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首页> 外文期刊>Iranian journal of public health. >The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines
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The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines

机译：基于多元自适应回归样条的网络成瘾量表分析

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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.
机译：背景：使用无偏且强大的统计方法确定对互联网依赖的实际影响至关重要。 MARS是文献中使用的一种新的非参数方法，用于基于因果关系的研究的参数估计。方法：为了检查MARS的性能，将MARS的发现与分类和回归树（C＆RT）的发现进行比较，在文献中认为MARS的发现可以有效地揭示出MARS的性能。变量之间的相关性。该研究的数据集来自“互联网成瘾量表”（IAS），该表试图揭示个人的成瘾水平。该研究的人口包括754名中学生（301名女性，443名男性学生，缺少10个数据）。使用MARS 2.0试用版通过MARS方法进行分析，并通过SPSS完成C＆RT分析。结果：MARS获得了该模型的六个基本功能。作为这六个函数的共同结果，找到了模型的回归方程。在预测变量上，MARS表明，平均每天的互联网使用时间，互联网使用的目的，学生的学习成绩和母亲的职业等因素具有显着影响（P <0.05）。在这项比较研究中，MARS从C＆RT的依赖水平预测中获得了不同的结论。结论：在这一研究中，观察到了MARS揭示了变量（被认为是重要的）改变模型特征的程度。

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