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Peer reviewedSchafer, William D. – Measurement and Evaluation in Counseling and Development, 1992
Discusses problems researchers face when they want to describe relationship between several predictors and criterion variable. Considers ways of addressing problem of contribution of each predictor depending on which other predictors are in regression equation. Focuses on parallel information for each variable, examining initial and final…
Descriptors: Data Analysis, Data Interpretation, Regression (Statistics), Research Problems
Donmoyer, Robert – 1984
This paper addresses a variation of the traditional validity question asked of qualitative researchers. Here the question is not "How do we know the qualitative researcher's question is valid?" but rather, "How does the qualitative researcher choose from among a multitude of apparently valid or at least plausible…
Descriptors: Data Analysis, Epistemology, Research Methodology, Research Problems
Dingman, Harvey F.; Peck, Robert F. – 1968
The reanalysis of data collected in large research projects presents problems, mainly because the hypotheses to be tested may be dependent on the hypotheses of the original project. One possible solution to this problem would be to use the data to group subjects into typologies. (HW)
Descriptors: Classification, Data Analysis, Information Storage, Research Problems
Keeves, John P. – 1974
This paper, presented at the Fifth Annual Conference of the Australian Science Education Research Association in 1974, discusses the various strategies for analyzing the data gathered to explore the effect of specific school variables on educational achievement. The discussion centers around the International Association for the Evaluation of…
Descriptors: Academic Achievement, Data Analysis, Educational Environment, Research Problems
Peer reviewedAuerbach, Carl – Perceptual and Motor Skills, 1971
Descriptors: Data Analysis, Forced Choice Technique, Measurement, Research Problems
Design and Analysis Problems Associated with Qualitative Data in Educational Research. Final Report.
Fienberg, Stephen E.; Larntz, Kinley – 1979
This research project addresses a series of methodological and theoretical statistical problems in the analysis of categorical data using loglinear and logistic response models, which grow directly out of problems in the study of the American educational system, and in basic educational research. The project focuses on the adaptation and…
Descriptors: Data Analysis, Educational Research, Mathematical Models, Research Problems
Glass, Gene V.; And Others – 1980
Integrative analysis, or what is coming to be known as meta-analysis, is the integration of the findings of many empirical research studies of a topic. Meta-analysis differs from traditional narrative forms of research reviewing in that it is more quantitative and statistical. Thus, the methods of meta-analysis are merely statistical methods,…
Descriptors: Data Analysis, Literature Reviews, Research Methodology, Research Problems
Peer reviewedSobel, Suzanne B. – American Psychologist, 1978
This is a critique of an article on the Cambridge Somerville Youth Study. It is noted that although the idea of a 30-year follow-up is good, the experimenter's variables are not strong enough to justify the conclusions drawn. (Author/AM)
Descriptors: Data Analysis, Evaluation Methods, Followup Studies, Measurement Techniques
Peer reviewedWachs, Theodore D. – Child Development, 1987
This study of the stability of parent behaviors toward toddlers over a 3-week period used both aggregated and nonaggregated data. Comparison of stability correlations indicated higher stabilities for aggregated scores, with the level of stability increasing as scores from additional single sessions were aggregated. (PCB)
Descriptors: Data Analysis, Infants, Parent Child Relationship, Parents
Peer reviewedJennings, Earl; Green, Janet L. – Journal of Experimental Education, 1984
The authors argue that a parameterization in terms of cell means is a useful conceptual device for resolving problems inherent in the nonorthogonal analysis of variance. They demonstrate their argument by considering two widely-used methods for testing main effects. (Author/BW)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design
Goodell, John S.
Questionnaires are important research tools despite their unpopularity. They are generally less costly per response than other techniques, may elicit more candid answers, provide more time for respondents to consider their replies, and do not introduce interviewer or observer bias. The rate of return is an indication of the usefulness of a…
Descriptors: Data Analysis, Data Collection, Library Science, Questionnaires
Peer reviewedWeisberg, Herbert I. – Journal of Educational Statistics, 1978
A re-analysis of evaluation data concerning the Emergency School Aid Act shows that findings of the original evaluators are open to multiple interpretations. (JKS)
Descriptors: Compensatory Education, Data Analysis, Elementary Education, Evaluation Methods
Peer reviewedFields, Mitchell W. – Educational Administration Quarterly, 1985
As complex, sophisticated techniques for statistical analysis become easier to obtain and apply, it becomes increasingly critical for researchers to examine their data prior to analysis to ensure that the data are suitable for analysis. Exploratory Data Analysis provides methods for subjecting data to appropriate pre-analysis scrutiny. (PGD)
Descriptors: Data Analysis, Educational Administration, Educational Research, Research Methodology
Peer reviewedWalberg, Herbert J.; And Others – Review of Educational Research, 1984
This paper demonstrates the variety of positive-skew phenomena and discusses their theoretical, research, and practical implications in education. (PN)
Descriptors: Academic Achievement, Data Analysis, Research Problems, Scores
Peer reviewedKoopman, Raymond F. – Psychometrika, 1976
This note proposes an alternative implementation of the regression method which should be slightly faster than the principal components methods for estimating missing data. (RC)
Descriptors: Comparative Analysis, Data Analysis, Factor Analysis, Multiple Regression Analysis


