Skip to main content

Guiding Principles for Evaluating Evidence in Education Research

Part of the Educational Governance Research book series (EGTU,volume 6)

Abstract

Based on their experiences from their work with two national initiatives designed to reform educational practice in U.S., the authors present seven guiding principles of evidence-based/informed educational policy and research to lay the foundation for making rigorous and comprehensive judgments about what evidence and scientific research designs should be taken into account when scaling-up educational reforms to serve the public good . The authors further provide case examples from US with a clear potential to both utilize and generate evidence in the public interest including educational research studies that seeks to support underrepresented groups in preparing for and achieving successful transitions to postsecondary education and careers, in STEM and other fields. The authors conclude that educational researchers have a critical role to play in providing decision-makers with the tools to judge the evidence to serve public good .

Mistaking no answers in practice for no answers in principle is a great source of moral confusion – Sam Harris

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-58850-6_10
  • Chapter length: 23 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-58850-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Hardcover Book
USD   139.99
Price excludes VAT (USA)

Notes

  1. 1.

    American Recovery and Reinvestment Act. (Pub.L.11-5); Gates Foundation: http://www.gatesfoundation.org/united-states/Pages/measures-of-effective-teaching-fact-sheet.aspx

  2. 2.

    Results of the 2009 NAEP for U.S. high school seniors found no significant changes in the gap between white and black students’ reading scores from 1992 to 2009, and no significant change between white and black or Hispanic students’ mathematics scores from 2005 to 2009 (NCES 2011).

  3. 3.

    KIPP (http://www.kipp.org/) is “based around high expectations for student achievement; commitment to a college preparatory education by students, parents, and faculty; devotion of time to both educational and extracurricular activities; increased leadership power of school principals; and a focus on results through regular student assessments” (U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse 2010). Urban Prep is a Chicago-based initiative operating in the only all-male public schools in the state of Illinois to “provide a comprehensive, high-quality college preparatory education that results in graduates succeeding in college” (see http://www.urbanprep.org/about/historvlindex.asp).

  4. 4.

    See, Dynarski and Scott-Clayton (2007) and Hoxby (2007). Other examples of online resources on the college selection and application processes in the U.S. include the National Center for Education Statistics College Navigator (http://nces.ed.gov/collegenavigator) and the American Council on Education, Lumina Foundation for Education, and Ad Council’s KnowHow2GO (http://www.knowhow2go.org/).

  5. 5.

    See the Success for All Foundation’s ‘Our Story’, retrieved February 22, 2011 from http://www.successforall.org/About/story.html

  6. 6.

    The What Works Clearinghouse is an initiative of the U.S. Department of Education’s Institute of Education Sciences which ‘develops and implements standards for reviewing and synthesizing education research’ (http://ies.ed.gov/ncee/wwc/aboutus). The Campbell Collaboration is an ‘international research network that produces systematic reviews of the effects of social interventions’ (http://www.campbellcollaboration.org/aboutus/index.php). The Society for Research on Educational Effectiveness seeks to advance and disseminate research on the causal effects of education interventions, programs, and policy (http://www.sree.org/pages/mission.php).

  7. 7.

    Anderson’s original Adaptive Control of Thought (ACT) theory of human cognition was first described in Anderson, 1976; elaborated in 1983; and refined into the ACT-R (Adaptive Control of Thought-Rational) theory for understanding and stimulating cognition, 1993, which is the foundation of the Cognitive Tutor software.

  8. 8.

    For additional information see Ritter et al. (2007a, b). For a review of this study, see the WWC July 2009 Intervention Report on the Cognitive Tutor® Algebra I available online at http://ies.ed.gov/ncee/wwc/pdf/wwccogtutor072809.pdf

  9. 9.

    For additional information on BioKIDS see the project’s web site at http://www.biokids.umich.edu/

  10. 10.

