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Long, Susan; Cognetta, Randall A. – 1978
After a brief introduction and discussion of the advantages and disadvantages of questionnaires, this publication explains how to develop a questionnaire. Each of the five parts of a questionnaire (heading, procedural statements, items, comment space, and procedures for return) are discussed, with concrete examples. Guidelines for pre-test/tryout…
Descriptors: Data Analysis, Data Collection, Field Tests, Questionnaires
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Schneider, Gail Thierbach; Burgos, Fermin – NASSP Bulletin, 1987
Computer-based management systems offer an efficient means of recording, categorizing, and summarizing data on student discipline. This article furnishes basic information on developing student files, entering data, and generating meaningful reports, such as individual student profiles, date and period frequency, student referral distribution, and…
Descriptors: Computer Software, Data Collection, Data Interpretation, Database Management Systems
Beaudin, Bart P.; Dowling, William D. – Performance and Instruction, 1985
Discusses results of a survey of trainers in midwest business and industry to identify the different data collection methods they used to determine job and task training needs as well as organizational and individual training needs within their organizations. (MBR)
Descriptors: Business, Data Collection, Individual Needs, Methods
Sneider, Cary; And Others – Classroom Computer Learning, 1986
Discusses various software packages which help science and mathematics students make sense out of data (formulating hypotheses, interpreting graphs, analyzing possibilities, and collecting data with real-time graphing). Includes a list of commercially available programs for the areas, with program name, publisher, grade level(s), price, and…
Descriptors: Computer Software, Data Analysis, Data Collection, Elementary Secondary Education
Thomas, R. Murray – 2003
This guide discusses combining qualitative and quantitative research methods in theses and dissertations. It covers a wide array of methods, the strengths and limitations of each, and how they can be effectively interwoven into various research designs. The first chapter is "The Qualitative and the Quantitative." Part 1, "A…
Descriptors: Data Analysis, Data Collection, Data Interpretation, Doctoral Dissertations
Office of Student Financial Assistance (ED), Washington, DC. – 2002
This manual is a guide to handling data related to the federal student financial aid program through the electronic data exchange (EDE) system EDExpress. The overview defines how data is exchanged and identifies key players in the exchange of data. The overall process for sending and receiving data is outlined, along with the overall processing…
Descriptors: College Applicants, Data Collection, Federal Aid, Higher Education
LeTendre, Brenda Guenther; Lipka, Richard – 2000
This guide gives elementary school educators the tools they need to get answers to their questions about program evaluation. It shows how to use information to work more effectively and efficiently, and its explains how to use program evaluation techniques to make sure that the solutions selected truly solve the problems of interest. The first two…
Descriptors: Data Analysis, Data Collection, Elementary Education, Elementary School Teachers
Caro-Bruce, Cathy – 2000
This handbook is a roadmap for action research facilitators to help groups as they work through the research process. It offers quotations, handouts, strategies, resources, and insights from actual experiences. The sections of the handbook follow the action research cycle, focusing on: "What is Action Research?"; "What is the Action…
Descriptors: Action Research, Data Collection, Educational Research, Elementary Secondary Education
Russell, Liz – 1999
This informational packet provides guidelines to help schools make the most of their school grounds and contains the following: a handbook; a loose leaf activity pack; videotape; and poster. The handbook provides information on each stage of the school ground development/change process, offers the rationale for improving school grounds, and lists…
Descriptors: Data Collection, Educational Facilities Improvement, Elementary Schools, Foreign Countries
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Mamchur, Carolyn M. – Canadian Journal of Education, 1981
Familiarizes educators and researchers with a method to develop curriculum according to the individual cognitive styles of individual students. (Author)
Descriptors: Cognitive Development, Cognitive Style, Curriculum Development, Data Collection
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Gugerty, John J.; Crowley, Carol Berry – Journal for Vocational Special Needs Education, 1982
The authors present an informal assessment strategy format to aid teachers in meeting the needs of the handicapped student. The format includes an assessment sequence, how to analyze assessment results, and how to interpret results. (CT)
Descriptors: Data Analysis, Data Collection, Disabilities, Informal Assessment
Morris, John M. – American School and University, 1981
The first of two articles outlines how the director of the physical plant at Monroe County Community College (Michigan) developed a preventive maintenance program. The first step, assembling a set of up-to-date records, is detailed. (MLF)
Descriptors: Data Collection, Equipment Maintenance, Facility Inventory, Prevention
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Trezise, Kathy – Teaching Children Mathematics, 1995
Provides measurement, estimation, and data analysis activities for grades three to four and five to six using boxes of cereal and containers of frozen yogurt. (MKR)
Descriptors: Computation, Data Collection, Elementary Education, Food
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Starkey, Mary Ann – Teaching Children Mathematics, 1995
The more that students see themselves in everyday situations that involve mathematics, the more likely they are to be successful. Incorporating names, ages, and other personal data into mathematical activities is easy and fun. (MKR)
Descriptors: Childhood Interests, Data Collection, Elementary School Mathematics, Learning Activities
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Somers, Kay; And Others – Mathematics Teacher, 1996
Presents an activity to help students overcome mathematics anxiety by collecting and organizing data through summarization and graphic representation. (MKR)
Descriptors: Data Collection, Graphs, Higher Education, Learning Activities
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