Bibliography Abramowitz, M. and Stegun, I. A. (1965). Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables. Dover, New York. Barlow, D., Nock, M., and Hersen, M. (2009). Single Case Experimental Designs. Strategies for Studying Behavior for Change. Boston, MA: Pearson, 3rd edition. Beeson, P. M. and Robey, R. R. (2006). Evaluating single-subject treatment research: Lessons learned from the aphasia literature. Neuropsychology Review, 16:161–169. Berger, J. O. and Berry, D. A. (1988). Statistical analysis and the illusion of objectivity. American Scientist, 76:159–165. Berger, J. O. and Sellke, T. (1987). Testing a point null hypothesis: The irreconcilability of p values and evidence. Journal of the American Statistical Association, 82(397):112–122. Bisconer, S. W., Green, M., Mallon-Czajka, J., and Johnson, J. S. (2006). Managing aggression in a psychiatric hospital using a behaviour plan: A case study. Journal of Psychiatric and Mental Health Nursing, 13:515–521. Bult´e, I. and Onghena, P. (2008). An R package for single-case randomization tests. Behavior Research Methods, 40:467–478. Bult´e, I. and Onghena, P. (2009). Randomization tests for multiple-baseline designs: an extension of the SCRT-R package. Behavior Research Methods, 41:477–485. Buonaccorsi, J. P. (2010). Measurement Error: Models, Methods, and Applications. Chapman & Hall/CRC interdisciplinary statistics series. Boca Raton, FL: CRC Press. Burgess, P., Pirkis, J., and Coombs, T. (2009). Modelling candidate effectiveness indicators for mental health services. Australian and New Zealand Journal of Psychiatry, 43:531–538. 157 BIBLIOGRAPHY Busk, P. L. and Marascuilo, L. A. (1988). Autocorrelation in single-subject research: a counterargument to the myth of no autocorrelation. Behavioral Assessment, 10:229–242. Carlin, B. P. and Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society. Series B (Methodological), 57(3):473–484. Casella, G. and Berger, R. L. (2002). Statistical Inference. Duxbury, Pacific Grove, CA. Chandrasekaran, S., Gu, M., Sun, X., Xia, J., and Zhu, J. (2007). A superfast algorithm for toeplitz systems of linear equations. SIAM Journal on Matrix ˘ S1266. Analysis and Applications, 29(4):1247ˆaA¸ Cleveland, W. S. (1981). Lowess: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician, 35:54. Cohen, J. (1992). A power primer. Psychological Bulletin, 112:155–159. Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49:997– 1003. de Beurs, E. (2010). De genormaliseerde t-score. een “euro” voor testuitslagen [the normalized t-score. a “euro” for test outcomes.]. Maandblad Geestelijke volksgezondheid, 65:684 – 695. de Vries, R. M., Hartogs, B. M. A., and Morey, R. D. (Submitted). A tutorial on computing bayes factors for single-subject designs. Behavior Therapy. de Vries, R. M. and Morey, R. D. (2013). Bayesian hypothesis testing for singlesubject designs. Psychological Methods, 18(2):165 – 185. de Vries, R. M., Morey, R. D., and Tendeiro, J. N. (In preparation). Bayesian hypothesis testing for routine outcome measurement data. Derogatis, L. R. (2001). Brief Symptom Inventory (BSI)-18. Administration, scoring and procedures manual. Minneapolis: NCS Pearson, Inc. Dickey, J. M. and Lientz, B. P. (1970). The weighted likelihood ratio, sharp hypotheses about chances, the order of a Markov chain. The Annals of Mathematical Statistics, 41(1):214–226. Edwards, W., Lindman, H., and Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70:193–242. Ferron, J. and Foster-Johnson, L. (1998). Analyzing single-case data with visually guided randomization tests. Behavior Research Methods, 30:698–706. Fox, J. (2008). Applied regression analysis and generalized linear models. Sage, Los Angeles, 2 edition. 158 BIBLIOGRAPHY Gelfand, A. and Smith, A. F. M. (1990). Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association, 85:398–409. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian data analysis (2nd edition). Chapman and Hall, London. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741. Glover, S. and Dixon, P. (2004). Likelihood ratios: a simple and flexible statistic for empirical psychologists. Psychonomic Bulletin & Review, 11:791–806. Good, I. J. (1985). Weight of evidence: A brief survey. In Bernardo, J. M., DeGroot, M. H., Lindley, D. V., and Smith, A. F. M., editors, Bayesian Statistics 2, pages 249–270, North-Holland. Elsevier Science Publishers B.V. Goodman, S. N. (1999a). Toward evidence-based medical statistics. 2: the Bayes factor. Annals of Internal Medicine, 130(12):1005–1013. Goodman, S. N. (1999b). Toward evidence-based medical statistics I. The p-value fallacy. Annals of internal medicine, 130:995–1004. Hacking, I. (1965). Logic of Statistical Inference. Cambridge University Press, Cambridge, England. Haller, H. and Krauss, S. (2002). Misinterpretations of significance: A problem students share with their teachers? Methods of Psychological Research Online, 7. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57:97–109. Henriques, G., Keffer, S., Abrahamson, C., and Horst, S. J. (2011). Exploring the effectiveness of a computer-based heart rate variability biofeedback program in reducing anxiety in college students. Applied Psychophysiol Biofeedback, 36:101–112. Jacobson, N. S. and Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59:12–19. Jaynes, E. (1986). Bayesian methods: General background. In Justice, J., editor, Maximum-Entropy and Bayesian Methods in Applied Statistics. Cambridge University Press, Cambridge. Jaynes, E. (2003). Probability theory: the logic of science. Cambridge University Press, Cambridge, UK. 159 BIBLIOGRAPHY Jeffreys, H. (1946). An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 186(1007):453–461. Jeffreys, H. (1961). Theory of Probability (3rd Edition). Oxford University Press, New York. Johnson, V. E. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, 110:19313–19317. Jones, W. P. (2003). Single-case time series with bayesian analysis: A practioner’s guide. Measurement and Evaluation in Counseling and Development, 36:28 – 39. Kinugasa, T., Cerin, E., and Hooper, S. (2004). Single-subject research designs and data analyses for assessing elite athletes’ conditioning. Sports Medicine, 34:1035–1050. Lambert, M. J., Whipple, J. L., Hawkins, E. J., Vermeersch, D. A., Nielsen, S. L., and Smart, D. W. (2003). Is it iime for clinicians to routinely track patient outcome? a meta-analysis. Clinical Psychology: Science and Practice, 10(3):288–301. Lee, P. M. (2004). Bayesian statistics: An introduction (3rd ed.). Wiley, New York. Liang, F., Paulo, R., Molina, G., Clyde, M. A., and Berger, J. O. (2008). Mixtures of g-priors for Bayesian variable selection. Journal of the American Statistical Association, 103:410–423. Lindley, D. V. (1957). A statistical paradox. Biometrika, 44:187–192. Loftus, G. R. (1996). Psychology will be a much better science when we change the way we analyze data. Current directions in psychological science, 5:161–171. Ma, H.-H. (2006). An alternative method for quantitative synthesis of singlesubject researchers. Behavior Modification, 30:598–617. Maggin, D. M., Swaminathan, H., Rogers, H. J., O’Keeffe, B. V., Sugai, G., and Horner, R. H. (2011). A generalized least squares regression approach for computing effect sizes in single-case research: application examples. Journal of School Psychology, 49:301–321. Manolov, R. and Solanas, A. (2009). Problems of the randomization test for ab designs. Psicol´ ogica, 30:137–154. Mastropieri, M. and Scruggs, T. (1985). Early intervention for socially withdrawn children. The Journal of Special Education, 19:429–441. 160 BIBLIOGRAPHY Matyas, T. A. and Greenwood, K. M. (1997). Serial dependency in single-case time series. In Franklin, R. D., Allison, D. B., and Gorman, B. S., editors, Design and analysis of single-case research. Erlbaum, Mahwah, NJ. McDowall, D., McCleary, R., Meidinger, E. E., and Hay, R. A. (1980). Interrupted time series analysis. Number 21 in Quantitative applications in the social sciences. Sage, Beverly Hills. Morey, R. D. and de Vries, R. M. (2014). BayesSingleSub 0.6.2. Comprehensive R Archive Network. Morey, R. D., Romeijn, J.