    The Principled Assessment Designs for Inquiry (PADI) project builds on developments in measurement theory, technology, cognitive psychology, and science inquiry, implementing the evidence-centered assessment design (ECD) framework (see http://padi.sri.com). For additional information on the BioKIDS/PADI collaboration and details of the assessment system, see Songer et al. (2009), and Gotwals and Songer (2006).

  11. 11.

    For additional information on the TPRI see Foorman et al. (1998) and Foorman et al. (2007); and the web site at http://www.childrensleaminginstitute.org/ourprograms/program-overview/TPRI/. For information on FAIR see Foorman and Petscher (2010) and Foorman et al. (2009); and the web site at http://www.fcrr.org/fair/index.shtm

  12. 12.

    For a complete listing of current research projects being conducted by research faculty at the Florida Center for Reading Research, see http://www.fcrr.org/centerResearch/centerResearch.shtm

  13. 13.

    For a detailed description of the Schools and Staffing Survey, including copies of instrumentation administered in 1987–1988 m 1990–1991, 1993–1994, 1999–2000, 2003–2004, and 2007–2008, see the National Center for Education Statistics online at http://nces.ed.gov/surveys/sass/index.asp

  14. 14.

    For information about the SimCalc intervention and the scaling-up SimCalc study, see the Kaput Center for Research and Innovation in STEM Education (http://www.kaputcenter.umassd.edu/projects/simcalc), the SRI International Scaling Up SimCalc project website (at http://math.sri.com/index.html), and Roschelle et al. (2010b).

  15. 15.

    Specifically, using a method and a propensity score sub classification estimator introduced by O’Muircheartaigh and Hedges reduced “bias in the estimate of a population average treatment effect” and identified “the portion of a population for which an experiment can generalize with fewer costs in terms [of] bias, variance, and extrapolation” (Tipton 2011: 4).

  16. 16.

    For additional information on the TEACH (Training Early Achievers for Careers in Health) Research program see http://chess.uchicago.edu/TEACH

  17. 17.

    For additional information on the College Ambition Program and the NSF-supported Transforming Interests in STEM Careers (TISC) study evaluating its impacts see the program website at http://collegeambition.org/

References

  • American Educational Research Association (AERA). (2016). Retrieved from homepage: https://www.aera.org

  • American Recovery and Reinvestment Act of 2009 (ARRA). (2009, February 19). Pub. L. No. 111–5, 123 Stat. 115, 516.

    Google Scholar 

  • BioKIDS: Kids’ Inquiry of Diverse Species. (2005). Retrieved from http://www.biokids.umich.edu

  • Bohrnstedt, G. W., & Stecher, B. M. (2002). What we have learned about class size reduction in California. Sacramento: California Department of Education.

    Google Scholar 

  • Bohrnstedt, G., Stecher, B., & Wiley, E. (2000). The California class size reduction evaluation: Lessons learned. In How small classes help teachers do their best (pp. 201–226). Philadelphia: Temple University Center for Research in Human Development and Education.

    Google Scholar 

  • Borman, G. D., Hewes, G. M., Overman, L. T., & Brown, S. (2003). Comprehensive school reform and achievement: A meta-analysis. Review of Educational Research, 73(2), 125–230. doi:10.3102/00346543073002125.

    CrossRef  Google Scholar 

  • Borman, G., Slavin, R. E., Cheung, A., Chamberlain, A., Madden, N. A., & Chambers, B. (2007). Final reading outcomes of the national randomized field trial of success for all. American Educational Research Journal, 44(3), 701–731. doi:10.3102/0002831207306743.

    CrossRef  Google Scholar 

  • Bryk, A. S. (2015). Accelerating how we learn to improve. Educational Researcher, 44(9), 467–478.

    CrossRef  Google Scholar 

  • Campbell Collaboration: Vision, Mission, and Key Principles. (2016). Retrieved from https://www.campbellcollaboration.org/vision-mission-and-principle/explore/our-key-principles

  • Campuzano, L., Dynarski, M., Agodini, R., & Rall, K. (2009). Effectiveness of reading and mathematics software products: Findings from two student cohorts. NCEE 2009–4041. National Center for Education Evaluation and Regional Assistance.

