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Expanding Frontiers in Research Designs, Methods, and Measurement in Support of Evidence-Based Practice in Early Childhood Special Education

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Handbook of Early Childhood Special Education

Abstract

In this chapter, we discuss advances in research designs and methods useful for generating plausible causal evidence in early childhood special education. In addition, we describe contemporary perspectives about measurement and present example applications in early childhood special education. We begin the chapter by reviewing briefly the strengths, limitations, and implementation challenges associated with the conduct of randomized controlled trials or randomized field trials in early childhood special education research. This includes discussion of various design validity threats and issues related to causal inference. We then consider quasi-experimental and correlational research designs and discuss circumstances under which these designs might be appropriately and usefully applied to generate plausible causal evidence in early childhood special education research. Finally, we review current conceptualizations about reliability and validity and illustrate how generalizability theory and item response theory represent measurement advances for application in early childhood special education. Throughout the chapter, we illustrate the use of these designs and methodologies in early childhood education or early childhood special education through hypothetical examples or by presenting examples of published studies that have implemented these designs.

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Notes

  1. 1.

    It is important to acknowledge, that findings from RCTs or RFTs might also be susceptible to design validity threats (e.g., differential attrition, poor fidelity of intervention implementation).

  2. 2.

    Rasch models also exist for polychotomously scored items (see, e.g., Bond & Fox, 2007).

References

  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Austin, P. C. (2008). A critical appraisal of propensity score matching in the medical literature from 1996 to 2003. Statistics in Medicine, 27, 2037–2049.

    Article  PubMed  Google Scholar 

  • Austin, P. C. (2009). The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Medical Decision Making, 29, 661–677. doi:10.1177/0272989X09341755.

    Article  PubMed  Google Scholar 

  • Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399–424. doi:10.1080/00273171.2011.568786.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bailey, D. B., Nelson, L., Hebbeler, K., & Spiker, D. (2007). Modeling the impact of formal and informal supports for young children with disabilities and their families. Pediatrics, 120, e992–e1001. doi:10.1542/peds.2006-2775.

    Article  PubMed  Google Scholar 

  • Baldwin, B. (1989). A primer in the use and interpretation of structural equation models. Measurement and Evaluation in Counseling and Development, 22, 100–122.

    Google Scholar 

  • Bennett, K. K., Weigel, D. J., & Martin, S. S. (2002). Children’s acquisition of early literacy skills: Examining family contributions. Early Childhood Research Quarterly, 17, 295–317.

    Article  Google Scholar 

  • Biglan, A., Ary, D., & Wagenaar, A. C. (2000). The value of interrupted time-series experiments for community intervention research. Prevention Science, 1, 31–49.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bishop, C., Leite, W., & Snyder, P. (2016). Using propensity score weighting to reduce selection bias in large-scale data sets. Manuscript in preparation.

    Google Scholar 

  • Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 301–328). New York, NY: Springer. doi:10.1007/978-94-007-6094-3_15.

    Chapter  Google Scholar 

  • Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Boruch, R. F. (1975). Coupling randomized experiments and approximations to experiments in social program evaluation. Sociological Methods and Research, 4(1), 31–53.

    Article  Google Scholar 

  • Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco, CA: Holden Hay.

    Google Scholar 

  • Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2010). Time series analysis: Forecasting and control (4th ed.). Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Brennan, R. L. (2001). Generalizability theory. New York, NY: Springer.

    Book  Google Scholar 

  • Brennan, R. L. (2010). Generalizability theory and classical test theory. Applied Measurement in Education, 24, 1–21. doi:10.1080/08957347.2011.532417.

    Article  Google Scholar 

  • Bricker, D., Bailey, E., & Bruder, M. B. (1984). The efficacy of early intervention and the handicapped infant: A wise or wasted resources. Advances in Development and Behavioral Pediatrics, 5, 373–423.

    Google Scholar 

  • Bruckner, C. T., Yoder, P. J., & McWilliam, R. A. (2006). Generalizability and decision studies: An example using conversational language samples. Journal of Early Intervention, 28, 139–153.

    Article  Google Scholar 

  • Bryant, D. M., Burchinal, M., & Zaslow, M. (2011). Empirical approaches to strengthening the measurement quality: Issues in the development and use of quality measures in research and applied settings. In M. Zaslow, I. Martinez-Beck, K. Tout, & T. Halle (Eds.), Quality measurement in early childhood settings (pp. 33–47). Baltimore, MD: Brookes.

