Identifying the Factors that Influence Change in SEBD Using Logistic Regression Analysis

Abstract

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent variables. The seminal contribution of John Nelder and Robert Wedderburn (1972) introduced the concept of Generalized Linear Models. GLMs overcome the limitations of Normal regression models and accommodate any distribution which is a member of the exponential family. Moreover, these models relate the dependent variable to the linear predictor (non-random component) through any invertible link function. Logistic regression models are GLMs that accommodate categorical dependent variables. They assume a Binomial distribution and Logit canonical link function. The iteratively re-weighted least squares algorithm using the Fisher scoring technique is employed to maximize the log-likelihood function in GLMs and estimate the model parameters. In this paper, Logistic regression analysis was used to identify the dominant factors that influence change in social, emotional and behaviour difficulties (SEBD) of Maltese children. The study comprised 486 pupils whose SEBD was assessed by both teachers and parents using the Strengths and Difficulties Questionnaire (Goodman 1997) when the children were aged 6 and 9 years old.