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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
Peer reviewed Peer reviewed
Wilcox, Rand R. – Journal of Educational Statistics, 1984
Two stage multiple-comparison procedures give an exact solution to problems of power and Type I errors, but require equal sample sizes in the first stage. This paper suggests a method of evaluating the experimentwise Type I error probability when the first stage has unequal sample sizes. (Author/BW)
Descriptors: Hypothesis Testing, Mathematical Models, Power (Statistics), Probability
Peer reviewed Peer reviewed
Towstopiat, Olga – Contemporary Educational Psychology, 1984
The present article reviews the procedures that have been developed for measuring the reliability of human observers' judgments when making direct observations of behavior. These include the percentage of agreement, Cohen's Kappa, phi, and univariate and multivariate agreement measures that are based on quasi-equiprobability and quasi-independence…
Descriptors: Interrater Reliability, Mathematical Models, Multivariate Analysis, Observation
Peer reviewed Peer reviewed
Wilson, Thomas P. – Sociological Methods and Research, 1979
A recent recommendation by Holt (EJ 200 576) that coefficients resulting from estimating log-linear and similar models should not be interpreted is argued to be based on lack of clarity about the substantive and theoretical importance of the choice between dummy and effect coding for categorical variables. (Author/GDC)
Descriptors: Expectancy Tables, Goodness of Fit, Mathematical Models, Probability
Wilcox, Rand R. – 1979
Three separate papers are included in this report. The first describes a two-stage procedure for choosing from among several instructional programs the one which maximizes the probability of passing the test. The second gives the exact sample sizes required to determine whether a squared multiple correlation coefficient is above or below a known…
Descriptors: Bayesian Statistics, Correlation, Hypothesis Testing, Mathematical Models
Lai, Morris K. – 1974
When analysis of variance is used, statistically significant differences may or may not be of practical significance to educators. A large part of the problem is due to the fact that a "zero difference" null hypothesis can always be rejected statistically if the sample size is large enough. If, however, a method based on the noncentral F…
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Mathematical Models
Godbout, Robert C.; And Others – 1977
The problem of spurious significance in multivariate exploratory research is discussed. When a very large number of statistical tests are performed, many tests will be significant on the basis of chance alone. To counter this problem, the use of two sign tests to analyze sets of results has been suggested; the chance expectation [CE test] assesses…
Descriptors: Classroom Research, Educational Experiments, Educational Research, Hypothesis Testing
Magidson, Jay – 1977
In evaluation research studies, it often occurs that several program participants (experimentals) drop out of the program prior to completion. Since noncompleters generally differ substantially from completers in many respects, a control group which originally was representative of the participant group will most likely not be representative of…
Descriptors: Attrition (Research Studies), Career Education, Control Groups, Discriminant Analysis