Use of linear multiple regression analysis on dental survey data.
The use of multiple regression analysis in dental research has increased greatly in recent years. However, in most cases insufficient or no details are given to assess the validity of the results obtained through this statistical approach. Since multiple regression is the method of choice when dealing with three or more variables, provided the assumptions are fulfilled, this study reports the use of this technique on caries data from a random sample of schoolchildren from Andalucia, Spain. Linear multiple regression was used to analyse caries experience, expressed as dmft or DMFT, a quantitative dependent variable. The explanatory or independent variables considered for each age group were gender, type of area, social class and province, following the basic survey method recommended by the World Health Organization. Laborious data transformation procedures had to be employed to comply with necessary assumptions for this statistical technique. The difficulties encountered may be partly due to the fact that all the independent variables considered in the study were categorical or qualitative variables. In the process of ensuring valid results, several helpful statistics were identified. In addition, statistically significant influences on the dependent variable studied were easily detected, as well as the order of importance of the independent variables included in the model. However, the complex data transformation procedures required to validate assumptions in this study suggested that in some instances it could prove extremely difficult to use linear multiple regression on survey data.
Salas-Wadge
M H
MH
Department of Dental Health Policy, Eastman Dental Institute, London, UK.
D000293:Adolescent; D000704:Analysis of Variance; D002648:Child; D004245:DMF Index; D003731:Dental Caries / Q000453:epidemiology*; D003753:Dental Health Surveys; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D015203:Reproducibility of Results; D012307:Risk Factors; D012423:Rural Health / Q000706:statistics & numerical data; D012737:Sex Factors; D012923:Social Class; D013030:Spain / Q000453:epidemiology; D014504:Urban Health / Q000706:statistics & numerical data