Prediction of secondary school students' environmental attitudes by a logistic regression model

Atik A. D., Isildar G. Y., Erkoç F.

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, vol.24, no.3, pp.4355-4370, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1007/s10668-021-01618-3
  • Journal Name: ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, PASCAL, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Business Source Elite, Business Source Premier, CAB Abstracts, Geobase, Greenfile, Index Islamicus, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.4355-4370
  • Keywords: Environmental attitudes, Secondary school students, Logistic regression approach, BEHAVIOR, URBAN, ASSOCIATION, MOTIVATIONS, PERCEPTION, PROTECTION
  • Gazi University Affiliated: Yes

Abstract

This paper explores some variables and impacts affecting secondary school students' environmental attitudes. The Environmental Attitude Scale was used as a data collection tool. The participants consisted of 1794 students attending nine secondary schools with all grades in urban and rural places in five provinces. Students' place of residence, gender, having a pet or growing a plant, school type, family income, discussing environmental issues at home, and grade were all tested using binary logistic regression analysis to create a model that predicted their environmental attitudes. Among the variables included in the logistic regression model regarding secondary school students' environmental attitudes, gender, having a pet or growing plant, school type, family income, discussing environmental issues at home, and grade significantly predict their attitudes. The classification of students' low or high with the logistic regression model is examined; the accurate classification rate is 60.9.