Ogundari, Kolawole (2021): A systematic review of statistical methods for estimating an education production function.
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Abstract
The quality of administrative or longitudinal data used in education research has always been an issue of concern since they are collected mainly for recording and reporting, rather than research. The advancement in computational techniques in statistics could help researchers navigates many of these concerns by identifying the statistical model that best fits this type of data for research. The paper provides a comprehensive review of the statistical methods important for estimating education production function to recognize this. The article also provides an extensive overview of empirical studies that used the techniques identified. We believe a systematic review of this nature provides an excellent resource for researchers and academicians in identifying critical statistical methods relevant to educational studies.
Item Type: | MPRA Paper |
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Original Title: | A systematic review of statistical methods for estimating an education production function |
Language: | English |
Keywords: | Education, Production Function, Statistical Methods, Causal Analysis, Regression |
Subjects: | I - Health, Education, and Welfare > I2 - Education and Research Institutions > I21 - Analysis of Education I - Health, Education, and Welfare > I2 - Education and Research Institutions > I23 - Higher Education ; Research Institutions I - Health, Education, and Welfare > I2 - Education and Research Institutions > I25 - Education and Economic Development |
Item ID: | 105283 |
Depositing User: | Dr. Kolawole Ogundari |
Date Deposited: | 15 Jan 2021 01:31 |
Last Modified: | 15 Jan 2021 01:31 |
References: | Alauddin. M and C. Tisdell (2006). Student’s Evaluation of Teaching Effectiveness: What Surveys tell and what, the University of Queensland, Economic Theory, Applications, and Issues Working Paper No.42. Queensland Australia. Al-Barrak. M.A and M. Al-Razgan (2016). Predicting Students Final GPA using decision trees: A case study. International Journal of information and Education Technology, Vol. 6(7): 528-533. Altonji. J, T. Elder, and C.R. Tabler (2005). Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools, Journal of Political Economy, Vol. 113: 151-184. Amore. M. D and S. Murtinu (2019). Tobit models in strategy research: critical issues and applications. Global Strategy Journal. DOI:10.1002/gsj.1363 Angrist. J. D and J. Pischke (2008). Mostly Harless Econometrics: An empiricist’s companion. Princeton University Press. Andrew, R., J. Li, M. Lovenheim (2012). Heterogenous paths through college: Detailed patterns and relationships with graduation and earnings. National Centers for Analysis of Longitudinal Data in Education Research, Working paper No. 83, Washington, DC. An. G., J. Wang, Y. Yang, and X. Du (2018). A study on the effects of students’ STEM academic achievement with Chines parents’ participative styles in school education. Educational Sciences: Theory & Practices, Vol. 19(1): 41-54. Arrelano, M and S. Bond (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, Vol. 58: 277-297. Atteberry, A., D. Bassok, and V. C. Wong (2019). The effects of full-day Pre-kindergarten: experimental evidence of impacts on children’s school readiness. Educational Evaluation and Policy Analysis, Vol. 41(4): 537-562. Athey. S and G. Imbens (2016). Recursive partitioning for heterogeneous causal effects. Proceeding National Academic of Science, Vol. 113(27); 7353-7360. Athey. S., G. Imbens, Y. Kong, and V. Ramachandra (2016). An introduction to recursive partitioning for heterogeneous causal effects estimation using causalTree package. https://github.com/susanathey/causalTree. Austin. P.C (2018). Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes. Statistics in medicine, Vol. 37: 1874-1894. Austin. P.C and E. A. Stuart (2017). Estimating the effect of treatment in binary outcomes using full matching on the propensity score. Statistical methods in Medical Research, Vol. 26(6), 2505-2525. Ayalon, H., and A. Yogev (1997). Students, schools, and enrollment in science and humanity courses in Israeli secondary education. Educational Evaluation and Policy Analysis, 19(4), 339-353. Bautsch. B (2014). The effects of concurrent enrollment on the college-going and remedial education rates of Colorado’s High School students. Colorado Department of Higher Education (CDHE) Working paper. Becker. S.O. (2016). Using instrumental variables to establish causality. IZA World of Labor 2016: 250. Doi:10.15185/izawol.250 Berk. R and J. M. MacDonald (2008). Overdispersion and Poisson regression. Journal of Quantitative Criminology, Vol. 24: 269-284. Bernal. P., N. Mittag, and J. A. Qureshi (2016). Estimating the effects of school quality using multiple proxies. Labour Economics, Vol. 39: 1-10. Bifulco. R (2012). Can nonexperimental estimates replicate estimates based on Random Assignment in the evaluation of school choice? A within-study comparison. Journal of Policy Analysis and Management, Vol. 31(3): 729-751. Blundell, R and S. Bond (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, Vol. 87(1): 115-143. Borrego. M., E. P. Douglas, C. T. Amelink (2009). Quantitative, Qualitative, and Mixed Research Methods in Engineering Education. Journal of Engineering Education, Vol. 109(3): 53-66. Bowles, T. J, and J. Jones (2004). The effect of supplemental instruction on retention: a bivariate probit model. Journal of student retention, Vol. 5(4): 431-437. Burke, M.A, and T.R. Sass (2008). Classroom peer effects and student achievement. National Center for Analysis of Longitudinal Data in Education Research Working Paper No. 18, Washington DC. Boyd. G.A. (2008). Estimating Plant Level Energy Efficiency with a Stochastic Frontier. The Energy Journal, Vol. 29(2): 23-43. Browers, A. J, and R. Sprott (2012). Examining the multiple trajectories associated with dropping out of high school: a growth mixture model analysis. The journal of Educational Research, Vol. 105(3): 176-195. Collins, L. M, and L.S.T (2010). Latent Class and Latent Transition Analysis, With applications in the social, behavioral, and health sciences. New York: Wiley. Clark. J.A ( 1984). Estimation of Economies of Scale in Banking Using a Generalized Functional Form. Journal of Money, Credit, and Banking, Vol. 16(1): 53-68. Calcago. J.C and B. T. Long (2008). The impact of postsecondary remediation using a regression discontinuity approach: Addressing endogenous sorting and noncompliance. National Bureau of Economic Research (NBER) working paper No. 14194, Cambridge, MA. Canaan. S and P. Mouganle (2018). Returns to Education Quality for Low-Skilled Students: Evidence from a Discontinuity," Journal of Labor Economics, Vol. 36 (2): r: 395-436. Card. D (1999). The causal effect of education on earnings. Handbook of Labor Economics, Vol. 3. Pp. 1801-1863. Cartwright, N. (2011). A philosopher’s view of the long road from RCTs to effectiveness. Lancet, 377, 1400–1401. Cascio. E. U (2019). Does universal preschool hit target? Program access and preschool impacts. National Bureau of Economic Research Working Paper 2315, Cambridge, MA. Cavalluzzo, L., D. L. Lowther, C. Mokher, and C. Fan (2012). Effects of the Kentucky Virtual Schools’ hybrid program for Algebra I on grade 9 student mat achievement. Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance (NCEE) Working paper No. 2012-4020. U.S. Department of Education, Washington, DC Chakraborty. T and R. Jayaraman (2019). School feeding and learning achievement: Evidence from India midday meal program. Journal of Development Economics, Vol. 139: 249-269. Chisadza. S (2015). A bivariate probit model of the transition from school to work in the post-compulsory schooling period: a case study of young adults in the cape area. DNA Economics. Choi, A., J. Calero, and J.O. Escardibul (2012). Private tutoring and academic achievement in Korea: an approach through PISA-2006, KEDI Journal of Educational Policy, Vol. 9(2): 299-302. Cragg. J. G (1971). Some statistical models for limited dependent variables with application to the demand for durable goods, Econometrica, Vol. 39: 828-844. Croninger, R. G. J. K. Rice, A. Rathbun, and M. Nishio (2007). Teacher qualifications and early learning: effects of certification, degree, and experience on first-grade student achievement. Economics of Education Review, Vol 26: 312-324. Cornelisz, I (2013). Relative private school effectiveness in the Netherlands: A reexamination of PISA 2006 and 2009 data, Procedia Economics and Finance, Vol. 5: 192-201. Cordero. J.M., V. Cristobal, D. Santin (2017). Causal inference on education policies: A survey of empirical studies using PISA, TIMSS, PIRLS. Munich Personal RePEc