Angrist, J. D. and Pischke, J. S. 2010. ‘The credibility revolution in empirical economics: how better research design is taking the con out of econometrics’, Journal of Economic Perspectives, 24(2): 3–30.CrossRefGoogle Scholar

Angrist, J. D. and Pischke, J. S. 2014. Mastering ‘Metrics: The Path from Cause to Effect. Princeton, NJ: Princeton University Press.Google Scholar

Benjamini, Y. and Hochberg, Y. 1995. ‘Controlling the false discovery rate: a practical and powerful approach to multiple testing’, Journal of the Royal Statistical Society. Series B (Methodological), 289–300.Google Scholar

Brown, S. R. and Melamed, L. E. 1990. Experimental Design and Analysis. (Quantitative Applications in the Social Sciences, No. 74). London: Sage Publications.CrossRefGoogle Scholar

Campbell, D. T. and Stanley, J. C. 1966. Experimental and Quasi-experimental Designs for Research. Chicago: Rand-McNally.Google Scholar

Favero, N. and Bullock, J. 2015. ‘How (not) to solve the problem: an evaluation of scholarly responses to common source bias’, Journal of Public Administration Research and Theory, 25(1): 285–308.CrossRefGoogle Scholar

Freedman, D. 2008. ‘On regression adjustment to experimental data’, Advances in Applied Mathematics, 40(2): 180–93.CrossRefGoogle Scholar

Gerber, A. S. and Green, D. P. 2012. Field Experiments: Design, Analysis and Interpretation. New York: W.W. Norton and Company.Google Scholar

Graham, J. W. 2012. Missing Data: Analysis and Design. New York: Springer.CrossRefGoogle Scholar

Grimmelikhuijsen, S. and Klijn, A. 2015. ‘The effects of judicial transparency on public trust: evidence from a field experiment’, Public Administration, 93: 995–1011. doi: 10.1111/padm.12149.CrossRefGoogle Scholar

Groeneveld, S., Tummers, L., Bronkhorst, B., Ashikali, T., and Van Thiel, S. 2014. ‘Quantitative methods in public administration: their use and development through time’, International Public Management Journal, 18(1): 61–86.CrossRefGoogle Scholar

Guala, F. 2005. The Methodology of Experimental Economics, Cambridge, Cambridge University Press.CrossRefGoogle Scholar

Holland, P. W. 1986. ‘Statistics and causal inference’. Journal of the American Statistical Association, 81(396): 945–60.Google Scholar

Holm, S. 1979. ‘A simple sequentially rejective multiple test procedure’, Scandinavian Journal of Statistics, 6(2): 65–70.Google Scholar

Jakobsen, M. and Jensen, R. 2015. ‘Common method bias in public management studies’, International Public Management Journal, 18(1): 3–30.CrossRefGoogle Scholar

James, O., Jilke, S., Petersen, C., and Van de Walle, S. 2016. ‘Citizens’ blame of politicians for public service failure: experimental evidence about blame reduction through delegation and contracting’, Public Administration Review, 76(1): 83–93.CrossRefGoogle Scholar

James, O. and Van Ryzin, G. 2015. ‘Motivated reasoning about public performance: an experimental study of how citizens judge Obamacare’, Paper presented to PMRA 2015 Annual Conference, University of Minnesota, Minneapolis, MN.Google Scholar

Jilke, S., Van Ryzin, G., and Van de Walle, A. 2015. ‘Responses to decline in marketized public services: an experimental evaluation of choice-overload’, Journal of Public Administration Research and Theory, doi: 10.1093/jopart/muv021.CrossRefGoogle Scholar

Kirk, R. E. 2012. Experimental Design: Procedures for the Behavioral Sciences: Procedures for the Behavioral Sciences. London: Sage Publications.Google Scholar

LaLonde, R. J. 1986. ‘Evaluating the econometric evaluations of training programs with experimental data’, The American Economic Review, 76(4): 604–20.Google Scholar

Lin, W. 2013. ‘Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique’, The Annals of Applied Statistics, 7(1): 295–318.CrossRefGoogle Scholar

Meier, K. J. and O’Toole, L. J. 2013. ‘Subjective organizational performance and measurement error: common source bias and spurious relationships’, Journal of Public Administration Research and Theory, 23(2): 429–56.CrossRefGoogle Scholar

Morgan, S. L. and Winship, C. 2015. Counterfactuals and Causal Inference. 2nd Edition. Cambridge: Cambridge University Press.Google Scholar

Murphy, K. R., Myors, B., and Wolach, A., 2014. Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests. New York: Routledge.CrossRefGoogle Scholar

Remler, D. K. and Van Ryzin, G. G. 2015. Research Methods in Practice: Strategies for Description and Causation. London: Sage Publications.Google Scholar

Rubin, D. B. 1974. ‘Estimating causal effects of treatment s in randomized and non-randomized studies’, Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar

Shadish, W. R., Cook, T. D., and Campbell, D. T. 2002. Experimental and Quasi-experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin Company.Google Scholar

Stuart, E. A. 2010. ‘Matching methods for causal inference: a review and a look forward’, Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25(1): 1–21.CrossRefGoogle Scholar

Wendorf, C. A. 2004. ‘Primer on multiple regression coding: common forms and the additional case of repeated contrasts’, Understanding Statistics, 3(1): 47–57.CrossRefGoogle Scholar