-W., and Rouder, J. N. (2013). The humble Bayesian: model checking from a fully Bayesian perspective. British Journal of Mathematical and Statistical Psychology, 66:68–75. Morey, R. D. and Rouder, J. N. (2011). Bayes factor approaches for testing interval null hypotheses. Psychological Methods, 16:406–419. Morey, R. D., Rouder, J. N., Pratte, M. S., and Speckman, P. L. (2011). Using MCMC chain outputs to efficiently estimate Bayes factors. Journal of Mathematical Psychology, 55:368–378. Morey, R. D. and Wagenmakers, E.-J. (submitted). Simple relation between one-sided and two-sided Bayesian point-null hypothesis tests. Submitted. Morgan, D. L. and Morgan, R. K. (2009). Single-case research methods for the behavioral and health sciences. Sage, Los Angeles, CA [etc.]. Parker, R. I. and Brossart, D. F. (2003). Evaluating single-case research data: a comparison of seven statistical methods. Behavior Therapy, 34:189–211. Parker, R. I., Brossart, D. F., Vannest, K. J., Long, J. R., Garcia de Alba, R., Baugh, F. G., and Sullivan, J. R. (2005). Effect sizes in single case research: How large is large? School Psychology Review, 34(1):116 – 132. Parker, R. I. and Hagan-Burke, S. (2007). Median-based overlap analysis for single case data. Behavior Modification, 31:919–936. Parker, R. I., Hagan-Burke, S., and Vannest, K. (2007). Percentage of all nonoverlapping data (PAND): An alternative to PND. Journal of Special Education, 40:194–204. Parker, R. I. and Vannest, K. (2009). An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy, 40:357–367. Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing. 161 BIBLIOGRAPHY Pollard, P. and Richardson, J. (1987). On the probability of making Type I errors. Psychological Bulletin, 102:159–163. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, F. P. (1992). Numerical Recipes in C: The art of Scientific Computing. Cambridge University Press, Cambridge, England, second edition edition. R Development Core Team (2009). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Raftery, A. E. and Lewis, S. (1992). Comment: One long run with diagnostics: Implementation strategies for markov chain monte carlo. Statistical Science, 7:493–497. Rasquin, S. M. C., Van De Sande, P., Praamstra, A. J., and Van Heugten, C. M. (2009). Cognitive-behavioral intervention for depression after stroke: Five single case studies on effects and feasibility. Neuropsychological Rehabilitation, 19:208–222. Reichardt, C. S. and Gollob, H. F. (1997). When confidence intervals should be used instead of statistical tests, and vice versa. In Harlow, L. L., Mulaik, S. A., and Steiger, J. H., editors, What if there were no significance tests?, pages 259–284. Lawrence Erlbaum Associates, Mahwah, New Jersey, USA. Rindskopf, D. (2014). Nonlinear bayesian analysis for single case designs. Journal of School Psychology, 52(2):179 – 189. Roberts, G. O. and Sahu, S. K. (1997). Updating schemes, correlation structure, blocking and parameterization for the Gibbs sampler. Journal of the Royal Statistical Society, Series B, Methodological, 59:291–317. Ross, S. M. (2002). Simulation. Academic Press, London, 3rd edition edition. Rouder, J. N. and Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin and Review, 12:573–604. Rouder, J. N. and Morey, R. D. (2005). Relational and arelational confidence intervals: A comment on Fidler et al. (2004). Psychological Science, 16:77–79. Rouder, J. N., Morey, R. D., Verhagen, J., Province, J. M., and Wagenmakers, E.-J. (submitted). The p < .05 rule and the hidden costs of the free lunch in inference. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., and Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin and Review, 16:225–237. 