    Google Scholar 

  • Children’s Learning Institute (CLI) at The University of Texas-Houston Health Science Center and the Texas Institute for Measurement, Evaluation, and Statistics (TIMES) Technical Report TPRI (2010–2014 Edition). Retrieved from: http://tpri.org/resources/documents/20102014TechnicalReport.pdf

  • Children’s Learning Institute: TPRI Early Reading Assessment. (2015). Retrieved from https://www.childrenslearninginstitute.org/resources/tpri-early-reading-assessment

  • Chmielewski, A. K. (2014). An international comparison of achievement inequality in within-and between-school tracking systems. American Journal of Education, 120(3), 293–324.

    CrossRef  Google Scholar 

  • College Ambition Program. (2016). Retrieved from homepage: http://collegeambition.org

  • Duncan, G. J., & Murnane, R. J. (Eds.). (2011). Whither opportunity?: Rising inequality, schools, and children’s life chances. New York: Russell Sage Foundation.

    Google Scholar 

  • Dynarski, S., & Scott-Clayton, J. E. (2007). The feasibility of streamlining aid for college using the tax system. In National Tax Association papers and proceedings (vol. 99, pp. 250–262).

    Google Scholar 

  • Every Student Succeeds Act of 2015, S. 1177, 114th Cong. (2015). Washington, DC: US Department of Education. Public Law 114–95.

    Google Scholar 

  • Florida Center for Reading Research. (2008). Retrieved from homepage: http://www.fcrr.org/centerResearch/centerResearch.shtm

  • Foorman, B. R., & Petscher, Y. (2010). Development of spelling and differential relations to text reading in grades 3–12. Assessment for Effective Intervention, 36(1), 7–20.

    CrossRef  Google Scholar 

  • Foorman, B. R., Fletcher, J. M., Frances, D. J., Carlson, C. D., Chen, D., & Mouzaki, A. (1998). Technical report: Texas primary reading inventory (1998th ed.). Houston: Center for Academic and Reading Skills and the University of Houston.

    Google Scholar 

  • Foorman, B., Santi, K., & Berger, L. (2007). Scaling assessment-driven instruction using the internet and handheld computers. In B. Schneider & S. K. McDonald (Eds.), Scale-up in education (pp. 68–90). Plymouth: Rowman & Littlefield Publishers.

    Google Scholar 

  • Foorman, B., Torgesen, J., Crawford, E., & Petscher, Y. (2009). Assessments to guide reading instruction in K-12: Decisions supported by the new Florida system. Perspectives on Language and Literacy, 35(5), 13–19.

    Google Scholar 

  • Gates Foundation. (2016). Retrieved from homepage: http://www.gatesfoundation.org

  • Gotwals, A. W., & Songer, N. B. (2006). Measuring students’ scientific content and inquiry reasoning. In Proceedings of the 7th international conference on learning sciences (pp. 196–202). International Society of the Learning Sciences.

    Google Scholar 

  • Harris, S. (2016). Retrieved from homepage: https://www.samharris.org

  • Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational and Behavioral Statistics, 6(2), 107–128.

    CrossRef  Google Scholar 

  • Hedges, L. V. (2013). Recommendations for practice: Justifying claims of generalizability. Educational Psychology Review, 25(3), 331–337. doi:1040-726X.

    Google Scholar 

  • Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945–960.

    CrossRef  Google Scholar 

  • Hoxby, C. M. (Ed.). (2007). College choices: The economics of where to go, when to go, and how to pay for it. Chicago: University of Chicago Press.

    Google Scholar 

  • Imbens, G. W., & Rubin, D. B. (2010). Rubin causal model. In S. N. Durlauf & L. E. Blume (Eds.), Microeconometrics (pp. 229–241). New York: Macmillan.

