Mitchell Karpman
Biographic Data
| ID | 10847284 |
|---|---|
| NAME | Mitchell Karpman |
| GIVEN NAMES | Mitchell |
| FAMILY NAME | Karpman |
| SIGNATURE | KARPMAN M |
| AFFILIATIONS | University of Maryland, College Park |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1980 |
| LATEST PUBLICATION YEAR | 1986 |
| H-INDEX | 0 |
Comparing Two Non-Parallel Regression Lines with the Parametric Alternative to Analysis of Covariance Using SPSS-X or SAS—The Johnson-Neyman Technique
The Johnson-Neyman (J-N; 1936) technique is a parametric alternative to analysis of covariance that permits non-parallel regression lines. This article presents computer programs for J-N using the transformational languages of SPSS-X and SAS. The programs are designed for two groups and one covariate
The Johnson-Neyman Technique Using SPSS or BMDP
When homogeneity of slopes, an assumption of analysis of covariance is not present, the Johnson-Neyman technique has been considered as an alternative to analysis of covariance. The limited use of the Johnson-Neyman technique is perhaps related to exclusion of an algorithm in BMDP or SPSS, two major statistical packages. The present paper describes how to apply the Johnson-Neyman technique for one or two covariates using SPSS and/or BMDP
Cohen's Set Correlation
Cohen (1982) incorporated two symmetric measures of bimultivariate covariation that are bounded by zero and one. Three distinct partialling models (partial, semipartial, and bipartial) were considered in addition to the general condition of two sets of variables. Partial, semipartial, and bipartial set correlation was explained. Although a series of matrix operations would generate the respective measures of bi-multivariate covariation for the th…
Stagewise Multivariate Linear Regression Using Spss or BMDP
The multivariate test statistic for multivariate regression may be computed using BMDP6M or SPSS CANCORR. A stagewise multivariate regression procedure is described which is equivalent to the multivariate extension of testing the semi-partial correlation coefficient
Ancova—A One Covariate Johnson-Neyman Algorithm
This program calculates the homogeneity of regression test, analysis of covariance test or the Johnson-Neyman technique for a one-way (unequal n) design
Disc—An Algorithm for Adjusting T2 Under the Condition of Heterogeneity of Covariance Matrices
This program calculates the adjusted T 2 statistic and univariate unequal variance t statistics when unequal dispersion matrices are present for two groups
No prominent works on this page.
Ancova—A One Covariate Johnson-Neyman Algorithm
This program calculates the homogeneity of regression test, analysis of covariance test or the Johnson-Neyman technique for a one-way (unequal n) design
Disc—An Algorithm for Adjusting T2 Under the Condition of Heterogeneity of Covariance Matrices
This program calculates the adjusted T 2 statistic and univariate unequal variance t statistics when unequal dispersion matrices are present for two groups
Stagewise Multivariate Linear Regression Using Spss or BMDP
The multivariate test statistic for multivariate regression may be computed using BMDP6M or SPSS CANCORR. A stagewise multivariate regression procedure is described which is equivalent to the multivariate extension of testing the semi-partial correlation coefficient
The Johnson-Neyman Technique Using SPSS or BMDP
When homogeneity of slopes, an assumption of analysis of covariance is not present, the Johnson-Neyman technique has been considered as an alternative to analysis of covariance. The limited use of the Johnson-Neyman technique is perhaps related to exclusion of an algorithm in BMDP or SPSS, two major statistical packages. The present paper describes how to apply the Johnson-Neyman technique for one or two covariates using SPSS and/or BMDP
Cohen's Set Correlation
Cohen (1982) incorporated two symmetric measures of bimultivariate covariation that are bounded by zero and one. Three distinct partialling models (partial, semipartial, and bipartial) were considered in addition to the general condition of two sets of variables. Partial, semipartial, and bipartial set correlation was explained. Although a series of matrix operations would generate the respective measures of bi-multivariate covariation for the th…
Comparing Two Non-Parallel Regression Lines with the Parametric Alternative to Analysis of Covariance Using SPSS-X or SAS—The Johnson-Neyman Technique
The Johnson-Neyman (J-N; 1936) technique is a parametric alternative to analysis of covariance that permits non-parallel regression lines. This article presents computer programs for J-N using the transformational languages of SPSS-X and SAS. The programs are designed for two groups and one covariate
Mathematics (6 works) · Statistics (6 works) · Computer Science (5 works) · Econometrics (5 works) · Analysis of covariance (4 works) · Covariance (4 works) · Advanced Statistical Methods and Models (3 works) · Covariate (3 works) · Regression analysis (3 works) · Algorithm (2 works)