Bayesian probability and statistics in management research: A new horizon

Journal of Management

Volumn 39, Issue 1, 2013, Pages 5-13

  Zyphur, Michael J ^a   Frederick, Frederick L ^b  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b RICE UNIVERSITY   (United States)

Author keywords

Bayes; Bayesian; Frequentist; Philosophy of science; Probability; Research methods; Statistics

Indexed keywords

EID: 84906701524     PISSN: 01492063     EISSN: 15571211     Source Type: Journal    
DOI: 10.1177/0149206312463183     Document Type: Article

References (89)

1. Aldrich, J. 2008. R. A. Fisher on Bayes and Bayes’ theorem. Bayesian Analysis, 3: 161-170.
2. Asparouhov, T., & Muthén, B. 2010. Bayesian analysis of latent variable models using Mplus. Unpublished manuscript. www.statmodel.com/download/BayesAdvantages18.pdf.
3. Bandyopadhyay, P. S., & Forster, M. R. 2011. Handbook of the philosophy of science: Philosophy of statistics, vol. 7. Oxford, UK: Elsevier.
4. Bayarri, M. J., & Berger, J. O. 2004. The interplay of Bayesian and frequentist analysis. Statistical Science, 19: 58-80.
5. Bayes, T. P. 1763. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53: 370-418.
6. Berger, J. 2003. Could Fisher, Jeffreys, and Neyman have agreed on testing? Statistical Science, 18: 1-32.
7. Berger, J. O., Boukai, B., & Wang, Y. 1997. Unified frequentist and Bayesian testing of a precise hypothesis. Statistical Science, 12: 133-160.
8. Berger, J. O., Brown, L. D., & Wolpert, R. L. 1994. A unified conditional frequentist and Bayesian test for fixed and sequential simple hypothesis testing. Annals of Statistics, 22: 1787-1807.
9. Berger, J. O., & Selke, T. 1987. Testing a point null hypothesis: The irreconcilability of p values and evidence. Journal of the American Statistical Association, 82: 112-122. Carnap, R. 1950. L.gical Foundations of Probability. Chicago: University of Chicago Press.
10. Cartwright, N. D. 2006. Well-ordered science: Evidence for use. Philosophy of Science, 73: 981-990.
11. Cartwright, N. D. 2007. Hunting causes and using them. Cambridge, UK: Cambridge University Press.
12. Cartwright, N. D. 2010. Predicting “it will work for us”: (Way) beyond statistics. In P. M. Illari, F. Russo, & J. Williamson (Eds.), Causality in the sciences: 750-768. Oxford, UK: Oxford University Press.
13. Cheng, J., & Madigan, D. 2010. Bayesian approaches to aspects of the Vioxx trials: Non-ignorable dropout and sequential meta-analysis. In A. O’Hagan & M. West (Eds.), The Oxford handbook of applied Bayesian analysis: 51-68. Oxford, UK: Oxford University Press.
14. Cox, D., & Mayo, D. G. 2009. Objectivity and conditionality in frequentist inference. In D. G. Mayo & A. Spanos (Eds.), Error and inference: Recent exchanges on experimental reasoning, reliability, and the objectivity and rationality of science: 276-304. Cambridge, UK: Cambridge University Press.
15. Danziger, K. 1994. Constructing the subject: Historical origins of psychological research. Cambridge, UK: Cambridge University Press.
16. Daston, L. 1994. How probabilities came to be subjective and objective. Historia Mathematica, 21: 330-344.
17. Daston, L. 1995. Classical probability in the Enlightenment. Princeton, NJ: Princeton University Press.
18. De Finetti, B. 1931/1989. Probabilism. Erkenntnis (M. C. Di Maio, M. C. Galavotti, & R. C. Jeffrey, Trans.), 31: 169-223.
19. Efron, B. 2005. Bayesians, frequentists, and statisticians. Journal of the American Statistical Association, 100: 1-5.
20. Efron, B. 2010. L.rge-scale inference: Empirical Bayes methods for estimation, testing, and prediction. Cambridge, UK: Cambridge University Press.
21. Ellis, R. L. 1842. On the foundations of the theory of probabilities. Transactions of the Cambridge Philosophical Society, 8: 1-6. http://www.biodiversitylibrary.org/item/49441#page/17/mode/1up.
22. Ellis, R. L. 1854. Remarks on the fundamental principle of the theory of probabilities. Transactions of the Cambridge Philosophical Society, 9: 605-607. http://www.biodiversitylibrary.org/item/19878#page/701/ mode/1up.
23. Fienberg, S. E. 2006. When did Bayesian inference become “Bayesian”? Bayesian Analysis, 1: 1-40.
24. Fisher, R. A. 1935a. Design of experiments. Edinburgh, UK: Oliver & Boyd.
