A Hierarchical Linear Modelling of Teacher Effects on Academic Achievement in the Kenya Certificate of Primary Education Examination

Epari Ejakait^1, , Maureen Olel², Lucas Othuon³ and Ochanda Khasenye⁴

¹Department of Educational Planning and Management, Masinde Muliro University of Science and Technology, Kakamega, Kenya

²Department of Educational Management and Foundations, Maseno University, Private Bag, Maseno, Kenya

³Department of Educational Psychology, Maseno University, Private Bag, Maseno, Kenya

⁴Department of Environment and Health Sciences, Technical University of Mombasa, P.O Box 90420-80100, GPO, Mombasa, Kenya

Cite this paper:
Epari Ejakait, Maureen Olel, Lucas Othuon and Ochanda Khasenye. A Hierarchical Linear Modelling of Teacher Effects on Academic Achievement in the Kenya Certificate of Primary Education Examination. American Journal of Educational Research. 2016; 4(14):1030-1040. doi: 10.12691/education-4-14-8

Abstract

The past five decades have seen rapid expansion in academic achievement surveys with mixed findings and interpretation. Utilizing the education production function models, the surveys sought to test whether school or teacher-level variables explain academic achievement variance to a greater extent than student-level variables. Within this framework, we modelled teacher-level predictors of academic achievement in the Kenya Certificate of Primary Education (KCPE) examination in Mumias and Kuria East Sub-Counties in Kenya. Using a three-level hierarchical linear model (with 1824 students at Level-1 nested within 305 teachers at Level-2 who were themselves nested within 61 schools at Level-3), the results suggest that adjusting for Level-1 and Level-3 covariates, teacher age, the number of short in-service courses attended by the teachers in their respective subject areas and the number of formal written tests in those respective academic subjects have statistically significant effect on student academic achievement in the Kenya Certificate of Primary Education Examination. Policy implications of these findings are discussed.

Keywords:
teacher-level predictors hierarchical linear modelling Kenya Certificate of Primary Education Examination Mumias Sub-County Kuria East Sub-County Kenya

