Development of Short Forms of Scales with Decision Tree Algorithms

Abstract

Scales or surveys are among the measurement tools developed to measure the perceptions of individuals on specified topics. In some cases, the length of these measuring tools may negatively affect the response rates of individuals towards these tools. In this regard, this study aimed to development of short forms of scales by decision trees algorithms. In this way, it can be provided to design short-form measuring tools that perform similar functions and to increase the rate of responses to measuring tools. In the study, predictions were made with decision trees, which are data mining methods. In this context, analyzes were made with decision trees algorithms to obtain short forms of three scales. According to the results obtained, a high level of correlation was found between scales' short and long forms. Thus, it can be concluded that short forms of scales are suitable for measuring similar purposes. Instead of using scales consisting of 40, 20 and 20 items, expected measurements can be made with at least three and 10 items with appropriate tree algorithms for each scale. Among the suggestions of the study, it is possible to carry out similar studies for frequently used scales. So high participation rates for scales can be obtain. Index Terms-short form scale, decision trees, mobile phone problem use, mobile device addiction, attitude towards learning. I. INTRODUCTIONESEARCHERS need to take some measurements in order to get results on the topics they are curious about. Whether in science or social sciences, it is stated that measurements are among the basic activities of science [1]. In all these processes, developed measuring tools can be used. Sometimes, specially developed methods and equipment are used for the measurement process. In the social sciences, the relationships between the structures are wondered [2]. Thus, individuals' perceptions in targeted dimensions can be measured.

Reconstruction of the Taguchi Orthogonal Arrays with the Support Vector Machines Method

“…In this study, in order to reconstruct the L16 (2 15 ) Taguchi array, we formed the classification model by using the support vector machine model which belongs to L4 (2 3 ), L8 (2 7 ), L8 (2 4 ), and L12 (2 11 ) 2-level arrays [21]. During the creation of the model, we also tested with various kernel functions that were used for SVM.…”

“…We applied the model that is obtained for the L16 (2 15 ) array to find the L16 (2 3 4 4 ) mixed-level orthogonal array. To find the array L16 (2 3 4 4 ), we classified the arrays L16 (2 6 4 3 ), L16 (2 9 4 2 ), L16 (2 12 4 1 ), L8 (2 4 4 1 ), but we could not achieve satisfactory results.…”

“…It is also an effective method to use decision trees with SVM to solve multi-class problems. Decision trees are preferred to determine measurement performances and make predictions [15]. The algorithms are used in SVM applications are very successful in classification problems.…”

“…If one of these factors affects two and seven of them at three levels, 2x3 7 = 4.347 experiments should be performed to reach optimum values. If there are 15 parameters per three levels in this instance, 3 15 = 14.348.907 distinct experiments are required. Taguchi's orthogonal array method is the solution in this type of experiment designs in which applying is practically difficult.…”

“…In this study, we aimed to regenerate the L16 (2 15 ) Taguchi array using support vector machines methods, based on twolevel L4, L8, L12 arrays which are found in the Taguchi arrays catalog. We compared the array that we obtained and the original array with a sample experiment.…”