    Google Scholar 

  • Ingersoll, R. M. (1998). The problem of out-of-field teaching. The Phi Delta Kappan, 79(10), 773–776.

    Google Scholar 

  • Ingersoll, R. M. (1999). The problem of underqualified teachers in American secondary schools. Educational Researcher, 28(2), 26–37.

    CrossRef  Google Scholar 

  • Ingersoll, R. M. (2004). Why do high-poverty schools have difficulty staffing their classrooms with qualified teachers? Center for American Progress, Institute for America’s Future.

    Google Scholar 

  • Ingersoll, R. M., Han, M., & Bobbitt, S. (1995). Teacher supply, teacher qualifications, and teacher turnover: 1990–1991 (pp. 95–744). Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics, NCES.

    Google Scholar 

  • Kaput Center for Research and Innovation in STEM Education. (2016). University of Massachusetts, Dartmouth. Retrieved from: http://www.kaputcenter.umassd.edu/projects/simcalc

  • KIPP: About KIPP. (2016). Retrieved from homepage: http://www.kipp.org

  • KnowHow2Go. (2013). Retrieved from homepage: http://www.knowhow2go.org

  • Milesi, C., Brown, K., Hawkley, L., Dropkin, E., & Schneider, B. (2014). Charting the impact of federal spending for education research: A bibliometric approach. Educational Researcher, 43(7), 361–370. doi:10.3102/0013189X14554002.

    CrossRef  Google Scholar 

  • National Center for Education Statistics. (2011a). College Navigator. Retrieved from http://nces.ed.gov/collegenavigator

  • National Center for Education Statistics. (2011b). The nation’s report card: Reading 2011 (NCES 2012–457). Washington, DC: Institute of Education Sciences, U.S. Department of Education.

    Google Scholar 

  • National Center for Education Statistics. (2016). Schools and Staffing Survey (SASS). Washington, DC: Institute of Education Sciences, U.S. Department of Education. Retrieved from: http://nces.ed.gov/surveys/sass/index.asp.

    Google Scholar 

  • National Research Council. (2002). Scientific research in education. Committee on Scientific Principles for Education Research. In R. J. Shavelson & L. Towne (Eds.), Center for education, division of behavioral and social sciences and education. Washington, DC: National Academy Press.

    Google Scholar 

  • National Science Foundation. (2010a). Preparing the next generation of stem innovators: Identifying and developing our nation’s human capital. National Science Foundation. Retrieved from: https://www.nsf.gov/nsb/publications/2010/nsb1033.pdf

  • National Science Foundation. (2010b). Research and Evaluation on Education in Science and Engineering (REESE). Program Solicitation. Retrieved from: http://www.nsf.gov/pubs/2010/nsf10586/nsf10586.pdf

  • No Child Left Behind Act of 2002, S. 1115, 107th Cong. (2002). Washington, DC: US Department of Education. Public Law 107–110.

    Google Scholar 

  • O’Muircheartaigh, C., & Hedges, L. V. (2014). Generalizing from unrepresentative experiments: A stratified propensity score approach. Journal of the Royal Statistical Society, Series C, 63(2), 195–210. doi:10.1111/rssc.12037.

    CrossRef  Google Scholar 

  • Organization for Economic Co-operation and Development, OECD/EU. (2015). Indicators of Immigrant Integration 2015: Settling In, OECD Publishing, Paris. 1–348. doi:http://dx.doi.org/10.1787/9789264234024-en

  • Pellegrino, J. W., Chudowsky, N., & Glaser, R. (Eds.). (2001). Knowing what students know: The science and design of educational assessment. Washington, DC, National Academies Press.

    Google Scholar 

  • Pellegrino, J. W., Wilson, M. R., Koenig, J. A., & Beatty, A. S. (Eds.). (2014). Developing assessments for the next generation science standards. Washington, DC: National Academies Press.

