Descriptors: Academic Achievement, Data Analysis, Elementary Secondary Education, Evaluation Problems
Allen, Nancy L.; Carlson, James E.; Zelenak, Christine A. – 1999
This report documents the design, administration, and data analysis procedure of the National Assessment of Education Progress (NAEP) for 1996. It indicates the technical decisions that were made and the rationale behind them. Detailed substantive findings are not presented in this report. These chapters provide technical information about the…
Descriptors: Academic Achievement, Data Analysis, Data Collection, Databases
Ferrara, Steven; Walker-Bartnick, Leslie – 1990
A procedure was developed to detect differential item functioning (DIF) in a standardized essay test using the Partial Credit Model, the general polychotomous form of the Rasch model. Using a panel of experts in the writing process, hypothesized explanations for DIF at some score points were developed. Data for the study included averaged…
Descriptors: Achievement Tests, Data Analysis, Essay Tests, Evaluation Methods
Johnson, Eugene G.; And Others – 1990
Details of the design and data analysis associated with the 1988 National Assessment of Educational Progress (NAEP) are presented. The 1988 NAEP surveyed American students' knowledge of reading, writing, civics, U.S. history, and geography. Small-scale studies were conducted for mathematics and science. Populations included public school students…
Descriptors: Annual Reports, Data Analysis, Databases, Educational Assessment
Horst, Donald P.; And Others – 1974
Directors of educational projects need to be aware of the consequences their decisions may have for evaluation and appreciate the need for working closely with their evaluators from the earliest planning stage. Attempting to address the needs of project directors and evaluators, this guidebook deals with one central aspect of project…
Descriptors: Achievement Gains, Analysis of Covariance, Cognitive Measurement, Data Analysis
Horst, Donald P.; And Others – 1975
Measuring cognitive achievement gains in project evaluation is dealt with. This guide provides those concerned with project evaluation with the basic tools for conducting technically sound, interpretable evaluation studies. Exotic designs are avoided and five basic models which appear feasible to implement in real-world settings are focused on.…
Descriptors: Academic Achievement, Achievement Gains, Cognitive Development, Data Analysis
Stamper, 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