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Jaffe, A. J.; Spirer, Herbert F. – 1987
Numerous misuses of statistics are described and illustrated, and ways of recognizing and avoiding such misuse are discussed. The following five categories of statistical misuse are identified: a lack of knowledge of the subject matter, the quality of the basic data, the preparation of the study and the report, the statistical methodology, and a…
Descriptors: Data Interpretation, Methods Research, Quality Control, Research Problems

Ward, Thomas J. Jr.; Clark, Henry T. III – Journal of Educational Research, 1991
A study compared the influence of four missing data techniques on three published analyses of the High School and Beyond data (reexamining public- versus private-school achievement). Results indicated that use of data-replacement techniques affected the conclusions of the three studies examined. A small positive effect favoring private schooling…
Descriptors: Academic Achievement, Data Interpretation, High Schools, Private Schools
Gilbert, Neil – American Enterprise, 1994
A careful review of research suggests that the problem of sexual assault has been magnified and the data misinterpreted, in part because the issue of sexual violence against women has become enmeshed in politics. The current trend in sexual-violence research is to redefine it according to the ideological preferences of advocates. (SLD)
Descriptors: Data Interpretation, Females, Identification, Ideology
Hiroto, Nagato – 1997
Designed for language teachers who find reading statistical research difficult but necessary, this article focuses on the minimal knowledge needed about statistical techniques for interpreting research findings. It first examines the statistical reasoning underlying quantitative, empirical studies, including normal distribution, standard…
Descriptors: Classroom Research, Classroom Techniques, Comparative Analysis, Data Interpretation

Meyer, John W.; Baker, David P. – Sociology of Education, 1996
Analyzes the use of relevant international data to inform educational policy. Notes that such data can be used to extend research on school effects by incorporating comparative data, by evaluating the trade-offs involved in attempts to raise national achievement levels, and by broadening the range of international data. (MJP)
Descriptors: Academic Achievement, Data Collection, Data Interpretation, Educational Development
Lehman, Penny W.; And Others – 1992
The Community College Student Experiences Questionnaire (CCSEQ) is an instrument designed to determine how community college students spend their time and what quality of effort they put into learning. Specifically, the content of the questionnaire focuses on four elements: who students are and why they are at the college; how extensively and…
Descriptors: College Environment, Community Colleges, Comparative Analysis, Data Collection
Lehman, Penny W.; And Others – 1995
The Community College Student Experiences Questionnaire (CCSEQ) is designed to determine how community college students spend their time and the quality of effort that they put into the learning process. This updated manual describes elements of the CCSEQ and presents findings from 56 community colleges that have administered the questionnaire as…
Descriptors: College Environment, Community Colleges, Comparative Analysis, Data Collection