    Google Scholar 

  • Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54, 297–312.

    Article  PubMed  Google Scholar 

  • Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally.

    Google Scholar 

  • Capelleri, J. C., Darlington, R. B., & Trochim, W. M. K. (1994). Power analysis of cutoff-based randomized clinical trials. Evaluation Review, 18, 141–152.

    Article  Google Scholar 

  • Chen, D., Hu, B. Y., Fan, X., & Li, K. (2014). Measurement quality of the Chinese Early Childhood Program Rating Scale: An investigation using multivariate generalizability theory. Journal of Psychoeducational Assessment, 32, 236–248. doi:10.1177/0734282913504813.

    Article  Google Scholar 

  • Cochran, W. G. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics, 24, 295–313.

    Article  PubMed  Google Scholar 

  • Cook, T. D. (2008). “Waiting for life to arrive”: A history of the regression-discontinuity design in psychology, statistics and economics. Journal of Econometrics, 142, 636–654. doi:10.1016/j.jeconom.2007.05.002.

    Article  Google Scholar 

  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin.

    Google Scholar 

  • Cook, T. D., Shadish, W. R., & Wong, V. C. (2008). Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. Journal of Policy Analysis and Management, 27, 724–750. doi:10.1002/pam.20375.

    Article  Google Scholar 

  • Crocker, C., & Algina, J. (2008). Introduction to classical and modern test theory. Mason, OH: Cengage Learning.

    Google Scholar 

  • Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. R. (1972). The dependability of behavioral measurements: Theory of generalizability of scores and profiles. New York, NY: Wiley.

    Google Scholar 

  • Cronbach, L. J., Rajaratnam, N. R., & Gleser, G. C. (1963). Theory of generalizability: A liberalization of reliability theory. British Journal of Statistical Psychology, 16, 137–163.

    Article  Google Scholar 

  • Duncan, O. (1975). Introduction to structural equation models. New York, NY: Academic.

    Google Scholar 

  • Dunst, C. J., Hamby, D. W., & Brookfield, J. (2007). Modeling the effects of early childhood intervention variables on parent and family well-being. Journal of Applied Quantitative Methods, 2, 268–288.

    Google Scholar 

  • Elwert, F. (2013). Graphical causal models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 245–273). New York, NY: Springer. doi:10.1007/978-94-007-6094-3_13.

    Chapter  Google Scholar 

  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Fisher, L. D., Dixon, D. O., Herson, J., Frankowski, R. K., Hearron, M. S., & Peace, K. E. (1990). Intention to treat in clinical trials. In K. E. Peace (Ed.), Statistical issues in drug research and development (pp. 331–350). New York, NY: Marcel Dekker.

    Google Scholar 

  • Fox, L., Hemmeter, M. L., & Snyder, P. (2008). Teaching Pyramid Observation Tool for preschool classrooms: Pilot version [Instrument and manual]. Unpublished instrument. Nashville, TN: Vanderbilt.

    Google Scholar 

  • Garrett, P., Ferron, J., Ng’Andu, N., Bryant, D., & Harbin, G. (1994). A structural model for the developmental status of young children. Journal of Marriage and Family, 56, 147–163.

    Article  Google Scholar 

  • Gersten, R., & Dimino, J. A. (2006). RTI (response to intervention): Rethinking special education for students with reading disabilities (yet again). Reading Research Quarterly, 1, 99–108. doi:10.1598/RRQ.41.1.5.

    Article  Google Scholar 

  • Glynmour, M., & Greenland, S. (2008). Causal diagrams. In K. J. Rothman, S. Greenland, & T. Lash (Eds.), Modern epidemiology (3rd ed., pp. 183–209). Philadelphia, PA: Lippincott.

    Google Scholar 

  • Goldberger, A. S. (1972a). Selection bias in evaluating treatment effects: Some formal illustrations. Madison, WI: Institute on Poverty. Retrieved from http://www.irp.wisc.edu/publications/dps/pdfs/dp12372.pdf

  • Goldberger, A. S. (1972b). Selection bias in evaluating treatment effects: The case of interaction. Madison, WI: Institute on Poverty. Retrieved from http://www.irp.wisc.edu/publications/dps/pdfs/dp12972.pdf

  • Goldberger, A. S. (1972c). Structural equation methods in the social sciences. Econometrica, 40, 979–1001.

