{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T19:52:15Z","timestamp":1694634735613},"reference-count":33,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2009,2,20]],"date-time":"2009-02-20T00:00:00Z","timestamp":1235088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,2,20]]},"abstract":"Purpose<\/jats:title>The purpose of this paper is to explore teachers' behaviours in completing an internet survey using decision trees. Furthermore, to reduce the complexity of the decision trees, a statistical technique was used to decrease the number of input variables in the decision trees.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>A dataset of 47,647 samples was used to build the decision trees. These samples were collected from an internet survey of teachers in Taiwan. The output of the decision trees was the answering time (the time taken to complete the internet questionnaire). Eight variables were selected as the inputs for the decision trees. Two techniques were employed to build the decision trees \u2013 the exhaustive chi\u2010squared automatic interaction detector (ECHAID) and classification and regression tree (CRT) analysis. To reduce the complexity of the decision models, factor analysis technique was used to decrease the data dimensions (number of input variables) and to obtain a simplified decision model. One\u2010way ANOVA was used to validate the effects of the dimension reduction.<\/jats:p><\/jats:sec>Findings<\/jats:title>From the results of the factor analysis, a simplified decision tree is recommended using four input variables \u2013 teaching years, school level, sex and area. The classification accuracy of the simplified model is statistically equivalent to that of the original one, which used eight input variables.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>The complexity of decision trees theoretically depends on the number of input variables. This study used a statistical technique to decrease the number of input variables and thereby reduce the complexity of the decision trees. A statistical technique was employed to validate that the classification accuracy is not statistically different between the original decision model and the simplified one. The decision models proposed in this paper can be applied in estimating the answering time for completing a questionnaire during an internet survey.<\/jats:p><\/jats:sec>","DOI":"10.1108\/14684520910944427","type":"journal-article","created":{"date-parts":[[2009,2,22]],"date-time":"2009-02-22T05:03:11Z","timestamp":1235278991000},"page":"117-134","source":"Crossref","is-referenced-by-count":5,"title":["Behaviour analysis of internet survey completion using decision trees"],"prefix":"10.1108","volume":"33","author":[{"given":"Che\u2010Chern","family":"Lin","sequence":"first","affiliation":[]},{"given":"Hung\u2010Jen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Lung\u2010Hsing","family":"Kuo","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020219575014700_b1","doi-asserted-by":"crossref","unstructured":"Boyer, K.H., Olson, J.R., Calantone, R.J. and Jackson, E.C. 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