Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Abstract

Abstract. This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1) to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2) to discuss common pitfalls and methodological errors in developing a model, and 3) to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

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“…(1) Model Specifcation. Logistic regression is a statistical method for analysing a dataset in which there are one or more independent variables that determine an outcome (measured with a dichotomous variable) [36]. Accordingly, their general mathematical model was used as follows:…”

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Các nhân tố ảnh hưởng đến rủi ro tài chính: Nghiên cứu điển hình tại các doanh nghiệp vận tải và kho bãi niêm yết

“…Polsiri & Sookhanaphibarn (2009) đã sử dụng mô hình Logit để kiểm định tác động của các biến quản trị đối với khó khăn về tài chính của doanh nghiệp ở Thái Lan. Mô hình này cũng được Sarlija & Jeger (2011) vận dụng để dự báo rủi ro tài chính của các doanh nghiệp tại Croatia. Tại Việt Nam, Nguyen (2018) áp dụng mô hình Logit trong phân tích rủi ro phá sản của các doanh nghiệp bất động sản niêm yết trên HNX và HOSE.…”