162 BIBLIOGRAPHY Royall, R. (1997). Statistical Evidence: A Likelihood Paradigm. CRC Press, New York. Schmidt, F. L. and Hunter, J. E. (1997). Eight common but false objections to the discontinuation of significance testing in the analysis of research data. In Harlow, L. L., Mulaik, S. A., and Steiger, J. H., editors, What if there were no significance tests?, pages 37–64. Erlbaum, London. Sellke, T., Bayarri, M. J., and Berger, J. O. (2001). Calibration of p values for testing precise null hypotheses. American Statistician, 55:62–71. Shadish, W., Rindskopf, D., and Hedges, L. (2008). The state of the science in the meta-analysis of single-case experimental designs. Evidence-Based Communication Assessment and Intervention, 2:188–196. Shadish, W. and Sullivan, K. (2011). Characteristics of single-case designs used to assess intervention effects in 2008. Behavior Research Methods, 43(4):971 – 980. Sharpley, C. F. and Alavosius, M. P. (1988). Autocorrelation in behavioral data: an alternative perspective. Behavioral Assessment, 10:243–251. Simpson, S., Corney, R., Fitzgerald, P., and Beecham, J. (2003). A randomized controlled trial to evaluate the effectiveness and cost-effectiveness of psychodynamic counselling for general practice patients with chronic depression. Psychological Medicine, 33(2):229–239. Slade, M. (2002a). Routine outcome assessment in mental health services. Psychological Medicine, 32:1339–1343. Slade, M. (2002b). What outcomes to measure in routine mental health services, and how to assess them: a systematic review. Australian and New Zealand Journal of Psychiatry, 36:743–753. Solanas, A., Manolov, R., and Sierra, V. (2010). Lag-one autocorrelation in short series: estimation and hypotheses testing. Psicol´ ogica, 31:357–381. Stiles, W. B., Barkham, M., Twigg, E., Mellor-Clark, J., and Cooper, M. (2006). Effectiveness of cognitive-behavioural, person-centred and psychodynamic therapies as practised in uk national health service settings. Psychological Medicine, 36(4):555–566. Swaminathan, H., H.J., R., and Horner, R. (2014). An effect size measure and bayesian analysis. Journal of School Psychology, 52(2):213 – 230. Van Hees, S., Van der Vlist, P., and Mulder, N. (2011). Van Weten naar Meten: ROM in de ggz [From Knowledge to Measurement: ROM in the ggz]. Amsterdam: Uitgeverij Boom. 163 BIBLIOGRAPHY Van Os, J., Kahn, R., Denys, D., Schoevers, R. A., Beekman, A. T. F., Hoogendijk, W. J. G., Van Hemert, A. M., Hodiamont, P. P. G., Scheepers, F., Delespaul, P. A. E. G., and Leentjens, A. F. G. (2012). Rom: gedragsnorm of dwangmaatregel? Tijdschrift voor Psychiatrie, 54:245 – 253. Wagenmakers, E.-J. (2007). A practical solution to the pervasive problem of p values. Psychonomic Bulletin and Review, 14:779–804. Wagenmakers, E.-J., Lee, M. D., Lodewyckx, T., and Iverson, G. (2008). Bayesian versus frequentist inference. In Hoijtink, H., Klugkist, I., and Boelen, P., editors, Practical Bayesian Approaches to Testing Behavioral and Social Science Hypotheses, pages 181–207, New York. Springer. Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., and Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cognitive Psychology, 60:158–189. Wellek, S. (2003). Testing Statistical Hypotheses of Equivalence. Chapman & Hall/CRC, Boca Raton. Wheat, A. and Larkin, K. (2010). Biofeedback of heart rate variability and related physiology: A critical review. Applied Psychophysiology and Biofeedback, 35(3):229 – 242. Zellner, A. and Siow, A. (1980). Posterior odds ratios for selected regression hypotheses. In Bernardo, J. M., DeGroot, M. H., Lindley, D. V., and Smith, A. F. M., editors, Bayesian Statistics: Proceedings of the First International Meeting held in Valencia (Spain), pages 585–603. University of Valencia. Zucker, D., Ruthazer, R., and Schmid, C. (2010). Individual (n-of-1) trials can be combined to give population comparative treatment effect estimates: Methodologic considerations. Journal of Clinical Epidemiology, 63(12):1312 – 1323. 164