MALG.FOR is a Microsoft FORTRAN 77 program that provides tests of significance between dependent zero-order and part correlations, part correlations with different predictors and covariates, part correlations with the same predictor and different covariates, and part correlations with different predictors and the same covariate. For each application, parameter estimates are calculated via four sets of algorithms. These algorithms are based on Malgady's modifications of Hotelling's t, Williams's modification of Hotelling's t, Olkin's z test, and Meng, Rosenthal, and Rubin's normal curve Z test, respectively.
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References
1.
Cohen, J. , & Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.
2.
Conger, A. J. , & Jackson, D. N. (1972). Suppressor variables, prediction, and the interpretation of psychological relationships. Educational and Psychological Measurement, 32579-599.
3.
Dunlap, W. P. , & Duffy, J. A. (1975). FORTRAN IV functions for calculating exact probabilities associated withz, chi-square, t, and F values. Behavior Research Methods and Instrumentation, 7, 59-60.
4.
Dunn, O. J. , & Clark, V. A. (1969). Correlation coefficients measured on the same individuals. Journal of the American Statistical Association, 64366-377.
5.
Dunn, O. J. , & Clark, V. A. (1971). Comparison of tests of the equality of dependent correlation coefficients. Journal of the American Statistical Association, 66904-908.
6.
Ferguson, G. A. (1976). Statistical analysis in psychology & education (4th ed.). New York: McGraw-Hill.
7.
Glass, G. V , & Stanley, J. C. (1970). Statistical methods in education and psychology. Englewood Cliffs, NJ: Prentice Hall.
8.
Hendrickson, G. F. , & Collins, J. R. (1970). Note correcting the results in "Olkin's new formula for the significance of r13 vs. r23 compared with Hotelling's method."American Educational Research Journal, 7639-641.
9.
Hendrickson, G. F. , Stanley, J. C., & Hills, J. R. (1970). Olkin's new formula for significance of r13 vs. r23 compared with Hotelling's method. American Educational Research Journal, 7189-195.
10.
Hittner, J. B. (1992). Sense of coherence and the neuroticism confound: Questions regarding discriminant validity. Poster presented at the annual convention of the Society of Behavioral Medicine, New York.
11.
Hotelling, H. (1940). The selection of variates for use in prediction, with some comments on the general problem of nuisance parameters. Annals of Mathematical Statistics, 11271-283.
12.
Malgady, R. G. (1987). Contrasting part correlations in regression models. Educational and Psychological Measurement, 47961-965.
13.
McNemar, Q. (1969). Psychological statistics (4th ed.). New York: Wiley.
14.
Meng, X. L. , Rosenthal, R., & Rubin, D. B. (1992). Comparing correlated correlation coefficients. Psychological Bulletin, 111172-175.
15.
Olkin, I. (1967). Correlations revisited. In J. C. Stanley (Ed.), Improving experimental design and statistical analysis (pp. 102-128). Chicago: Rand McNally.
16.
OLkin, I. , & Siotani, M. (1964). Asymptotic distribution of functions of a correlation matrix (Tech. Rep. No. 6). Stanford, CA: Stanford University, Laboratory for Quantitative Research in Education.
17.
Pavot, W. , & Diener, E. (1993). Review of the Satisfaction With Life Scale. Psychological Assessment, 5164-172.
18.
Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction (2nd ed.). New York: Holt, Rinehart & Winston.
19.
Silver, N. C. , & Dunlap, W. P. (1987). Averaging correlation coefficients: Should Fisher's z transformation be used?Journal of Applied Psychology, 72146-148.
20.
Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87245-251.
21.
Williams, E. J. (1959). Significance of difference between two non independent correlation coefficients. Biometrics, 15135-136.
