Full-text resources of CEJSH and other databases are now available in the new Library of Science.

Visit https://bibliotekanauki.pl

Visit https://bibliotekanauki.pl

Full texts:

177-202

published

2022

author

- University of Bojnord, Iran

author

- University of Bojnord, Iran

- Alexander, d. L. J., Tropsha, A. & Winkler, D. A., (2015). Beware of R(2): Simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. Journal of chemical information and modeling, 55, pp. 1316-1322.
- Brown, N., (2005). Graphical illustration of two-predictor suppression effects, v0.05 [Online]. Available: https://steamtraen.shinyapps.io/suppressiongraphics/ [Accessed August 30 2020].
- Cohen, J., Cohen, P., (1975). Applied multiple regression/correlation analysis for the behavioral sciences, Oxford, England, Lawrence Erlbaum.
- Cohen, J., Cohen, P., (1983). Applied multiple regression/correlation analysis for the behavioral sciences, Hillsdale, N.J, Lawrence Erlbaum Associates.
- Cohen, J., Cohen, P., West, S. & Aiken, L., (2003). Applied multiple regression/correlation analysis for the behavioral sciences, Mahwah, NJ, Lawrence Erlbaum Associates.
- Conger, A. J., (1974). A revised definition for suppressor variables: A guide to their identification and interpretation. Educational and Psychological Measurement, 34, pp. 35 -46.
- Conger, A. J., Jackson, D. N., (1972). Suppressor variables, prediction, and the interpretation of psychological relationships. Educational and Psychological Measurement, 32, pp. 579 -599.
- Currie, I., Korabinski, A., (1984). Some comments on bivariate regression. Journal of the Royal Statistical Society: Series D (The Statistician), 33, pp. 283 -293.
- Darlington, R. B., (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161 -182.
- Darlington, R. B., Hayes, A. F., (2017). Regression analysis and linear models: concepts, applications, and implementation, New York, The Guilford Press.
- Fox, J., (1997). Applied regression analysis, linear models, and related methods, Thousand Oaks, CA, US, Sage Publications, Inc.
- Friedman, L., Wall, M., (2005). Graphical views of suppression and multicollinearity in multiple linear regression. The American Statistician, 59, pp. 127 -136.
- Hamilton, D., (1987). Sometimes R 2 > r 2 yx 1 + r 2 yx 2 : Correlated variables are not always redundant. The American Statistician, 41, pp. 129 -132.
- Holling, H., (1983). Suppressor structures in the general linear model. Educational and Psychological Measurement, 43, pp. 1 -9.
- Horst, P., (1941). The prediction of personal adjustment: A survey of logical problems and research techniques, with illustrative application to problems of vocational selection, school success, marriage, and crime, New York, NY, US, Social Science Research Council.
- Kvalseth, T. O., (1985). Cautionary note about R2. The American Statistician, 39, pp. 279 -285.
- Ludlow, L., Klein, K., (2014). Suppressor variables: The difference between ‘is’ versus ‘acting as’. Journal of Statistics Education, 22, null-null.
- Lynn, H. S., (2003). Suppression and confounding in action. The American Statistician, 57, pp. 58 -61.
- Mcfatter, R. M., (1979). The use of structural equation models in interpreting regression equations including suppressor and enhancer variables. Applied Psychological Measurement, 3, 123 -135.
- Meehl, P. E., (1945). A simple algebraic development of Horst's suppressor variables. The American Journal of Psychology, 58, pp. 550 -554.
- Mendershausen, H., (1939). Clearing variates in confluence analysis. Journal of the American Statistical Association, 34, pp. 93 -105.
- Nazifi, M., Fadishei, H., (2021a). Supsim: A Python package simulating two-predictor suppression and non-suppression situations [Online]. Available: https://github.com/fadishei/supsim [Accessed 11 May 2021].
- Nazifi, M., Fadishei, H., (2021b). Supsim: A Novel Computerized Algorithm Simulating Two-Predictor Suppression and Non-Suppression Situations [Online]. Available: https://youtu.be/6K82yDp-fNM [Accessed 10 November 2021].
- Nazifi, M., Fadishei, H., (2021c). Supsim Project [Online]. Available: https://supsim.netlify.app/supsim [Accessed 11 May 2021].
- Neill, J. J., (1973). Tests of the equality of two dependent correlations. Doctoral dissertation, University of California, Ann Arbor, Ann Arbor, MI: University Microfilms No.: 74 -7671.
- Neter, J., Kutner, M., Nuchtsheim, C. & Wasserman, W., (1996). Applied linear statistical models (4th ed), Chicago, Irwin.
- Pedhazur, E. (1997). Multiple regression in behavioral research: Explanation and prediction, Wadsworth, Thomson Learning.
- Sharpe, N. R., Roberts, R. A., (1997). The relationship among sums of squares, correlation coefficients, and suppression. The American Statistician, 51, pp. 46-48.
- Shieh, G., (2001). The inequality between the coefficient of determination and the sum of squared simple correlation coefficients. The American Statistician, 55, pp. 121-124.
- Tzelgov, J., Henik, A., (1991). Suppression situations in psychological research: Definitions, implications, and applications. Psychological Bulletin, 109, pp. 524-536.
- Velicer, W. F., (1978). Suppressor variables and the semipartial correlation coefficient. Educational and Psychological Measurement, 38, pp. 953-958.
- Watson, D., Clark, L. A., Chmielewski, M. & Kotov, R., (2013). The value of suppressor effects in explicating the construct validity of symptom measures. Psychological Assessment, 25, pp. 929 -941.
- Whuber, (2017). Generate a random variable with a defined correlation to an existing variable(s). Stack Exchange Inc.

Biblioteka Nauki

2156990

bwmeta1.element.ojs-doi-10_2478_stattrans-2022-0049