Archive Paper No. 76295.Online at https;//mpra.ub.uni-muenchen.de/76295/ Chang. H.S, and Y. Hsing (1996). A study of Demand for higher education at private institutions in the US: A Dynamic and General Specification, Education Economics, Vol. 4(3): 267-278. Denson. N and M. Ing (2014). Latent Class Analysis in Higher Education: An illustrative example of Pluralstic orientation. Research Higher Education, Vol. 55: 508-526. Denny, K., and V. Oppedisano (2013). The surprising effect of larger class sizes: Evidence using two identification strategies, Labour Economics, Vol. 23: 57-65. Deaton. A and N. Cartwright (2018). Understanding and misunderstanding randomized controlled trials. Social Science and Medicine, Vol. 201: 2-21. Denison. D. G (2002). Bayesian method for nonlinear classification and regression. Chichester England New York NY Deschant, N and K. Goeman (2015). Selection bias in educational issues and the use of Heckman’s sample model. In: Kristof De Witte (Ed), Contemporary Economic Perspective in Education. Leuven University Press pp. 35-51. Desjardins. C. D (2015). Modeling Zero-inflated and overdispersed count data: an empirical study of school suspensions. The Journal of experimental education, Vol. 84(3): 449-472. Doyle. W. R (2011). Effect of increased academic momentum on transfer rates: An application of the generalized propensity score. Economics of Education Review, Vol. 30 (1): 191-200. Dronkers, J and S. Avram (2010). A cross-sectional analysis of the relations of school choice and effectiveness differences between private-dependent and public schools. Educational Research and Evaluation, Vol. 16(2): 151-175 Duchini. E (2017). Is college remedial education a worthy investment? New evidence a worthy investment? New evidence from a sharp regression discontinuity design. Economic Education Review, Vol 60: 36-53 Duvendack, M., R. Palmer-Jones, J.B. Coperstrake, L. Hoope, Y. Loke, and N. Rao (2011). What is the evidence of the impact of microfinance on the well-being of the poor? EOO 1-center social science research unit, Institute of Education, University of London, London. ISBN 978-1-907345-19-7. Edwards. J. K., S. R. Cole, C. R. Lesko, W. C. C, Mathews, R. D. Morre, M. J. Mugavero, and D. Westreich (2016). An illustrative on inverse probability weighting to estimate policy-relevant causal effects. American Journal of Epidemiology, Vol. 184(4): 336-344. Elze. C., J. Gregson, U. Baber, E. Williamson, S. Sartori, R. Mehran, M. Nicholas, G. W. Stone, and S. J. Pocock (2017). Comparison of propensity score methods and covariate adjustment. Journal of the American College of Cardiology, Vol. 69(3): 345-357. Eminita. V and R. Widiyasari (2019). Analysis of factors affecting the undergraduate student quit the study. Journal of Physics: Conference Series 1157 doi:10. 1088/1742-6596/1157/3/032105. Evans. W. N and R. N. Schwab (1995). Finishing High school and starting college: Do Catholic Schools Make Difference? Quarterly Journal of Economics, Vol. 110: 941-974. Espinosa.A.M.G(2017). Estimating the education production function for cognitive and non-cognitive development of children in Vietnam through structural equation modeling using the Young Lives data base. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Quantitative Research Methods at University College London. Fan, J, and Q. Yao (2003). Nonlinear time series: nonparametric and parametric methods. Springer: New York. Frieden. T. R (2017). Evidence for health decision making—beyond randomized, controlled trials N. Engl. J. Med., 377 (2017), pp. 465-475 Fuller, S. C, and H. F. Ladd (2013). Schooled-based accountability and the distribution of teacher quality across grades in elementary schools. National Centers for Analysis of Longitudinal Data in Education Research, Working paper No. 75, Washington, DC., Gronberg, T., D. W. Jansen, and L.L. Taylor (2011). The adequacy of educational cost functions: lessons from Texas, Peabody Journal of Education, Vol. 86(1): 3-27. Gee, K, and R. M. (2014). The effects of single-sex versus coeducational schools on adolescent peer victimization and perpetration. Journal of Adolescence, Vol. 3: 1237-1251. Golino. H. F and C. M. A. Gomes (2016). The random forest as an imputation method for education and psychology research: its impact on item fit and difficulty of the Rasch model. International Journal of Research and Methods in Education. Vol. 39(4): 