    Google Scholar 

  • Principled Assessment Designs for Inquiry. (2003). Retrieved from homepage: http://padi.sri.com

  • Quint, J., Zhu, P., Balu, R., Rappaport, S., & DeLaurentis, M. (2015). Scaling up the success for all model of school reform: Final report from the investing in innovation (i3) evaluation. New York: MDRC.

    Google Scholar 

  • Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In R. Murnane, & G. Duncan (Eds.), Whither opportunity? Rising inequality and the uncertain life chances of low-income children. New York: Russell Sage Foundation.

    Google Scholar 

  • Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007a). Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249–255.

    CrossRef  Google Scholar 

  • Ritter, S., Kulikowich, J., Lei, P. W., McGuire, C. L., & Morgan, P. (2007b). What evidence matters? A randomized field trial of Cognitive Tutor Algebra I. Frontiers in Artificial Intelligence and Applications, 162, 13.

    Google Scholar 

  • Roschelle, J., Tatar, D., Shechtman, N., Hegedus, S., Hopkins, B., Knudsen, J., & Stroter, A. (2007). Can a technology-enhanced curriculum improve student learning of important mathematics. Results from 7th grade, year, 1.

    Google Scholar 

  • Roschelle, J., Shechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., Knudsen, J., & Gallagher, L. P. (2010a). Integration of technology, curriculum, and professional development for advancing middle school mathematics three large-scale studies. American Educational Research Journal, 47(4), 833–878.

    CrossRef  Google Scholar 

  • Roschelle, J., Tatar, D., Hedges, L., & Shechtman, N. (2010b). Two perspectives on the generalizability of lessons from scaling up SimCalc. Society for Research on Educational Effectiveness.

    Google Scholar 

  • Rowan, B., Correnti, R., Miller, R., & Camburn, E. (2009). School improvement by design: Lessons from a study of comprehensive school reform programs. Consortium for Policy Research in Education, 1–62. doi:10.12698/cpre.2009.sii.

  • Rubin, B. (2005). Bayesian inference for causal effects. In C. R. Rao & D. K. Dey (Eds.), Handbook of statistics, volume 25: Bayesian thinking: Modeling and computation (pp. 1–16). Amsterdam: Elsevier.

    Google Scholar 

  • Schanzenbach, D. W. (2006). What have researchers learned from Project STAR? Brookings Papers on Education Policy, 9, 205–228.

    CrossRef  Google Scholar 

  • Schneider, B. (2015). 2014 AERA Presidential Address, The College Ambition Program: A realistic transition strategy for traditionally disadvantaged students. Educational Researcher, 44(7), 394–403.

    CrossRef  Google Scholar 

  • Schneider, B., & McDonald, S. K. (2007). Scale-up in practice: An introduction. In B. Schneider & S. K. McDonald (Eds.), Scale-up in education: Vol. 2: Issues in practice (pp. 1–12). Lanham: Rowman & Littlefield.

    Google Scholar 

  • Schneider, B., & Stevenson, D. (1999). The ambitious generation: America’s teenagers, motivated but directionless. New Haven: Yale University Press.

    Google Scholar 

  • SimCalc, the mathematics of change. (2011). Retrieved from: http://math.sri.com/index.html

  • Society for Research on Educational Effectiveness (SREE): Mission. (2010). Retrieved from https://www.sree.org/pages/mission.php

  • Songer, N. B., Myers, P., & Gotwals, A. W. (2007). DeepThink: Fostering and measuring learning progressions focused on deep thinking about biodiversity. Poster presented at the Principal Investigators Meeting of the National Science Foundation, Washington, DC.

    Google Scholar 

  • Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46(6), 610–631.

    CrossRef  Google Scholar 

  • Stevenson, D. L. (2000). The fit and misfit of sociological research and educational policy. In M. T. Hallinin (Ed.), Handbook of the sociology of education (pp. 547–563). Springer US.

