STUDENT’S HOUSEHOLD CHARACTERISTICS HIERARCHICAL REGRESSION MODEL PREDICTING PUBLIC DAY SECONDARY SCHOOLS VARIATION IN EXAMINATION SCORES, KENYA
Purpose of the study: The purpose of this study was to use hierarchical regression to model the relationship between student’s household characteristics and variations in examination scores in public day secondary schools Tharaka Nithi County, Kenya.
Problem statement: Tharaka Nithi County is one of the 29 counties classified as the arid and semi-arid lands. In addition to the government of Kenya providing free tuition in the public day secondary schools, it also facilitates lunch costs in arid and semi-arid lands together with mobilizing community to support education through national council for nomadic education. The government efforts aim to ascertain equality of opportunity in attainment of quality secondary education in public day secondary schools. Nevertheless, compared to other public day secondary schools in other Kenyan Counties, Tharaka Nithi County public day secondary schools have had the highest variations in the examinations successively for five years, 2014 – 2018. Thus, raised the question on the relationship between student’s household characteristics and the variations in examination scores in public day secondary schools of Tharaka Nithi County while controlling for the other predictor variables of variations in examination scores.
Research methodology: Convergent parallel design, a mixed method research approach was employed. The study was conducted in the public day secondary schools in Tharaka Nithi County, Kenya. Study target population comprised of all the principals and year 2020 form 3 students and their parents or guardians. Stratified random sampling technique was used to identify 738 (368 male and 370 female) students in form 3 and their parents or guardians while purposive sampling was used to identify 15 public day secondary school principals and 63 student group interview participants (31 male and 32 female). Questionnaires, interview schedules and document analysis sheets were used to collect data. Hierarchical regression was used on the quantitative data analysis to model the relationship between student’s household characteristics and variations in examination scores in public day secondary schools. Further, a case study approach thematic analysis was used on qualitative data to obtain an in-depth knowledge on the model of relationship between student’s household characteristics and variations in examination scores in public day secondary schools. Research findings were presented in tables.
Results of the study: A statistically significant positive relationship, r = 0.662 at p < .01 between student’s household characteristics and variations in student’s examination scores was found. The study findings revealed that variations in students’ examinations scores enlarged by 0.438 of each standard deviation of student’s household characteristics. Subsequently, the study null hypothesis; no statistically significant equation for predicting variations in examination scores from students’ household characteristics was rejected.
Conclusion and policy recommendation: The study concluded that in public day secondary schools in Tharaka Nithi County, there is a relationship between student’s household characteristics and variations in examination scores. Thus, equality of opportunity in attainment of quality secondary education in public day secondary schools in Tharaka Nithi County is not ascertained. The study thus recommends that the financing of the public day secondary schools to take cognizance of the differences in students’ household characteristics.
Keywords: Student’s household characteristics, variations in examination scores, public day secondary schools, equality of opportunity, and hierarchical regression model.
Baker, B., & Levin, J. (2014). Educational equity, adequacy, and equal opportunity in the commonwealth: An evaluation of Pennsylvania’s school finance system. American Institutes for Research. (October), 109.
Cerdeira, J., Nunes, L., Reis, A., & Seabra, M. (2018). Predictors of Student Success in Higher Education: Secondary School Internal Scores versus National Exams. Nova School of Business and Economics, Universidade Nova de Lisboa.
Faught, E., Williams, P., Willows, N., Asbridge, M., & Paul, V. (2017). The association between food insecurity and academic achievement in Canadian school-aged children. Public Health Nutrition, 20(15), 2778–2785. https://doi.org/10.1017/S1368980017001562
Gay, L. (1992). Educational Research: Competence for Analysis and Applications (4th ed.). Macmillan.
Gustafsson, J.-E., Nilsen, T., & Kajsa, H. (2018). School characteristics moderating the relation between student socio-economic status and mathematics achievement in grade 8. Evidence from 50 countries in TIMSS 2011. Studies in Educational Evaluation, 57, 16–30.
Hansen, W. L. (1970). Education and Production Functions. National Bureau of Economic Research. http://www.nber.org/chapters/c3276
Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of Human Resources, 14(351), 88.
Huisman, J., & Smits, J. (2017). Keeping children in school: Effects of household and context characteristics on school dropout in 363 districts of 30 developing countries. Nijmegen Center for Economics (NiCE).
Hungi, N. (2012). Accounting for Variations in the Quality of Primary School Education. UNESCO International Institute for Educational Planning, 44.
International Budget Partnership. (2017). Processes for Financing Public Basic Education in South Africa. Cornerstone Economic Research.
Kariuki, D. (2017). Personal, Family and School Factors As correlates of Achievement Motivation among Form two students in Nairobi County, Kenya. Kenyatta University.
Konow, J., Saijo, T., & Akai, K. (2016). Equity versus Equality. Kiel University, Loyola Marymount University, Kochi Institute of Technology, University of Tokyo, 75376. https://mpra.ub.uni-muenchen.de/75376/
Kyriakides, L., Devine, D., & Papastylianou, D. (2017). Quality and Equity in Education: Theories, Applications and Potentials. Erasmus, 1(1), 5.
Malusa, G. (2017). Equity in educational systems and policies: A difficult social justice choice. Research Gate, 21(47), 86–122.
Patrinos, H., & Psacharopoulos, G. (2020). Chapter 4—Returns to Education in Developing Countries. Academic Press, 53–64.
Psacharopoulos, G., & Patrinos, H. (2018). Returns to investment in education: A decennial review of the global literature. Research Gate, 26(5), 445–458. https://doi.org/10.1080/09645292.2018.1484426
Rakabe, E. (2016). Equitable Resourcing of Schools for Better Outcomes. Brookings Institution Press, 105–130.
Rawls, J. (1999). A Theory of Justice. The Belknap Press of Harvard University Press.
Republic of Kenya. (2009). Revised Policy Framework for Nomadic Education in Kenya. Government Printer.
Republic of Kenya. (2016). School Nutrition and Meals Strategy for Kenya. Government Printer.
UIS. (2018). Handbook on Measuring Equity in Education. UNESCO Institute for Statistics.
UNESCO. (2017). Ensuring Adequate, Efficient and Equitable Finance in Schools in the Asia-Pacific Region. The United Nations Educational, Scientific and Cultural Organization. http://www.unesco.org/open-access/terms-use-ccbysa-en
UNESCO. (2020). Global Education Monitoring Report 2020: Inclusion and education: All means all. (p. 444). UNESCO. https://docs.wfp.org/api/documents/WFP-0000117088/download/?_ga=2.263078348.134665739.1600155685-1777775229.1600155685
Wakwabubi, S., Achoka, J., Shiundu, J., & Ejakait, E. (2016). Students’ Socio-Economic Status and Enrollment in Public Secondary Schools in Kenya. International Journal Advances in Social Science and Humanities, 4(04), 70–80.