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Coleman, J.S., et al., Equality of Education Opportunity (Coleman) Study (EEOS). 1966, Washington, DC: Department of Health, Education, and Welfare, Office of Education/National Center for Education Statistics.
[2]	Hanushek, E.A. and S.G. Rivkin, Harming the Best: How Schools Affect the Black–White Achievement Gap. Journal of Policy Analysis and Management, 2009. 28(3): p. 366-393.
[3]	Carbonaro, W. and C. Elizabeth, School Sector and Student Achievement in the Era of Standards Based Reforms. Sociology of Education, 2010. 83(2): p. 160-182.
[4]	Southern and Eastern Africa Consortium for Measuring Education Quality, Quality of primary school inputs in Kenya. 2011, SACMEQ: Harare, Zimbabwe.
[5]	Hungi, N. and F.W. Thuku, Differences in pupil achievement in Kenya: Implications for policy and practice. International Journal of Educational Development, 2010. 30(1): p. 33-43.
[6]	Ejakait, E., et al., Factors associated with low achievement among students from Nairobi's urban informal neighborhoods. Urban Education, 2011. 46(5): p. 1056-1077.
[7]	Neild, R.C., The Effect of Teacher Certification on Middle Grades Achievement in an Urban District. Educational Policy, 2009. 23(5): p. 732-760.
[8]	Konstantopoulos, S. and V. Chung, The persistence of teacher effects in elementary grades. American Educational Research Journal, 2011. 48(2): p. 361-386.
[9]	Legewie, J. and T.A. DiPrete, School Context and the Gender Gap in Educational Achievement. American Sociological Review, 2012. 77(3): p. 463-485.
[10]	Heyneman, S.P. and W.A. Loxley, Influences on Academic Achievement Across High and Low Income Countries: A Re-Analysis of IEA Data. Sociology of Education, 1982. 55(1): p. 13-21.
[11]	Heyneman, S.P. and W.A. Loxley, The Effect of Primary-School Quality on Academic Achievement Across Twenty-nine High and Low-Income Countries. The American Journal of Sociology, 1983. 88(6): p. 1162-1194.
[12]	Sanders, W.L. and J.C. Rivers, Cumulative and Residual Effects of Teachers on Future Student Academic Achievement. 1996, University of Tennessee Value-Added Research and Assessment Center: Knoxville, Tennessee.
[13]	Wright, S.P., S.P. Horn, and W.L. Sanders, Teacher and Classroom Context Effects on Student Achievement: Implications for Teacher Evaluation. Journal of Personnel Evaluation in Education, 1997. 11(1): p. 57-67.
[14]	Darling-Hammond, L., Teacher Quality and Student Achievement: A Review of State Policy Evidence. 1999, Center for the Study of Teaching and Policy, University of Washington: Seattle, Washington.
[15]	Laczko-Kerr, I. and D.C. Berliner, The Effectiveness of "Teach for America" and Other Under-certified Teachers on Student Academic Achievement: A Case of Harmful Public Policy. Education Policy Analysis Archives, 2002. 10(37): p. 1-53.
[16]	Wenglinsky, H., How Schools Matter: The Link Between Teacher Classroom Practices and Student Academic Performance. Education Policy Analysis Archives, 2002. 10(12): p. 1-30.
[17]	Nye, B., L.V. Hedges, and S. Konstantopoulos, Do Low-Achieving Students Benefit More from Small Classes? Evidence from the Tennessee Class Size Experiment. Educational Evaluation and Policy Analysis, 2002. 24(3): p. 201-217.
[18]	Rivkin, S.G., E.A. Hanushek, and J.F. Kain, Teachers, Schools, and Academic Achievement. Econometrica, 2005. 73(2): p. 417-458.
[19]	Caprara, G.V., et al., Teachers' self-efficacy beliefs as determinants of job satisfaction and students' academic achievement: A study at the school level. Journal of School Psychology, 2006. 44(6): p. 473-490.
[20]	Hanushek, E.A., The Economics of Schooling: Production and Efficiency in Public Schools. Journal of Economic Literature, 1986. 24(3): p. 1141-1177.
[21]	Hanushek, E.A., The impact of differential expenditures on school performance. Educational Researcher, 1989. 18(4): p. 45.
[22]	Hanushek, E.A., Assessing the Effects of School Resources on Student Performance: An Update. Educational Evaluation and Policy Analysis, 1997. 19(2): p. 141-164.
[23]	Rabe-Hesketh, S. and A. Skrondal, Multilevel and Longitudinal Modeling Using Stata, Volumes I and II. Third ed. 2012, College Station, Texas USA: Stata Press.
[24]	Bryk, A.S. and S.W. Raudenbush, Hierarchichal Linear Models: Applications and Data Analysis Methods. 1992, Newbury Park, California USA: SAGE Publications, Inc.
[25]	Raudenbush, S.W. and A.S. Bryk, Hierarchical Linear Models: Application and Data Analysis Methods. Second ed. 2002, Thousand Oaks, California: Sage Publications, Inc.
[26]	Goldstein, H., Multilevel statistical models. 2nd ed. 1995, New York, USA: Halsted Press.
[27]	Hardin, J.W. and J.M. Hilbe, Generalized Linear Models and Extensions. 3rd ed. 2012, College Station, Texas USA: Stata Press.
[28]	Butt, J.J., The Greenwood Dictionary of World History. 2006, Westport, USA: Greenwood Press.
[29]	Chiraghdin, S. and M.E. Mnyampala, Historia Ya Kiswahili. 1977, Nairobi: Oxford University Press.
[30]	Massamba, D.P.B., Historia Ya Kiswahili. 2002, Nairobi: Jomo Kenyatta Foundation.
[31]	Leckie, G., Three-Level Multilevel Models. 2013, Bristol, UK: Centre For Multilevel Modelling, University of Bristol.
[32]	Leckie, G., Introduction to Multilevel Modelling. 2010, Bristol, UK: Centre For Multilevel Modelling, University of Bristol.
[33]	StataCorp (2013) Stata Multilevel Mixed-Effects Reference Manual: Release 13.
[34]	Filmer, D. and L.H. Pritchett, Estimating Wealth Effects without Expenditure Data-or Tears: An Application to Educational Enrolments in States of India. Demography, 2001. 38(1): p. 115-132.
[35]	Rutstein, S.O. and J. Kiersten, The DHS Wealth Index. 2004, Calverton, Maryland USA: ORC Macro.
[36]	ORC Macro. The DHS Program, Demographic and Health Surveys: Wealth Index. 2016 [cited 2016 18/07]; Available from: http://www.dhsprogram.com/topics/wealth-index/Index.cfm.
[37]	Taylor, R., Interpretation of correlation coefficient. Journal of Diagnostics Medical Sonography, 1990. 6(1): p. 35-39.
[38]	Acock, C.A., A Gentle Introduction to Stata. 2006, College Station, Texas USA: Stata Press.
[39]	Hox, J.J., Applied Multilevel Analysis. 1995, Amsterdam, The Netherlands: TT-Publikaties.
[40]	Daniel, H.C., Socioeconomic Status and Academic Achievement: Trajectories from Childhood to Adolescence. Canadian Journal of Education, 2009. 32(3): p. 558-590.
[41]	Jensen, E., Teaching with Poverty in Mind: What Being Poor Does to Kids' Brains and What Schools Can Do About It. 2009, Alexandria, VA USA: ASCD.
[42]	Gutman, L.M. and J. Vorhaus, The Impact of PupilBehaviour and Wellbeing on Educational Outcomes. 2012, London, UK: Childhood Wellbeing Research Centre, Institute of Education, University of London.
[43]	Valiente, C., J. Swanson, and N. Eisenberg, Linking Students’ Emotions and Academic Achievement: When and Why Emotions Matter. Child Dev Perspect 2012. 6(2): p. 129-135.
[44]	Hanushek, E.A., Hanushek Responds. Educational Researcher, 1989. 18(6): p. 25.
[45]	Willms, D.J. and M.A. Somers, Family, classroom, and school effects on children’s educational outcomes in Latin America. School Effectiveness and School Improvement, 2001. 12(4): p. 409-445.