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What is the “Meaning of School†to High School Students? A Scale Development and Implementation Study Based on IRT and CTT

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  • Çetin Toraman
  • GüneÅŸ Korkmaz

Abstract

Understanding education and schooling has always been of great importance for researchers and for the society in digital era. The changing conditions and ever-growing situations in education have also impacted on the meaning that the students attribute to schools, especially after the pandemic. This study aims to develop a measurement tool that will provide information about the meaning of school for high school students and make comparisons on the basis of some sociodemographic variables using this tool. About 6,453 students studying in various types of high schools participated in the research. The data were randomly divided into two data files. Exploratory factor analysis, Item response theory analysis, and reliability analyses (Cronbach’s Alpha and McDonald’s omega) were performed to determine the construct validity of the scale from the first data file created with 1,940 high school students. Confirmatory factor analysis was used to confirm the structure from the second data file created by 1,898 high school students. From the total data file, analyses were conducted with students’ gender, grades, school type, the field of study/major, mother and father’s level of education, and family income. The results reveal that most of the students attribute a positive meaning to the school in condition that they are female and study in early grades. In addition, if they are not successful enough, if they were not provided quality education in science, if their parents’ education level and family income is low, they have positive views about school.

Suggested Citation

  • Çetin Toraman & GüneÅŸ Korkmaz, 2023. "What is the “Meaning of School†to High School Students? A Scale Development and Implementation Study Based on IRT and CTT," SAGE Open, , vol. 13(3), pages 21582440231, September.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231199054
    DOI: 10.1177/21582440231199054
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    References listed on IDEAS

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    1. Theocharis Kromydas, 2017. "Rethinking higher education and its relationship with social inequalities: past knowledge, present state and future potential," Palgrave Communications, Palgrave Macmillan, vol. 3(1), pages 1-12, December.
    2. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    3. William Strout, 1990. "A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation," Psychometrika, Springer;The Psychometric Society, vol. 55(2), pages 293-325, June.
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