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Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Peer reviewed Peer reviewed
Hsu, Louis M. – Journal of Counseling Psychology, 1989
Discusses three topics related to interpretation of discriminant analyses (DA's): (1) partial F ratios and partial Wilks's lambdas for predictor variables in standard, step-down, and stepwise DA's; (2) relation of goals of classification to definition/evaluation of classification rules; and (3) significance tests for total hit rates in internal…
Descriptors: Data Interpretation, Discriminant Analysis, Multivariate Analysis, Predictor Variables
Peer reviewed Peer reviewed
Krishnan, Parmeswara – Alberta Journal of Educational Research, 1995
Comments on some methodological limitations of the research base of "The Bell Curve": blind use of the normal distribution (bell curve); avoidance of nonnormal statistical distributions, which are more appropriate for some social and economic characteristics; copious use of percentiles and quintiles, inappropriate with nonnormal…
Descriptors: Data Interpretation, Intelligence Quotient, Multivariate Analysis, Research Methodology
Jarrell, Michele E.; And Others – 1989
Procedures used in Doctor of Education (Ed.D.) dissertations at the University of Alabama (Tuscaloosa) were studied. Focus was on identifying: (1) characteristics of the research designs used; (2) sources of the instruments used to collect data; (3) reports of reliability estimates and evidence of validity of the instruments; and (4) types of…
Descriptors: Comparative Analysis, Data Interpretation, Doctoral Dissertations, Doctoral Programs
Peer reviewed Peer reviewed
Beck, E. M.; Tolnay, Stewart E. – Historical Methods, 1995
Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)
Descriptors: Data Interpretation, Evaluation Criteria, Higher Education, Multivariate Analysis