title: The analysis of internet addiction scale using multivariate adaptive regression splines.
abstract: BACKGROUND
  Determining real effects on internet dependency is too crucial with unbiased and robust statistical method. MARS is a new non-parametric method in use in the literature for parameter estimations of cause and effect based research. MARS can both obtain legible model curves and make unbiased parametric predictions.
  
  
  METHODS
  In order to examine the performance of MARS, MARS findings will be compared to Classification and Regression Tree (C&RT) findings, which are considered in the literature to be efficient in revealing correlations between variables. The data set for the study is taken from "The Internet Addiction Scale" (IAS), which attempts to reveal addiction levels of individuals. The population of the study consists of 754 secondary school students (301 female, 443 male students with 10 missing data). MARS 2.0 trial version is used for analysis by MARS method and C&RT analysis was done by SPSS.
  
  
  RESULTS
  MARS obtained six base functions of the model. As a common result of these six functions, regression equation of the model was found. Over the predicted variable, MARS showed that the predictors of daily Internet-use time on average, the purpose of Internet-use, grade of students and occupations of mothers had a significant effect (P< 0.05). In this comparative study, MARS obtained different findings from C&RT in dependency level prediction.
  
  
  CONCLUSIONS
  The fact that MARS revealed extent to which the variable, which was considered significant, changes the character of the model was observed in this study.
authors.affiliation: Dept. of Computer and Instructional Technology Education, Educational Faculty, Yuzuncu Yil University, Van, Turkey.
authors.forename: M
authors.initials: M
authors.lastname: Kayri
keywords: Internet addiction; Linear correlation; MARS; Piecewise function