345-348. Grosskopf. S., K. J. Hayes, and L. L. Taylor (2014). Applied efficiency analysis in education, Economics and Business Letters, Vol. 3(1): 19-26. Gyimah-Brempong, K., and A. Gyapong (1991). Characteristics of Education Production Functions: An application of Canonical Regression Analysis. Economics of Education Review, 10(1), pp. 7-17. Gottfried. M. A (2010). Evaluating the relationship between student attendance and achievement in Urban Elementary and Middle Schools: An instrumental variables approach. American Educational Research Journal, Vol. 47(2): 434-465. Greene. T (2019). The impact of the transfer on Baccalaureate competition. Education Research and Data Center (ERDC) Working paper, Olympia Washington. Guarcello. M.A., R. A. Levine, J. Beamer, J. P. Frazee, M. A. Laumakis, and S. A. Schellenberg (2017). Balancing student success: Assessing supplemental Instruction Through Coarsened Exact Matching. Tech Know Lean, Vol. 22: 335-352. Hanushek, E. (1979). Conceptual and Empirical Issues in the Estimation of Educational Production Functions. The Journal of Human Resources, 14(3), pp. 351-388 Hoyle, R. (2012). The model specification in structural equation modeling. In: R. Hoyle, ed. Handbook of structural equation modeling. New York: Guilford Press, pp. 126-144 . Hallberg, K., R. Williams, A. Swanlund, and J. Eno (2018). Short comparative interrupted Time series using aggregated school-level Data in Education Research, Educational Researcher, Vol. 47(5): 295-306. Hardman. J., A. Paucar-Caceres, and A. Fielding (2012). Predicting students’ Progression in Higher Education by using the Random Forest Algorithm. Systems Research and Behavioral Science, Vol. 30: 194-203. Harris. D and T. Sass (2007). Teacher training, teacher quality, and student achievement. National Centers for Analysis of Longitudinal Data in Education Research, Working Paper No. 3, Washington, DC. Hanauer, Matthew (2019) "Using Propensity Score Matching to Evaluate Differences in Public and Private Students on Self-Control," International Journal of School Social Work: Vol. 4: Iss. 1. https://doi.org/ 10.4148/2161-4148.1034 Harris, Heather D., "Propensity score matching in higher education assessment" (2015).Masters Theses. 55. https://commons.lib.jmu.edu/master201019/55 Heckman. J (1997). Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources, Vol. 32(3): 441-462. Heckman. J (1979). Sample selection bias as a specification error, Econometrica, Vol. 47: 153-161. Hanushek, E. (2007). Education Production Functions, Stanford: Hoover Institution, Stanford University. Hanushek, E. A (1986). The economics of schooling, production, and efficiency in public schools, Journal of Economic Literature, Vol. 24: 1141-1177. Halkiotis, D., I. Konteles, and V. Brinia (2018). The technical efficiency of high schools: The case of a Greek Prefecture, Education Sciences, Vol. 8(84): DOI:10.3390/educsci8020084 Henderson, S., A. Petrosino, S. Gukenberg, and S. Hamilton (2008). A second follow up year for measuring how benchmark assessments affect student achievement (REL Technical Brief, REL 2008-002). Washington, DC: US. Department of Education, Institute of Education Sciences, National Center for Education and Regional Assistance, Regional Educational Laboratory Northeast and Islands. Herbst. C. M (2016). The impact of quality rating and improvement systems in families’ childcare choices and childcare labor supply. Institute for the Study of Labor (IZA) Discussion Paper No. 10383. Hogrebe, N and R. Strietholt (2016). Does-non participation in preschool affect children's reading achievement? International evidence from propensity score analyses. Large scale assessment in education, Vol. 4(2): DOI 10.1186/s40536‑016‑0017‑3 Hsiao. C (2007). Panel data analysis-advantages and challenges. Test: 16: 1-22. DOI 10.1007/s11749-007-0046-x Iacus, S. M., G. King, G. Porro (2012). Causal inference without balance checking: coarsened exact matching. Political Analysis, Vol. 20: 1-24. Iacus, S. M., G. King, G. Porro (2019). A theory of statistical inference for matching methods in causal research, Political Analysis, Vol 27: 46-68 Imbens. G.W and J. D. Angrist, 1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, Vol. 62(2): 467-475. Joshi. R and J. M. Wooldridge (2019). Correlated random effects models with endogenous explanatory variables and unbalanced panels. Annals of Economics and Statistics, Vol. 134:243-268. Just. R. E., D. Zilberman, and