    Article  Google Scholar 

  • Goldberger, A. (1973). Structural equation models: An overview. In A. Goldberger & O. Duncan (Eds.), Structural equation models in the social sciences (pp. 1–18). New York, NY: Seminar Press.

    Google Scholar 

  • Goodwin, L. D., & Goodwin, W. L. (1991). Using generalizability theory in early childhood special education. Journal of Early Intervention, 15, 193–204. doi:10.1177/105381519101500208.

    Article  Google Scholar 

  • Gordon, R. A., Fujimoto, K., Kaestner, R., Korenman, S., & Abner, K. (2013). An assessment of the validity of the ECERS–R with implications for measures of child care quality and relations to child development. Developmental Psychology, 49, 146–160. doi:10.1037/a0027899.

    Article  PubMed  Google Scholar 

  • Gormley, W. J., Jr., Gayer, T., Phillips, D., & Dawson, B. (2005). The effects of universal pre-k on cognitive development. Developmental Psychology, 41, 872–884. doi:10.1037/0012-1649.41.6.872.

    Article  PubMed  Google Scholar 

  • Gormley, W. T., Jr., Phillips, D. A., Newmark, K., Welti, K., & Adelstein, S. (2011). Social-emotional effects of early childhood education programs in Tulsa. Child Development, 82, 2095–2109. doi:10.1111/j.1467-8624.211.01648.x.

    Article  PubMed  Google Scholar 

  • Greene, W. H. (1993). Econometric analysis (2nd ed.). New York, NY: Macmillan.

    Google Scholar 

  • Gu, X. S., & Rosenbaum, P. R. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2, 405–420.

    Google Scholar 

  • Guo, S., & Fraser, M. W. (2014). Propensity score analysis: Statistical methods and applications (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Guralnick, M. J. (Ed.). (1997). The effectiveness of early intervention. Baltimore, MD: Brookes.

    Google Scholar 

  • Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11, 1–12.

    Article  Google Scholar 

  • Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69, 201–209.

    Article  Google Scholar 

  • Hahs-Vaughn, D. L., & Onwuegbuzie, A. J. (2006). Estimating and using propensity score analysis with complex samples. The Journal of Experimental Education, 75, 31–65.

    Article  Google Scholar 

  • Hambleton, R. K. (1985). Item response theory. Norwell, MA: Kluwer-Nijhoff.

    Book  Google Scholar 

  • Harms, T., Clifford, R. M., & Cryer, D. (1998). Early childhood environment rating scale (Rev. ed.). New York, NY: Teachers College Press.

    Google Scholar 

  • Harvey, R. J., & Hammer, A. L. (1999). Item response theory. The Counseling Psychologist, 27, 353–383. doi:10.1177/0011000099273004.

    Article  Google Scholar 

  • Hedges, L. V. (2010, June). Generating plausible causal hypotheses. Presentation at the 2010 Institute of Education Sciences Research Conference, National Harbor, MD. Retrieved from http://ies.ed.gov/director/conferences/10ies_conference/presentations.asp

  • Hirano, K., & Imbens, G. (2001). Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes Research Methodology, 2, 259–278.

    Article  Google Scholar 

  • Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2006). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15, 199–236.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hong, G. (2012). Marginal mean weighting through stratification: A generalized method for evaluating multivalued and multiple treatments with nonexperimental data. Psychological Methods, 17, 44–60.

    Article  PubMed  Google Scholar 

  • Hong, G., & Yu, B. (2008). Effects of kindergarten retention on children’s social-emotional development: An application of propensity score method to multivariate, multilevel data. Developmental Psychology, 44, 407–421.

    Article  PubMed  Google Scholar 

  • Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. New York, NY: Guilford Press.

    Google Scholar 

  • Huggins, A. C., & Algina, J. (2015). The partial credit model and generalized partial credit model as constrained nominal response models, with applications in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 22, 308–318. doi: 10.1080/10705511.2014.937374

    Google Scholar 

  • Huggins, A. C., & Penfield, R. D. (2012). An NCME instructional module on population invariance in linking and equating. Educational Measurement: Issues and Practices, 31, 27–40.

    Article  Google Scholar 

  • Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615–635. doi:10.1016/j.jeconom.2007.05001.

    Article  Google Scholar 

  • Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86.

