The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines.
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 unbias...
| Publicado en: | Iranian Journal of Public Health Vol. 39; no. 4; pp. 51 - 64 |
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| Autor principal: | |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Tehran University of Medical Sciences
2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=58650941&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 58650941 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22516085 6N6F jtl: Iranian Journal of Public Health issn: 22516085 maglogo: N pubinfo: dt: 2010 vid: 39 iid: 4 pid: 21783 pub: Tehran University of Medical Sciences artinfo: ui: 58650941 58650941 104826086 58650941 ppf: 51 ppct: 13 formats: fmt: @attributes: type: P tig: atl: The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines. aug: au: Kayri, M. affil: Dept. of Computer and Instructional Technology Education, Educational Faculty, Yuzuncu Yil University, Van, Turkey sug: subj: Internet Addiction Human Turkiye Data Analysis Software Nonparametric Statistics Regression Descriptive Statistics Scales ab: 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. Conclusion: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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