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Leister, K. Dawn – 1996
Commonality analysis is a method of partitioning variance that has advantages over more traditional "OVA" methods. Commonality analysis indicates the amount of explanatory power that is "unique" to a given predictor variable and the amount of explanatory power that is "common" to or shared with at least one predictor…
Descriptors: Analysis of Variance, Correlation, Heuristics, Power (Statistics)
Mittag, Kathleen Cage – 1993
Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…
Descriptors: Correlation, Factor Analysis, Heuristics, Mathematical Models
Rowell, R. Kevin – 1991
In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…
Descriptors: Equations (Mathematics), Estimation (Mathematics), Heuristics, Mathematical Models
Daniel, Larry G. – 1990
A small multivariate data set is used to illustrate the usefulness of structure coefficients when interpreting results of educational experiments. Data are analyzed using a multivariate analysis of variance (MANOVA), and results are interpreted in three different ways to determine the contribution of individual variables to prediction: (1) using…
Descriptors: Analysis of Variance, Educational Research, Heuristics, Multivariate Analysis
Grabinger, R. Scott – Performance and Instruction, 1988
This introductory article defines and delimits expert systems. The discussion covers the concepts of artificial intelligence, the components of an expert system, and the significance of expert systems when compared to more traditional decision making tools. (CLB)
Descriptors: Algorithms, Decision Making, Expert Systems, Heuristics

Broadhead, Glenn J.; Wright, Richard R. – Journal of Advanced Composition, 1986
Argues that problem-solving assignments whose only purpose is to train students to report technical details ignore much of the real-world context of a problem. Presents an approach to give students a broader sense of problem solving. (MS)
Descriptors: Audience Awareness, Heuristics, Higher Education, Problem Solving

Tversky, Amos; Kahneman, Daniel – Psychological Review, 1983
Judgments under uncertainty are often mediated by intuitive heuristics that are not bound by the conjunction rule of probability. Representativeness and availability heuristics can make a conjunction appear more probable than one of its constituents. Alternative interpretations of this conjunction fallacy are discussed and attempts to combat it…
Descriptors: Cognitive Processes, Error Patterns, Evaluative Thinking, Heuristics

Smith, Rochelle – College English, 1984
Presents writing as a form of dialog and the paragraph as a unit of implied dialog. (MM)
Descriptors: Coherence, Heuristics, Paragraph Composition, Writing (Composition)
Hyslop-Margison, Emery J. – 2000
This paper proposes that the current interest in critical thinking is based on important conceptual, epistemological, and procedural confusions. It suggests that the attempt to identify a successful critical thinking construct mirrors the search for miracle tonics often peddled by snake oil salesmen as a medicinal cure-all. It goes to suggest that…
Descriptors: Critical Thinking, Educational Theories, Elementary Secondary Education, Heuristics
Henard, David H. – 1998
The important and sometimes difficult-to-grasp concept of regression suppressor variable effects is explored. An inquiry into the phenomenon of suppressor effects is accomplished via a synthesis of the existing literature and the use of a small heuristic data set to improve the accessibility of the concept. Implications for researchers are also…
Descriptors: Heuristics, Multiple Regression Analysis, Predictive Measurement, Predictor Variables

Rigdon, Edward E. – Structural Equation Modeling, 1998
Continuing a discussion of the topic of fit assessment in structural equation modeling, this article accepts the compromise proposed by H. Marsh (1998) and offers tentative heuristic models for interpreting fit indices that involve the new baseline model proposed by E. Rigdon (1998). (SLD)
Descriptors: Comparative Analysis, Correlation, Goodness of Fit, Heuristics

Keyes, Tim K.; Levy, Martin S. – Journal of Educational and Behavioral Statistics, 1997
H. Levene (1960) proposed a heuristic test for heteroscedasticity in the case of a balanced two-way layout, based on analysis of variance of absolute residuals. Conditions under which design imbalance affects the test's characteristics are identified, and a simple correction involving leverage is proposed. (SLD)
Descriptors: Analysis of Variance, Heuristics, Power (Statistics), Research Design

Umesh, U. N.; And Others – Educational and Psychological Measurement, 1989
An approach is provided for calculating maximum values of the Kappa statistic of J. Cohen (1960) as a function of observed agreement proportions between evaluators. Separate calculations are required for different matrix sizes and observed agreement levels. (SLD)
Descriptors: Equations (Mathematics), Evaluators, Heuristics, Interrater Reliability

Kaufer, David S.; Steinberg, Erwin R. – College Composition and Communication, 1988
Offers a heuristic for writers to appraise the relative value of information in texts as an aid to revising. Uses the example of noun strings versus prepositional phrases. (SR)
Descriptors: Heuristics, Higher Education, Revision (Written Composition), Teaching Methods

van der Meij, Hans; Carroll, John M. – Technical Communication: Journal of the Society for Technical Communication, 1995
Presents an overview of principles and heuristics for designing minimalist instruction, with examples and theoretical or empirical arguments. Provides a starting point from which to create minimalist instruction to suit a variety of uses. (SR)
Descriptors: Heuristics, Higher Education, Instructional Design, Instructional Effectiveness