    Article  Google Scholar 

  • Institute of Education Sciences, National Center for Education Statistics (n. d.). Early childhood longitudinal program (ECLS). Retrieved from http://nces.ed.gov/ecls/index.asp

  • Joffe, M. M., Ten Have, T. R., Feldman, H. I., & Kimmel, S. E. (2004). Model selection, confounder control, and marginal structural models: Review and new applications. The American Statistician, 58, 272–279.

    Article  Google Scholar 

  • Justice, L. M., Bowles, R. P., & Skibbe, L. E. (2006). Measuring preschool attainment of print-concept knowledge: A study of typical and at-risk 3-to-5-year-old children using item response theory. Language, Speech, and Hearing Services in Schools, 37, 224–235.

    Article  PubMed  Google Scholar 

  • Justice, L. M., & Ezell, H. K. (2001). Word and print awareness in 4-year-old children. Child Language Teaching and Therapy, 17, 207–225.

    Article  Google Scholar 

  • Kamata, A., & Bauer, D. J. (2008). A note on the relation between factor analytic and item response theory models. Structural Equation Modeling, 15, 136–153.

    Article  Google Scholar 

  • Kamata, A., & Vaugn, B. K. (2004). An introduction to differential item functioning analysis. Learning Disabilities: A Contemporary Journal, 2(2), 49–69.

    Google Scholar 

  • Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22, 523–539.

    Article  Google Scholar 

  • Kaplan, D. (1999). An extension of the propensity score adjustment method for the analysis of group differences in MIMIC models. Multivariate Behavioral Research, 34, 467–492.

    Article  PubMed  Google Scholar 

  • Kim, W. H., & Park, E. Y. (2011). Causal relation between spasticity, strength, gross motor function, and functional outcome in children with cerebral palsy: A path analysis. Developmental Medicine & Child Neurology, 53, 68–73. doi:10.1111/j.1469-8749.2010.03777x.

    Article  Google Scholar 

  • Kline, R. B. (2011). Principles and practices of structural equation modeling. New York, NY: Guildford Press.

    Google Scholar 

  • Knight, C. R., & Winship, C. (2013). Causal implications of mechanistic thinking: Identification using directed acyclic graphs (DAGs). In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 275–299). New York, NY: Springer. doi:10.1007/978-94-007-6094-3_14.

    Chapter  Google Scholar 

  • Lambert, M. C., Williams, S. G., Morrison, J. W., Samms-Vaughan, M. E., Mayfield, W. A., & Thornburg, K. R. (2008). Are the indicators for the Language and Reasoning subscale of the Early Childhood Environment Rating Scale-Revised psychometrically appropriate for Caribbean classrooms? International Journal of Early Years Education, 16, 41–60. doi:10.1080/09669760801892219.

    Article  Google Scholar 

  • Lauritzen, S. (1996). Graphical models. Oxford, England: Clarendon.

    Google Scholar 

  • Lee, B. K., Lessler, J., & Stuart, E. A. (2011). Weight trimming and propensity score weighting. PLoS One, 6, 1–6.

    Google Scholar 

  • Lee, R., Zhai, F., Brooks-Gunn, J., Han, W., & Waldfogel, J. (2014). Head start participation and school readiness: Evidence from the Early Childhood Longitudinal Study-Birth Cohort. Developmental Psychology, 50, 202–215. doi:10.1037/a0032280.

    Article  PubMed  Google Scholar 

  • Leite, W. L. (2015). Latent growth modeling of longitudinal data with propensity score matched groups. In W. Pan & H. Bai (Eds.), Propensity score analysis: Fundamentals, developments, and extensions (pp. 191–216). New York, NY: Guilford Press.

    Google Scholar 

  • Lipsey, M. W., Farran, D. C., Bilbrey, C., Hofer, K. G., & Dong, N. (2011). Initial results of the evaluation of the Tennessee voluntary pre-k program. Retrieved from http://peabody.vanderbilt.edu/docs/pdf/pri/New%20Initial%20Results%20of%20the%20Evaluation%20of%20TN-VPK.pdf

  • Lomax, R. (1989). Covariance structure analysis: Extension and developments. In B. Thompson (Ed.), Advances in social science methodology (Vol. 1, pp. 171–204). Greenwich, CT: JAI Press.

    Google Scholar 

  • May, H. (2012). Non-equivalent comparison group designs. In H. Cooper (Ed.), APA handbook of research methods in psychology: Vol. 2. Research designs. Washington, DC: American Psychological Association. doi: 10.1037/13620-026.