    Google Scholar 

  • Success for All Foundation: Our Story. (2005). Retrieved from http://www.successforall.org/who-we-are

  • The Center for Health and the Social Sciences. (2016). High school students: Training early achievers for careers in heatlh (TEACH). The University of Chicago. Retrieved from: http://chess.uchicago.edu/TEACH

  • Tipton, E. (2011). Improving the external validity of randomized experiments using propensity score subclassification. Working Paper.

    Google Scholar 

  • Tipton, E. (2014). How generalizable is your experiment? An index for comparing experimental samples and populations. Journal of Educational and Behavioral Statistics, 39(6), 478–501.

    CrossRef  Google Scholar 

  • Tipton, E., Hedges, L. V., Borman, G., Vaden-Kiernan, M., Caverly, S., & Sullivan, K. (2014). Sample selection in randomized experiments: A new method using propensity score stratified sampling. Journal of Research on Educational Effectiveness, 7(1), 114–135. doi:10.1080/19345747.2013.831154.

    CrossRef  Google Scholar 

  • U.K., House of Commons. (2006). Science and technology committee: scientific advice, risk and evidence based policy making (Vol. 1). House of Commons.

    Google Scholar 

  • U.S. Department of Labor. (2011). A profile of the working poor, 2009. U.S. Department of Labor, U.W. Bureau of Labor Statistics. March 2011. Retrieved from: http://www.bls.gov/opub/reports/working-poor/archive/workingpoor_2009.pdf

  • Urban Prep Academies: History. (2012). Retrieved from: http://www.urbanprep.org/about/history-creed

  • Walters, P. B., Lareau, A., & Ranis, S. (Eds.). (2008). Education research on trial. Taylor & Francis.

    Google Scholar 

  • Weiss, C. H. (1982). Policy research in the context of diffuse decision making. The Journal of Higher Education, 53, 619–639.

    CrossRef  Google Scholar 

  • Weiss, C. H. (1989). Congressional committees as users of analysis. Journal of Policy Analysis and Management, 8(3), 411–431.

    CrossRef  Google Scholar 

  • What Works Clearinghouse. (2009). Intervention report: Cognitive tutor algebra I. Retrieved from https://www.mbaea.org/documents/filelibrary/pdf/cognitive_tutor/WWC_CogTutor_Report_July2009_B2A3C279D0481.pdf

  • What Works Clearinghouse. (2010). What works clearinghouse: Quick review of the report “Student Characteristics and Achievement in 22 KIPP Middle Schools. U.S. Department of Education, Institute of Education Sciences. Retrieved from: http://ies.ed.gov/ncee/wwc/Docs/QuickReview/kipp_092110.pdf

  • What Works Clearinghouse. (2016). What we do. Retrieved from: http://ies.ed.gov/ncee/wwc/WhatWeDo

  • Word, E., Johnston, J., Bain, H., Fulton, B. D., Zaharias, J. B., Achilles, C. M., Lintz, M. N. Folger, J. & Breda, C. (1990). The State of Tennessee’s student/teacher achievement ratio (STAR) Project. Tennessee Board of Education.

    Google Scholar 

  • World Education Research Association (WERA). (2016). Retrieved from homepage: https://www.wera.org

  • Yamada, H., & Bryk, A. S. (2016). Assessing the first two years’ effectiveness of statway® a multilevel model with propensity score matching. Community College Review. 0091552116643162.

Download references

Acknowledgement

This material is based upon work supported by the National Science Foundation under awards: No. DRL-131672 (CAP), No. OISE-1545684 (PIRE), and No. DRL-0815295 (ARC).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Barbara Schneider .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

McDonald, S.K., Schneider, B. (2017). Guiding Principles for Evaluating Evidence in Education Research. In: Eryaman, M., Schneider, B. (eds) Evidence and Public Good in Educational Policy, Research and Practice. Educational Governance Research, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-58850-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58850-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58849-0

  • Online ISBN: 978-3-319-58850-6

  • eBook Packages: EducationEducation (R0)