25. Fisher, R. A. 1935b. The logic of inductive inference. Journal of the Royal Statistical Society, 98: 39-54.
26. Gabbay, D. M., Hartmann, S., & Woods, J. 2011. Handbook of the history of logic: Inductive logic, vol. 10. Oxford, UK: Elsevier.
27. Galavotti, M. C. 2005. Philosophical introduction to probability. Palo Alto, CA: CSL..
28. Galavotti, M. C. 2011. The modern epistemic interpretations of probability: L.gicism and subjectivism. In D. M. Gabbay, S. Hartmann, & J. Woods (Eds.), Handbook of the history of logic: Inductive logic, vol. 10: 153-203. Oxford, UK: Elsevier.
29. Garrett, E. S., & Zeger, S. L. 2000. L.tent class model diagnosis. Biometrics, 56: 1055-1067.
30. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. 2003. Bayesian data analysis (2nd ed.). Boca Raton, FL. Chapman & Hall.
31. Gephart, R. P. 2006. Ethnostatistics and organizational research methods. Organizational Research Methods, 9: 417-431.
32. Gigerenzer, G. 1987. Probabilistic thinking and the fight against subjectivity. In L. Krüger, G. Gigerenzer, & M. S. Morgan (Eds.), The probabilistic revolution, vol. 2: 11-33. Cambridge, MA: MIT Press.
33. Gigerenzer, G. 1993. The superego, the ego, and the id in statistical reasoning. In G. Keren & C. L.wis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues: 311-339. Hillsdale, NJ: L.wrence Erlbaum.
34. Gigerenzer, G. 2004. Mindless statistics. Journal of Socio-Economics, 33: 587-606.
35. Gigerenzer, G., & Murray, D. J. 1987. Cognition as intuitive statistics. New York: Psychology Press.
36. Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Krüger, L. 1989. The empire of chance: How probability changed science and everyday life. Cambridge, UK: Cambridge University Press.
37. Gill, J. 2007. Bayesian methods: A social and behavioral sciences approach (2nd ed.). Boca Raton, FL. Chapman & Hall.
38. Good, I. J. 1989. Good thinking: The foundations of probability and its applications. Mineola, NY: Dover.
39. Hacking, I. 1983. Representing and intervening: Introductory topics in the philosophy of natural science. Cambridge, UK: Cambridge University Press.
40. Hacking, I. 1990. The taming of chance. Cambridge, UK: Cambridge University Press.
41. Hacking, I. 1992. Statistical language, statistical truth and statistical reason: The self-authentication of a style of scientific reasoning. In E. McMullin (Ed.), The social dimensions of science, vol. 3: 130-157. Notre Dame, IN: University of Notre Dame Press.
42. Hacking, I. 2001. An introduction to probability and inductive logic. Cambridge, UK: Cambridge University Press.
43. Hacking, I. 2002. Historical ontology. Cambridge, MA: Harvard University Press.
44. Hacking, I. 2006. The emergence of probability: A philosophical study of early ideas about probability, induction and statistical inference. Cambridge, UK: Cambridge University Press.
45. Hájek, A. 2009. Fifteen arguments against hypothetical frequentism. Erkenntnis, 70: 211-235.
46. Howie, D. 2002. Interpreting probability: Controversies and developments in the early twentieth century. Oxford, UK: Oxford University Press.
47. Howson, C., & Urbach, P. 2006. Scientific reasoning: The Bayesian approach (3rd ed.). Peru, IL. Open Court.
48. Jaynes, E. T. 2003. Probability theory: The logic of science. Cambridge, UK: Cambridge University Press.
49. Jeffrey, R. 2004. Subjective probability: The real thing. Cambridge, UK: Cambridge University Press.
50. Jeffreys, H. 1939/1998. Theory of probability (3rd ed.). Oxford, UK: Clarendon.
51. Kadane, J. B. 1995. Prime time for Bayes. Controlled Clinical Trials, 16: 313-318.
52. Kadane, J. B. 1999. The insignificance of null hypothesis significance testing. Political Research Quarterly, 52: 647-674.
53. Keynes, J. M. 1921/2008. A treatise on probability. Seaside, OR: Rough Draft Printing.
54. Krüger, L., Daston, L. J., & Heidelberger, M. 1987. The probabilistic revolution: Ideas in history, vol. 1. Cambridge, MA: MIT Press.
55. Krüger, L., Gigerenzer, G., & Morgan, M. S. (1990). The probabilistic revolution: Ideas in the sciences, vol. 2. Cambridge, MA: MIT Press.
56. Kruschke, J. K. 2010a. Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1: 658-676.