    Google Scholar 

  • McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., & Burgette, L. F. (2013). A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Statistics in Medicine, 32, 3388–3414. doi:10.1002/sim.5753.

    Article  PubMed  PubMed Central  Google Scholar 

  • McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9, 403–425.

    Article  PubMed  Google Scholar 

  • McWilliam, R. A., & Ware, W. B. (1994). The reliability of observations of young children’s engagement: An application of generalizability theory. Journal of Early Intervention, 18, 34–46. doi:10.1177/105381519401800104.

    Article  Google Scholar 

  • Messick, S. (1993). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). Phoenix, AZ: American Council on Education and the Oryx Press.

    Google Scholar 

  • Morgan, S. L., & Harding, D. J. (2006). Matching estimators of causal effects: Prospects and pitfalls in theory and practices. Sociological Methods and Research, 35, 3–60. doi:10.1177/0049124106289164.

    Article  Google Scholar 

  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. Cambridge, MA: Cambridge University Press.

    Book  Google Scholar 

  • Mosteller, F., & Boruch, R. (Eds.). (2002). Evidence-matters: Randomized trials in education research. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • Mueller, R. O. (1997). Structural equation modeling: Back to basics. Structural Equation Modeling, 4, 353–369.

    Article  Google Scholar 

  • National Research Council. (2008). Early childhood assessment: Why, what, and how? Washington, DC: The National Academies Press.

    Google Scholar 

  • Neyman, J. (1990). On the application of probability theory to agricultural experiments. Essay on principles, section 9, translated (with discussion). Statistical Science, 5, 465–480. (Original work published 1923)

    Google Scholar 

  • Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufman.

    Google Scholar 

  • Pearl, J. (2009a). Causality: Models, reasoning, and inference (2nd ed.). Cambridge, MA: Cambridge University Press.

    Book  Google Scholar 

  • Pearl, J. (2009b). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146.

    Article  Google Scholar 

  • Pearl, J. (2010). The foundations of causal inference. Sociological Methodology, 40, 75–149.

    Article  Google Scholar 

  • Pearl, J. (2012). The causal foundations of structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 68–91). New York, NY: Guildford Press.

    Google Scholar 

  • Pedhazer, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Phillips, D. A., & Meloy, M. E. (2012). High-quality school-based pre-k can boost early learning for children with special needs. Exceptional Children, 78, 471–490.

    Google Scholar 

  • Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Nielsen and Lydiche (for Danmarks Paedagogiske Institut). (Original work published 1960)

    Google Scholar 

  • Reichardt, C. S. (1979). The statistical analysis of data from non-equivalent group designs. In T. D. Cook & D. T. Campbell (Eds.), Quasi-experimentation: Design and analysis issues for field settings (pp. 147–205). Boston, MA: Houghton Mifflin.

    Google Scholar 

  • Reise, S. P., Ainsworth, A. T., & Haviland, M. G. (2005). Item response theory: Fundamentals, applications, and promise in psychological research. Current Directions in Psychological Science, 14, 95–101.

    Article  Google Scholar 

  • Robins, J. M., & Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association, 90, 122–129.

    Article  Google Scholar 

  • Robins, J. M., Rotnitzky, A., & Zhao, L.-P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106–121.

    Article  Google Scholar 

  • Robins, J., Sued, M., Lei-Gomez, Q., & Rotnitzky, A. (2007). Comment: Performance of double-robust estimators when “inverse probability” weights are highly variable. Statistical Science, 22, 544–559. doi:10.1214/07-STS227D.

    Article  Google Scholar 

  • Rosenbaum, P. R. (1987). Model-based direct adjustment. Journal of the American Statistical Association, 82, 387–394.

    Article  Google Scholar 

  • Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York, NY: Springer.

    Book  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983a). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983b). Addressing sensitivity to an unobserved binary covariate in an observational study with a binary outcome. Journal of the Royal Statistical Society: Series B Methodological, 45, 212–218.

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using sub-classification on the propensity score. Journal of the American Statistical Association, 79, 516–524.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39, 33–38.

    Google Scholar 

  • Rossi, P. H., & Freeman, H. E. (1989). Evaluation: A systematic approach (4th ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688–701.

    Article  Google Scholar 

  • Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2, 1–26.

