IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v13y2023i3p21582440231199054.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440231199054
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440231199054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Piotr Koc, 2021. "Measuring Non-electoral Political Participation: Bi-factor Model as a Tool to Extract Dimensions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 271-287, July.
    2. Jules Ellis & Arnold Wollenberg, 1993. "Local homogeneity in latent trait models. A characterization of the homogeneous monotone irt model," Psychometrika, Springer;The Psychometric Society, vol. 58(3), pages 417-429, September.
    3. Edward Ip, 2000. "Adjusting for information inflation due to local dependency in moderately large item clusters," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 73-91, March.
    4. Melissa Gladstone & Gillian Lancaster & Gareth McCray & Vanessa Cavallera & Claudia R. L. Alves & Limbika Maliwichi & Muneera A. Rasheed & Tarun Dua & Magdalena Janus & Patricia Kariger, 2021. "Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan," IJERPH, MDPI, vol. 18(11), pages 1-19, June.
    5. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
    6. Victoria T. Tanaka & George Engelhard & Matthew P. Rabbitt, 2020. "Using a Bifactor Model to Measure Food Insecurity in Households with Children," Journal of Family and Economic Issues, Springer, vol. 41(3), pages 492-504, September.
    7. Cervantes, Víctor H., 2017. "DFIT: An R Package for Raju's Differential Functioning of Items and Tests Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i05).
    8. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    9. Joshua B. Gilbert & James S. Kim & Luke W. Miratrix, 2023. "Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 889-913, December.
    10. Bing Li & Cody Ding & Huiying Shi & Fenghui Fan & Liya Guo, 2023. "Assessment of Psychological Zone of Optimal Performance among Professional Athletes: EGA and Item Response Theory Analysis," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    11. Ick Hoon Jin & Minjeong Jeon, 2019. "A Doubly Latent Space Joint Model for Local Item and Person Dependence in the Analysis of Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 236-260, March.
    12. Michela Gnaldi & Silvia Bacci & Thiemo Kunze & Samuel Greiff, 2020. "Students’ Complex Problem Solving Profiles," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 469-501, June.
    13. Li Cai, 2010. "A Two-Tier Full-Information Item Factor Analysis Model with Applications," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 581-612, December.
    14. Alessandro Chiarotto & Annette Bishop & Nadine E Foster & Kirsty Duncan & Ebenezer Afolabi & Raymond W Ostelo & Muirne C S Paap, 2018. "Item response theory evaluation of the biomedical scale of the Pain Attitudes and Beliefs Scale," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-17, September.
    15. Pierre Canisius Kamanzi & Gaële Goastellec & Laurence Pelletier, 2021. "Mass University and Social Inclusion: The Paradoxical Effect of Public Policies," Social Inclusion, Cogitatio Press, vol. 9(3), pages 32-43.
    16. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    17. Jinming Zhang & William Stout, 1999. "Conditional covariance structure of generalized compensatory multidimensional items," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 129-152, June.
    18. Harald Hruschka, 2021. "Comparing unsupervised probabilistic machine learning methods for market basket analysis," Review of Managerial Science, Springer, vol. 15(2), pages 497-527, February.
    19. Ivo Molenaar, 1998. "Data, model, conclusion, doing it again," Psychometrika, Springer;The Psychometric Society, vol. 63(4), pages 315-340, December.
    20. Margaret Mungai & Damiannah Kieti & Isabella Mapelu, 2021. "Tourism education enrolment among Kenyan public universities: Socioeconomic aspects," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(6), pages 340-348, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231199054. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.