E. Hochman (1983). Estimation of Multicrop Production Functions. American Journal of Agricultural Economics, Vol. 65(4): 770-780. Johnes, J., M. Portela, and E. Thanassoulis (2017). Efficiency in education, Journal of Operational Research Society, Vol. 68: 331-338. Jyoti. D.F., E.A. Frongillo, and S. J. Jones (2005). Food insecurity affects school children’s academic performance, weight gain, and social skills. Journal of Nutrition, Vol. 135: 2831-2839. Kaliba. A. R., R. J. Mushi, A. G. Gongwe, and K. Mazvimavi (2020). A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach. World Development, Vol. 127 Karl. A. T., Y. Yang and S. L. Lohr (2013). A correlated random effects model for nonignorable missing data in the value-added assessment of teacher, Journal of Educational and Behavioral Statistics, Vol. 38(6): 577-603. Knaus. M., M. Lechner, and A. Strittmatter (2018). Machine learning estimation of heterogeneous causal effects empirical Monte Carlo evidence. Working paper, University of St. Gallen. Kuzmina, J and M. Carnoy (2016). The effectiveness of vocational versus general secondary education: Evidence from the PISA for countries with early tracking, International Journal of Manpower, Vol. 37(1): 2-24. Konstantopoulos, S, and S. She (2016). Class size effects of reading achievement using Cyprus: Evidence from TIMSS. Educational Research and Evaluation, Vol. 22: 86-109. Krueger. A. B (1997). Experimental estimates of education production functions. National Bureau of Economic Research (NBER) working paper 6051. Cambridge, MA Koç. C (2004). The productivity of health care and health production functions. Health Economics, Vol. 13(4): 739-747. Kline, R., (2011). Principles and Practice of Structural Equation Modeling. 3rd ed. New York: Guildford Press. Kang. L., F. Peng, and Y. Zhu (2019). Returns to Higher Education Subjects and Tiers in China: Evidence from the China Family Panel Studies. Studies in Higher Education, https://doi.org/10.1080/03075079.2019.1698538 Lazer, D., R. Kennedy. G. King and A. Vespignani (2014). Big data. The parable of Google Flu: traps in big data analysis. Science, Vol. 343: 1203-1205. Lechner. M (2019). Modified causal forests for estimating heterogeneous causal effects. CEPR Discussion Paper No. DP13430. Lee. V. (2000). Using Hierarchical linear modeling to study social contexts: The case of school effects. Educational Psychologist, Vol. 35(2): 125-141. Linden. A (2015). Conducting interrupted time series analysis for single and multiple group comparisons. The Stata Journal, Vol. 15(2): 480-500. Liou. P-Y (2009). Model comparison for count data with a positively skewed distribution with an application to the number of University courses completed. Paper presented at the Annual Meeting of the American Educational Research Association San Diego, April 16, 2009. Liu. Z., A. C.A. Kanter, K. D. Messer, and H.M. Kaiser (2013). Identifying significant characteristics of organic milk consumers: a CART analysis of an artefactual field experiment. Applied Economics, Vol. 45(21): 3110-3121. Li, W, and S. Konstantopoulos (2016). Class size effects on fourth Grade Mathematics Achievement: Evidence from TIMSS 2011. Journal of Research on Educational Effectiveness, Vol. 9(4): 503-530. Lokshin, M., and Z. Sajai (2004). Maximum likelihood estimation of endogenous switching models. The Stata Journal, Vol. 4(3): 282-289. Maddala. G. S (1983). Limited dependent and qualitative variables in Econometrics. Cambridge (UK): Cambridge University Press. McDaniel. T (2018). Using random forests to describe equity in higher education: a critical quantitative analysis of Utah’s Postsecondary Pipelines. Butler Journal of Undergraduate Research, Vol. 4, Article 10. Mckeown. K., T. Haase, and J. Pratschke (2015). Determinants of child outcomes in a cohort of children in the Free pre-school year in Ireland, 2012/2013. Irish Educational Studies, Vol. 34(3): 245-263. Mullainathan. S and J. Spiess (2017). Machin learning: An applied Econometric Approach. Journal of Economic Perspective, Vol. 31(2): 87-106. Mundlak, Y (1978). On the pooling of time series and cross-section data. Econometrica, Vol. 46: 69-85 Munoz, A.M., J. R. Prather, and J. H. Stronge (2011). Exploring teacher effectiveness using hierarchical linear models: Students- and Class level predictors in elementary school reading. Planning and Changing, Vol. 42 (3/4): 241-273. Nguyen. A. N and J. Taylor (2003). Post-High School Choices: New Evidence from a multinomial logit model. Journal of Population Economics, Vol. 16(2): 287-306. Niu. L (2017). Family socioeconomic status and choice of STEM Major in College: An analysis of a National Sample. College Student Journal, Vol. 51(2): 298-312. O’Dwyer, L. M., and Parker, C. E. (2014). A primer for analyzing nested data: multilevel modeling in SPSS using an example from a REL study (REL 2015–046). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northeast & Islands. Retrieved from http://ies.ed.gov/ncee/edlabs. Ogundari. K and O.D. Bolarinwa (2018). Impact of agricultural innovation adoption: a meta-analysis. Australian Agricultural and Resource Economics, Vol. 62(2): 217-236 Ogundari. K, and A. B. Aromolaran (2014). Impact of education on household welfare in Nigeria. International Economic Journal, Vol. 28(2): 345-364. Oreopoulos. P (2006). Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter. The American Economic Review, Vo. 96(1): 152-175. Papke. E. L (2005). The effects of spending on test pass rates: evidence from Michigan. Journal of Public Economics, Vol. 80: 821-839. Parker, C. E., O’Dwyer, L. M., & Irwin, C. W. (2014). The correlates of academic performance for English language learner students in a New England district (REL 2014–020). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northeast Islands. http://eric.ed.gov/?id=ED546480. Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. London: Wadsworth. Pfeffermann, D, and V. Landsman (2011). Are private schools better than public schools? Appraisal for Ireland by methods for observational studies. The Annals of Applied Statistics, Vol. 5(3): 1726. Ponzo. M (2013). Does bullying reduce educational achievement? An evaluation using matching estimators. Journal of Policy Modeling, Vol. 35: 1057-1078. Power. S., J. Qian, K. Jung, A. Schuler, N. H. Shan, T. Hastie, and R. Tibshirani (2018). Some methods for heterogeneous treatment effect estimation in high dimensions. Sta. Med, Vol. 37: 1767-1787. Rodgers. J. R (2001). A panel-data study of the effects of student attendance on University performance. Australian Journal of Education, Vol. 45(3): 284-295. Raskind. I. G., R. Haardörfer, and C. J.Berg (2019). Food insecurity, psychosocial health and academic performance among college and university students in Georgia, USA. Public Health Nutrition: 22(3), 476–485. Roodman, D (2009). How to do xtband2: an introduction to differences and system GMM in Stata. The Stata Journal, Vol. 9(1): 86-136. Sakellariou. C (2007). Education policy reform, local average treatment effect, and returns to schooling from instrumental variables in the Philippines. Applied Economics, Vol. 38(4): 473-481 Salehi. M and M. Roudbari (2015). Zero-inflated Poisson and negative binomial regression models: application in education. Medical Journal of the Islamic Republic of Iran, Vol. 29: 297 Shan. S., C. Li, J. Shi, L. Wang, and H. Cai (2014). Impact of effective communication Achievements sharing and positive classroom environments on learning performance. Systems Research and Behavioral Science system Research, Vol. 31: 471-482. Siddique. Z (2014). Randomized control trials in an imperfect world. IZA World of Labor Working paper No. 110. DOI: 10.15185/izawol.110 Silver. D., M. Saunders, and E. Zarate (2008). What factors predict high school graduation in the Los Angeles United School District. California Dropout Research Project Report # 14, University of California, Santa Barbara. Smerillo. N.E., A. J. Reynolds, J. A. Temple, and S. Ou (2019). Chronic Absence, Eighth-Grade Achievement, and High School Attainment in the Chicago Longitudinal Study. Journal of School of Psychology, Vol 67: 163-178. Somers. M., P. Zhu, R. Jacob, and H. Bloom (2003). The validity and precision of the comparative interrupted time series design in educational evaluation. MDRC Working Paper on Research Methodology. Storm. H., K. Baylis, and T. Heckelei (2019). Machine learning in agricultural and applied economics. European Review of Agricultural Economics.@doi:10.1093/erae/jbz033. Stratton. L.S., D. M. O’Toole, and J. N. Wetzel (2005). A multinomial Logit model of college Stop out and Dropout Behavior. Institute for the Study of Labor (IZA) Working paper No. 1634. Bonn, Germany. Streeter. A. J., N. X. Lin, L. Crathorne, M. Haasova, C. Hyde, D. Melzer, and W. E. Henley (2017). Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. Journal of Clinical Epidemiology, Vol. 87: 23-34. Subbiah. M., M.R. Srinivasan, and S. Shanthi (2011). Revisiting higher education data analysis: A Bayesian perspective. International Journal of Science and Technology Education Research, Vol. 12(2): 32-38. Sami. J., F. Pascal, and B. Younes (2013). Public Road Transport Efficiency: A Stochastic Frontier Analysis, Journal of Transportation Systems Engineering and Information Technology, Vol. 13(5): 64-71. Scippacercola, S, and L. D’ Ambra (2013). Estimating the relative efficiency of secondary schools by Stochastic Frontier Analysis. Procedia Economics and Finance 17 ( 2014 ) 79 – 88. Theobald. E (2018). Students are rarely independent: What, Why, and How to use random effects in Discipline-Based Education Research. CBE-Life Sciences Education, Vol. 17: 1-12. Tobin. J (1975). Estimation of relationships for limited dependent variables. Econometrica, Vol. 46: 24-36. Todd, P., and K. Wolpin (2006). The Production of Cognitive Achievement in Children: Home, School, and Racial Test Score Gaps, Philadelphia: University of Pennsylvania. Topirceanu. A and G. Grosseck (2017). Decision tree learning used for the classification of student archetypes in online courses. Paper presented at the 21st International Conference on knowledge-based and intelligent information and engineering systems, KES2017, 6-8 September, Marseilles, France. Tsai. S and Y. Xie (2011). Heterogeneity in Returns to College Education: Selection Bias in Contemporary Taiwan, School Science Research, Vol. 40(3): 796-810. Umansky. H and H. Dumont (2019). English Learner Labeling: How English Learner Status Shapes Teacher Perceptions of Students and the moderating role of Bilingual Instructional Settings. (EdWorkingPaper: 19-94). Retrieved from Annenberg Institute at Brown University: http://www.edworkingpapers.com/ai19-94 Uysal. S. D. (2011). Three Essays on Doubly Robust Estimation Methods. Ph.D. Dissertation submitted to the University of Konstanz. Worthington, A (2001). An empirical survey of frontier efficiency measurement techniques in education, Education Economics, Vol. 9(3): 245-268. Wager. S and S. Athey (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of American Statistics Association, Vol. 113: 1228-1242. Wang. J., A. Hefetz, and G. Liberman (2017). Applying structural equation modeling research. Culture and Education, Vol. 29(3): 563-618. Wang, X., Y. Chuang, and B. McCready (2017). The effect of earning an associate degree on community college transfer students’ performance and success at four-year institutions. Teachers' College Record. West, M.R., and L. Woessmann (2010). Every catholic child in a catholic school: Historical resistance to state schooling contemporary private competition and student achievement across countries. The Economic Journal, Vol. 120(546): 229-255. Weerts, D. J., A. F. Cabrera, and P. P. Mejias (2013). Uncovering categories of civically engaged college students: a latent class analysis. The review of Higher Education, Vol. 37: 141-168. Wooldridge, J. M (2002). Econometric Analysis of cross-section and panel data. MIT Press, Cambridge, MA. Wooldridge, J.M (2019). Correlated random effects models with unbalanced panels, Journal of Econometrics, Vol. 211(1): 137-150. Xu. Z., J. Hannaway, and S. D’Sounza (2009). Student transience in North Carolina: The effect of school mobility on student outcomes using longitudinal data. National Center for Analysis of Longitudinal Data in Education Research Working Paper No. 22, Washington DC. Vigdor. J. I (2008). Teacher salary bonuses in North Carolina (Working paper 15). Washington, DC, National Center for Analysis of Longitudinal Data in Education Research. Vandenberghe, V, and S. Robin (2004). Evaluating the effectiveness of private education across countries: a comparison of methods. Labour Economics, Vol. 11(4): 487-506. Zwick. R (1993). The validity of the GMAT for the prediction of Grades in Doctoral Study in Business and Management: An empirical Bayes approach. Journal of Educational Statistics, Vol. 18(1): 91-107. Zeiser. K.L., J. Taylor, J. Rickles, M. S. Garet, and M. Segeritz (2014). Evidence of deeper learning outcomes: Technical appendix. (Report #3 Findings from the study of deeper learning: Opportunities and outcomes). Washington, DC: American Institutes for Research. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/105283 |