    Google Scholar 

  • Rubin, D. B. (1986). Comment: Which ifs have causal answers? Journal of the American Statistical Association, 81, 961–962.

    Google Scholar 

  • Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757–763.

    Article  PubMed  Google Scholar 

  • Ruzek, E., Burchinal, M., Farkas, G., & Duncan, G. J. (2013). The quality of toddler child care and cognitive skills at 24 months: Propensity score analysis results from the ECLS-B. Early Childhood Research Quarterly, 29, 12–21.

    Article  Google Scholar 

  • Sandall, S., McLean, M. E., & Smith, B. J. (2000). DEC recommended practices in early intervention/early childhood special education. Longmont, CO: Sopris.

    Google Scholar 

  • Sauer, B., & Vander Weele, T. J. (2013). Supplement 2. Use of directed acyclic graphs. In P. Velentgas, N. A. Dreyer, P. Nourjah, S. R. Smith, & M. M. Torchia (Eds.), Developing a protocol for observational comparative effectiveness research: A user’s guide (AHRQ Publication No. 12 [13]-EHC099, pp. 177–184). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from www.effectivehealthcare.ahrq.gov/Methods-OCER.cfm

  • Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13, 279–313. doi:10.1037/a0014268.

    Article  PubMed  Google Scholar 

  • Scharfstein, D. O., Rotnitzky, A., & Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric non-response models (with discussion). Journal of the American Statistical Association, 94, 1096–1146.

    Article  Google Scholar 

  • Schochet, P. Z. (2008). Technical methods report: Statistical power for regression discontinuity designs in education evaluations (NCEE 2008-4026). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, US Dept of Education. Retrieved from http://ies.ed.gov/ncee/pdf/20084026.pdf

  • Schochet, P., Cook, T., Deke, J., Imbens, G., Lockwood, J. R., Porter, J., & Smith, J. (2010). Standards for regression discontinuity designs. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_rd.pdf

  • Setoguchi, S., Schneeweiss, S., Brookhart, M. A., Glynn, R. J., & Cook, E. F. (2008). Evaluating uses of data mining techniques in propensity score estimation: A simulation study. Pharmacoepidemiology and Drug Safety, 17, 546–555. doi:10.1002/pds.1555.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shadish, W. R. (2010). Campbell and Rubin: A primer and comparison of their approaches to causal inference in field settings. Psychological Methods, 15, 3–17.

    Article  PubMed  Google Scholar 

  • Shadish, W. R. (2011). Randomized controlled studies and alternative designs in outcome studies: Challenges and opportunities. Research on Social Work Practice, 21, 636–643. doi:10.1177/1049731511403324.

    Article  Google Scholar 

  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.

    Google Scholar 

  • Shadish, W. R., Matt, G. E., Navarro, A. M., & Phillips, G. (2000). The effects of psychological therapies under clinically representative conditions: A meta-analysis. Psychological Bulletin, 126, 512–529.

    Article  PubMed  Google Scholar 

  • Shavelson, R. J., & Towne, L. (Eds.). (2002). Scientific research in education. Washington, DC: National Academy Press.

    Google Scholar 

  • Shavelson, R., & Webb, N. (1991). Generalizability theory: A primer. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Shumway, R. H., & Stoffer, D. S. (2011). Time series analysis and its applications. New York, NY: Springer.

    Book  Google Scholar 

  • Snyder, P. (2006). Best-available research evidence: Impact on research in early childhood. In V. Buysse & P. W. Wesley (Eds.), Evidence-based practice in the early childhood field (pp. 35–70). Washington, DC: Zero to Three Press.

    Google Scholar 

  • Snyder, P. (2011). Implementing randomized controlled trials in preschool settings that include young children with disabilities: Considering the context of Strain and Bovey. Topics in Early Childhood Special Education, 31, 162–165. doi:10.1177/0271121411418005.

    Article  Google Scholar 

  • Snyder, P. A., Hemmeter, M. L., Fox, L., Bishop, C. C., & Miller, M. D. (2013). Developing and gathering psychometric evidence for a fidelity instrument: The Teaching Pyramid Observation Tool-Pilot Version. Journal of Early Intervention, 35, 150–172. doi:10.1177/1053815113516794.

    Article  Google Scholar 

  • Snyder, P., McLean, M., & Bailey, D. B. (2014). Types and technical characteristics of assessment instruments. In M. McLean, M. L. Hemmeter, & P. Snyder (Eds.), Essential elements for assessing infants and preschoolers with special needs (pp. 37–86). Boston, MA: Pearson.

