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Gertler, Dianne B.; Barker, Linda A. – 1972
This document contains data obtained from a pretest survey conducted to test the applicability of the instrument and the suitability of procedures to be used in the survey of secondary-level course offerings and enrollments during the 1972-73 school year. The last comprehensive survey of this nature was taken during the 1960-61 school year. The…
Descriptors: Courses, Curriculum, Enrollment, Measurement Instruments
Neel, John H.; Stallings, William M. – 1974
An influential statistics test recommends a Levene text for homogeneity of variance. A recent note suggests that Levene's test is upwardly biased for small samples. Another report shows inflated Alpha estimates and low power. Neither study utilized more than two sample sizes. This Monte Carlo study involved sampling from a normal population for…
Descriptors: Analysis of Variance, Educational Research, Hypothesis Testing, Monte Carlo Methods
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Flynn, James R. – Journal of Educational Measurement, 1984
Thorndike's Stanford-Binet data suggest that from 1932 to 1971-72 preschool children enjoyed greater IQ gains than older children, possibly due to the rise of television. Additional analysis indicated that gains were either due to sampling error or totally antedated 1947. Gains of 12 IQ points were found for Americans. (Author/EGS)
Descriptors: Achievement Gains, Age Differences, Intelligence Differences, Intelligence Quotient
Shaughnessy, J. Michael; Ciancetta, Matt; Canada, Dan – International Group for the Psychology of Mathematics Education, 2004
As part of a research project on students' understanding of variability in statistics, 272 students, (84 middle school and 188 secondary school, grades 6-12) were surveyed on a series of tasks involving repeated sampling. Students' reasoning on the tasks predominantly fell into three types: additive, proportional, or distributional, depending on…
Descriptors: Sampling, Sample Size, Secondary School Students, Statistical Analysis
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Tucker, Ledyard R.; Lewis, Charles – Psychometrika, 1973
Maximum likelihood factor analysis provides an effective method for estimation of factor matrices and a useful test statistic in the likelihood ratio for rejection of overly simple factor models. A reliability coefficient is proposed for analysis of factor solution. (Author/RK)
Descriptors: Analysis of Variance, Factor Analysis, Goodness of Fit, Item Sampling
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McQuitty, Louis L.; And Others – Educational and Psychological Measurement, 1972
Descriptors: Emotional Disturbances, Emotional Problems, Individual Characteristics, Mental Disorders
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Kristof, Walter – Psychometrika, 1971
Descriptors: Cognitive Measurement, Error of Measurement, Mathematical Models, Psychological Testing
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Passmore, David Lynn – Journal of Studies in Technical Careers, 1983
Vocational and technical education researchers need to be aware of the uses and limits of various statistical models. The author reviews the Rasch Model and applies it to results from a nutrition test given to student nurses. (Author)
Descriptors: Educational Research, Item Sampling, Nursing Education, Nutrition
Myers, Jane E.; Loesch, Larry C. – Humanist Educator, 1981
Discusses an instrument developed to assess the counseling needs of older persons. The Older Persons Counseling Needs Survey was administered to (N=107) older persons. Results of a statistical analysis indicated that older persons have a variety of counseling needs that are all approximately at the same need level. (RC)
Descriptors: Counseling Objectives, Individual Needs, Measures (Individuals), Needs Assessment
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Christensen, John O. – Journal of Library Administration, 1988
Description of common errors found in the statistical methodologies of research carried out by librarians, focuses on sampling and generalizability. The discussion covers the need to either adapt library research to the statistical abilities of librarians or to educate librarians in the proper use of statistics. (15 references) (CLB)
Descriptors: Educational Needs, Generalizability Theory, Higher Education, Library Education
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Kay, Robin – Journal of Educational Computing Research, 1992
Review of research investigating gender differences in computer-related behavior examines statistical and methodological flaws. Issues addressed include sample selection, sample size, scale development, scale quality, the use of univariate and multivariate analyses, regressional analysis, construct definition, construct testing, and the…
Descriptors: Computers, Construct Validity, Literature Reviews, Research Methodology
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Joarder, Anwar H. – International Journal of Mathematical Education in Science and Technology, 2002
The usual formula for variance depending on rounding off the sample mean lacks precision, especially when computer programs are used for the calculation. The well-known simplification of the total sums of squares does not always give benefit. Since the variance of two observations is easily calculated without the use of a sample mean, and the…
Descriptors: Research Design, Mathematics, Statistical Analysis, Sampling
Goldsamt, Milton R.; And Others – 1983
Third in a series, the monograph summarizes the key evaluation issues, design approaches, and statistical techniques used in conducting the 1980-1983 impact evaluation of Indian Education Act Title IV Part A programs. The monograph describes the major problems in evaluating the program to determine the degree of its positive contribution to…
Descriptors: American Indian Education, Data Analysis, Data Collection, Evaluation Methods
Tryon, Warren W. – 1984
A normally distributed data set of 1,000 values--ranging from 50 to 150, with a mean of 50 and a standard deviation of 20--was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from…
Descriptors: Computer Simulation, Estimation (Mathematics), Higher Education, Monte Carlo Methods
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Huberty, Carl J.; Blommers, Paul J. – Multivariate Behavioral Research, 1974
Descriptors: Analysis of Covariance, Analysis of Variance, Classification, Discriminant Analysis
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