TY - JOUR AU - Gatwiri Winniejoy Nkonge AU - Dr. Mukirae Njihia AU - Dr. John Ndiritu PY - 2020/11/05 Y2 - 2024/03/28 TI - STUDENT’S HOUSEHOLD CHARACTERISTICS HIERARCHICAL REGRESSION MODEL PREDICTING PUBLIC DAY SECONDARY SCHOOLS VARIATION IN EXAMINATION SCORES, KENYA JF - African Journal of Emerging Issues JA - AJOEI VL - 2 IS - 12 SE - Articles DO - UR - https://ajoeijournals.org/sys/index.php/ajoei/article/view/146 AB - 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.  ER -