    Google Scholar 

  • Snyder, P., Thompson, B., McLean, M. E., & Smith, B. J. (2002). Examination of quantitative methods used in early intervention research: Linkages with recommended practices. Journal of Early Intervention, 25, 137–150.

    Article  Google Scholar 

  • Spearman, C. (1907). Demonstration of formulae for the true measurement of correlation. American Journal of Psychology, 18, 72–101.

    Article  Google Scholar 

  • Spearman, C. (1913). Correlations of sums and differences. British Journal of Psychology, 5, 417–426.

    Google Scholar 

  • Spirtes, P., Glynmour, C. N., & Schein, R. (2001). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT.

    Google Scholar 

  • SRI International. (2014). National early intervention longitudinal study (NEILS). Retrieved from http://www.sri.com/work/projects/national-early-intervention-longitudinal-study-neils

  • Steiner, P. M., Cook, T. D., & Shadish, W. R. (2011). On the importance of reliability covariate measurement in selection bias adjustments using propensity scores. Journal of Educational and Behavioral Statistics, 36, 213–236. doi:10.3102/1076998610375835.

    Article  Google Scholar 

  • Steiner, P. M., Cook, T. D., Shadish, W. R., & Clark, M. H. (2010). The importance of covariate selection in controlling for selection bias in observational studies. Psychological Methods, 15, 250–267.

    Article  PubMed  Google Scholar 

  • Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25, 1–21.

    Article  PubMed  PubMed Central  Google Scholar 

  • Stürmer, T., Rothman, K. J., Avorn, J., & Glynn, R. J. (2010). Treatment effects in the presence of unmeasured confounding: Dealing with observations in the tails of the propensity score distribution—A simulation study. American Journal of Epidemiology, 172, 843–854. doi:10.1093/aje/kwq198.

    Article  PubMed  PubMed Central  Google Scholar 

  • Suen, H. K., Lu, C., Neisworth, J. T., & Bagnato, S. J. (1993). Measurement of team decision-making through generalizability theory. Journal of Psychoeducational Assessment, 11, 120–132. doi:10.1177/073428299301100202.

    Article  Google Scholar 

  • Sullivan, A. L., & Field, S. (2013). Do preschool special education services make a difference in kindergarten reading and mathematics skills? A propensity score weighting analysis. Journal of School Psychology, 51, 243–260. doi:10.1016/j.jsp.2012.12.004.

    Article  PubMed  Google Scholar 

  • Swaminathan, H., & Algina, J. (1977). Analysis of quasi-experimental time-series designs. Multivariate Behavioral Research, 12, 111–131.

    Article  PubMed  Google Scholar 

  • Thistlethwait, D., & Campbell, D. (1960). Regression-discontinuity analysis: An alternative to the ex-post facto experiment. Journal of Educational Psychology, 51, 309–317.

    Article  Google Scholar 

  • Thompson, B. (1998, July). The ten commandments of good structural equation modeling behavior: A user-friendly, introductory primer on SEM. Paper presented at the Annual Meeting of the Department of Education, Office of Special Education Programs (OSEP) Project Directors’ Conference, Washington, DC. Retrieved from http://files.eric.ed.gov/fulltext/ED420154.pdf

  • Thompson, B. (2003). Score reliability: Contemporary thinking on reliability issues. Thousand Oaks, CA: Sage.

    Book  Google Scholar 

  • Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York, NY: Guilford Press.

    Google Scholar 

  • Trochim, W. (1984). Research design for program evaluation. Beverly Hills, CA: Sage.

    Google Scholar 

  • Trochim, W. (Ed.). (1986). Advances in quasi-experimental design and analysis. In New directions for program evaluation series (No. 31). San Francisco, CA: Jossey Bass. Retrieved from http://www.socialresearchmethods.net/kb/advquasi.php

  • Tuckwiller, E. D., Pullen, P. C., & Coyne, M. D. (2010). The use of the regression discontinuity design in tiered intervention research: A pilot study exploring vocabulary instruction for at-risk kindergarteners. Learning Disabilities Research and Practices, 25(3), 137–150. doi:10.1111/j.1540-5826.2010.00311.x.

    Article  Google Scholar 

  • Velicer, W. F., & Fava, J. L. (2003). Time series analysis. In J. B. Weiner (Series Ed.), Research methods in psychology: Vol. 2. Handbook of psychology (pp. 581–606). New York, NY: Wiley.

