Abstract
Optimal scaling is a method designed to optimize the statistical power of the relationship between the predictor and outcome variables. It makes use of processes like discretization (converting continuous variables into discretized values), and regularization (correcting discretized variables for overfitting, otherwise called overdispersion). The current chapter gives examples and shows, that in order to fully benefit from optimal scaling a regularization procedure is important. This chapter also addresses automatic linear regression in SPSS. It provides much better statistics of regression data than traditional multiple linear regression does. Optimal scaling is a major contributor to the benefits of the automatic linear regression module in SPSS statistical software. We conclude, that optimal scaling using discretization, is a method for an improved analysis of clinical trials, where the consecutive levels of the variables are unequal. In order to fully benefit from optimal scaling, a regularization procedure for the purpose of correcting overdispersion is desirable.
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To readers requesting more background, theoretical and mathematical information of computations given, several textbooks complementary to the current production and written by the same authors are available: Statistics applied to clinical studies 5th edition, 2012, Machine learning in medicine a complete overview 2nd edition, 2020, SPSS for starters and 2nd levelers 2nd edition, 2015, Clinical data analysis on a pocket calculator 2nd edition, 2016, Understanding clinical data analysis, 2017, all of them edited by Springer Heidelberg Germany.
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Cleophas, T.J., Zwinderman, A.H. (2021). Optimal Scaling and Automatic Linear Regression. In: Regression Analysis in Medical Research. Springer, Cham. https://doi.org/10.1007/978-3-030-61394-5_16
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DOI: https://doi.org/10.1007/978-3-030-61394-5_16
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