WorldCat Identities

Rowell, R. Kevin

Overview
Works: 5 works in 5 publications in 1 language and 21 library holdings
Roles: Author
Classifications: KFA4114.D79,
Publication Timeline
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Most widely held works by R. Kevin Rowell
Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results by R. Kevin Rowell( Book )

1 edition published in 1991 in English and held by 1 WorldCat member library worldwide

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 discussed, using heuristic and actual research data to illustrate a non-generalizable outcome and a generalizable outcome. The procedure involves the use of two samples or subsamples to produce two pairs of regression equations from which respective shrinkages can be determined. The more closely the shrinkage estimates approach zero, the greater the degree of stability across the subsamples and the more confidence the researcher can vest in the replicability of the results. The first example uses a heuristic data set of 20 (2 subsets of 11 and 9 subjects, respectively) with 5 predictors to illustrate that weights must be compared empirically rather than subjectively. In the second example, data are drawn from a study of the life satisfaction of 200 nursing home residents. Seven tables present study data. An appendix contains the Statistical Analysis System program used to analyze the data. (Sld)
The Influence of Career Indecision on the Strong Interest Inventory and the Self-Directed Search: A Pilot Study by R. Kevin Rowell( Book )

1 edition published in 1999 in English and held by 1 WorldCat member library worldwide

A pilot study was conducted with 48 adults to determine if career indecision/dissatisfaction as indicated by flat Strong Interest Inventory (sii) (L. Harmon, J. Hansen, F. Borgen, and A. Hammer, 1994) profiles corresponded with flat profiles on the Self-Directed Search (sds) and to determine if indecision affected scores on sii Personal Style scales and on achievement. There was significant agreement between flat and elevated profiles on the sii and on the sds. Multiple regression analysis found that several of the si General Occupational Theme scales predicted scores on the sii Personal Style scales. There were, however, no meaningful differences in Personal Style mean scores between people experiencing career indecision/dissatisfaction represented by flat and elevated profiles on the sii, nor were there meaningful differences on Wide Range Achievement Test-3 (G. Wilkinson, 1993) scale means. Spelling achievement was related to Learning Environment on the sii Personal Style Scale. Future directions for study are provided. Contains 10 references. (Author/SLD)
Testing for Homogeneity of Slopes in Analysis of Covariance: A Tutorial by Kelli D Poremba( Book )

1 edition published in 1997 in English and held by 1 WorldCat member library worldwide

Although an analysis of covariance (ancova) allows for the removal of an uncontrolled source of variation that is represented by the covariates, this "correction," which occurs with the dependent variable scores is unfortunately seen by some as a blanket adjustment device that can be used with an inadequate amount of consideration for the homogeneity of slopes assumption. When regression slopes are found not to be parallel, treatment effects will most likely be biased, and there will be a reduction in the efficiency of the analysis. Twenty heuristic data sets coupled with analysis of variance and ancova analyses are provided to illustrate what may occur when the homogeneity of slopes requirement is not met. Even though each of the groups had identical means, variations in the distribution of data for one of the groups studies led to varying slopes. Consequently, three different ancova values resulted, only one of which was accurate. It should be noted that the homogeneity of slopes assumption can be violated to some degree without seriously affecting the robustness of tests of significance in ancova. (Contains 6 tables, 23 figures, and 3 references.) (Author/SLD)
Partitioning Predicted Variance in to Constituent Parts: How to Conduct Commonality Analysis by R. Kevin Rowell( Book )

1 edition published in 1991 in English and held by 1 WorldCat member library worldwide

This paper explains how commonality analysis (ca) can be conducted using a specific Statistical Analysis System (sas) procedure and some simple computations. Ca is used in educational and social science research to partition the variance of a dependent variable into its constituent predicted parts. Ca determines the proportion of explained variance that is unique to a predictor variable and the proportion that is common to two or more predictors. Whereas the ordering of the predictors using stepwise regression may lead to faulty data interpretations, ca is a method by which all possible predictor combinations are tested to determine the model that best explains predicted variance. Data from a study of life satisfaction (ls) among 198 elderly residents in 17 Texas nursing homes illustrate procedures for conducting ca with regression results. The subjects completed a ls questionnaire to determine if their self-reports of ls differed from those of the elderly living outside of nursing homes. Eight subscale components and the number of years in the nursing home were analyzed by regression to determine which variable best predicted nursing home satisfaction. Meaning was the dominant factor in predicting nursing home satisfaction and accounted for about 80% of all explained variance in the sample. In addition, a sas computer program for obtaining all possible R-squared values is discussed as an efficient method of implementing the required analyses. Ca offers a fairly straightforward method of analysis when no more than four independent variables are of interest. Three tables of data are presented, and the R-squares of ls scales are included. (Sld)
 
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