    Google Scholar 

  • Webb, N. M., Shavelson, R. J., & Haertel, E. H. (2007). Reliability coefficients and generalizability theory. In C. R. Rao (Ed.), Handbook of statistics (Volume on psychometrics, Vol. 26, pp. 81–124). London, England: Elsevier.

    Google Scholar 

  • Weiland, C., Wolfe, C. B., Hurwitz, M. D., Clements, D. H., Sarama, J. H., & Yoshikawa, H. (2012). Early mathematics assessment: Validation of the short form of a prekindergarten and kindergarten mathematics measure. Educational Psychology, 32, 311–333. doi:10.1080/10443410.2011.654190.

    Article  Google Scholar 

  • Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children’s mathematics, language, literacy, executive function, and emotional skills. Child Development, 84, 2112–2130. doi:10.1111/cdev.12099.

    Article  PubMed  Google Scholar 

  • West, S. G., Biesanz, J. C., & Pitts, S. C. (2000). Causal inference and generalization in field settings: Experimental and quasi-experimental designs. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 40–84). New York, NY: Cambridge University Press.

    Google Scholar 

  • West, S. G., & Thoemmes, F. (2010). Campbell’s and Rubin’s perspectives on causal inference. Psychological Methods, 15, 18–37.

    Article  PubMed  Google Scholar 

  • Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: Neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of Clinical Epidemiology, 63, 826–833. doi:10.1016/j.jclinepi.2009.11.020.

    Article  PubMed  PubMed Central  Google Scholar 

  • White, K. R., & Pezzino, J. (1986). Ethical, practical, and scientific considerations of randomized experiments in early education special education. Topics in Early Childhood Special Education, 6, 100–116.

    Article  Google Scholar 

  • Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and expectations. American Psychologists, 54, 594–604. doi:10.1037/0003-066X.54.8.594.

    Article  Google Scholar 

  • Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.

    Article  Google Scholar 

  • Wolery, M. (2011). Intervention research: The importance of fidelity measurement. Topics in Early Childhood Special Education, 31, 155–157. doi:10.1177/0271121411408621.

    Article  Google Scholar 

  • Wong, V. C., Cook, T. D., Barnett, S., & Jung, K. (2008). An effectiveness-based evaluation of five state pre-kindergarten programs. Journal of Policy Analysis and Management, 27, 122–154. doi:10.1002/pam.20310.

    Article  Google Scholar 

  • Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.

    Google Scholar 

  • Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5, 161–215.

    Article  Google Scholar 

  • Wright, B. D. (1968). Sample-free test calibration and person measurement. In Proceedings of the 1967 Invitational Conference on Testing Problems (pp. 85–101). Princeton, NJ: Educational Testing Service.

    Google Scholar 

  • Wright, D. B. (2006). Comparing groups in a before-after design: When t test and ANCOVA produce different results. British Journal of Educational Psychology, 76, 663–675. doi:10.1348/000709905X52210.

    Article  PubMed  Google Scholar 

  • Yen, W. M., & Fitzpatrick, A. R. (2006). Item response theory. In R. L. Brennan (Ed.), Educational measurement (4th ed., pp. 111–153). Westport, CT: Praeger.

    Google Scholar 

  • Zaidman-Zait, A., Mirenda, P., Zumbo, B. D., Wellington, S., Dua, V., & Kalynchuk, K. (2010). An item response theory analysis of the Parenting Stress Index-Short Form with parents of children with autism spectrum disorders. Journal of Child Psychology and Psychiatry, 51, 1269–1277. doi:10.1111/j.1469-7610.2010.02266.x.

    Article  PubMed  Google Scholar 

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Acknowledgments

Work reported in this paper was supported, in part, by a grant from the National Center for Special Education Research in the Institute of Education Sciences to the University of Florida (R324B120002). The opinions expressed are those of the authors, not the funding agency.

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Bishop, C.D., Snyder, P.A., Algina, J., Leite, W. (2016). Expanding Frontiers in Research Designs, Methods, and Measurement in Support of Evidence-Based Practice in Early Childhood Special Education. In: Reichow, B., Boyd, B., Barton, E., Odom, S. (eds) Handbook of Early Childhood Special Education. Springer, Cham. https://doi.org/10.1007/978-3-319-28492-7_27

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