57. Kruschke, J. K. 2010b. What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14: 293-300.
58. Kruschke, J. K. 2011. Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6: 299-312.
59. Lange, M. 2011. Hume and the problem of induction. In D. M. Gabbay, S. Hartmann, & J. Woods (Eds.), Handbook of the history of logic: Inductive logic, vol. 10: 43-91. Oxford, UK: Elsevier.
60. L.place, P. S. 1774/1986. Memoir on the probability of the causes of events (S. M. Stigler, Trans.). Statistical Science, 3: 364-378.
61. L.tour, B. 2004. Why has critique run out of steam? From matters of fact to matters of concern. Critical Inquiry, 30: 225-248.
62. L.w, J. 2004. After method: Mess in social science research. London: Routledge.
63. L.e, S.-Y. 2007. Structural equation modeling: A Bayesian perspective. Chichester, UK: Wiley.
64. L.ttle, R. J. 2006. Calibrated Bayes: A Bayes/frequentist roadmap. American Statistician, 60: 213-223.
65. MacKenzie, D. 1981. Statistics in Britain, 1865-1930: The social construction of scientific knowledge. Edinburgh, UK: Edinburgh University Press.
66. Maher, P. 2010. What is probability? Unpublished manuscript. http://patrick.maher1.net/preprints/pop.pdf.
67. Mayo, D. G., & Cox, D. 2009. Frequentist statistics as a theory of inductive inference. In D. G. Mayo & A. Spanos (Eds.), Error and inference: Recent exchanges on experimental reasoning, reliability, and the objectivity and rationality of science: 247-275. Cambridge, UK: Cambridge University Press.
68. Muthén, B. 2010. Bayesian analysis in Mplus: A brief introduction. Unpublished manuscript. www.statmodel.com/ download/IntroBayesVersion%203.pdf.
69. Muthén, B., & Asparouhov, T. In press. Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods.
70. Neyman, J. 1937. Outline of a theory of statistical estimation based on the classical theory of probability. Philosophical Transactions of the Royal Society of London, Series A, 236: 333-380.
71. Neyman, J., & Pearson, E. 1933. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A, 231: 289-337.
72. Peirce, C. S. 1923/2010. Chance, love, and logic: Philosophical essays (with a supplemental essay by John Dewey). Charleston, SC: Nabu Press.
73. Popper, K. 1959/2002. The logic of scientific discovery. New York: Routledge.
74. Porter, T. M. 1986. The rise of statistical thinking, 1820-1900. Princeton, NJ: Princeton University Press.
75. Press, S. J. 2010. Subjective and objective Bayesian statistics: Principles, models, and applications (2nd ed.). New York: John Wiley.
76. Ramsey, F. P. 1926/1990. Truth and probability. In D. H. Mellor (Ed.), Philosophical papers: 52-94. Cambridge, UK: Cambridge University Press.
77. Savage, L. J. 1954. Foundations of statistics. New York: John Wiley.
78. Selke, T., Bayarri, M. J., & Berger, J. O. 2001. Calibration of p values for testing precise null hypotheses. American Statistician, 55: 62-71.
79. Senn, S. 2011. You may believe you are a Bayesian but you are probably wrong. Rationality, Markets, and Morals, 2: 48-66.
80. Shannon, C. E. 1948. A mathematical theory of communication. Bell System Technical Journal, 27: 379-423.
81. Shapin, S., & Schaffer, S. 1985. L.viathan and the air-pump. Princeton, NJ: Princeton University Press.
82. Stigler, S. M. 1986. The history of statistics: The measurement of uncertainty before 1900. Cambridge, MA: Harvard University Press.
83. Venn, J. 1866/2006. The logic of chance. Mineola, NY: Dover.
84. Verran, H. 2001. Science in an African logic. Chicago: University of Chicago Press.
85. von Mises, R. 1957. Probability, statistics, and truth. Mineola, NY: Dover.
86. Wagenmakers, E.-J., L.e, M., L.dewyckx, T., & Iverson, G. 2008. Bayesian versus frequentist inference. In H. Hoijtink, I. Klugkist, & P. A. Boelen (Eds.), Bayesian evaluation of informative hypotheses: 181-207. New York: Springer.
87. Zabell, S. 1989. R. A. Fisher on the history of inverse probability. Statistical Science, 4: 247-263.
88. Zabell, S. 2011. Carnap and the logic of induction. In D. M. Gabbay, S. Hartmann, & J. Woods (Eds.), Handbook of the history of logic: Inductive logic, vol. 10: 265-309. Oxford, UK: Elsevier.
89. Zyphur, M. J., & Oswald, F. L. 2012. Bayesian estimation and inference: A user’s guide.Unpublished manuscript