Logistic regression for risk factor modelling in stuttering research

ObjectivesTo outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering.DesignThe principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed.ResultsThe way the statistical procedures are employed are demonstrated with some hypothetical data.ConclusionResearch into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome.Educational objectives: After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression.

► Gives the reader a simple, practical but comprehensive introduction to logistic regression analysis.
► Discusses applications of logistic regression analysis to stuttering and makes some new suggestions.
► Provides background information to allow evaluation of research that uses logistic regression and related techniques.
► Gives guidance on the design of studies to assess issues in stuttering research where logistic regression is suitable.