Things Statistical

This space will be used to post resources related to statistics in the future. 
Factor Analysis Materials
HARKing
  • Anderson, D. R., Burnham, K. P., Gould, W. R., & Cherry, S. (2001). Concerns about finding effects that are actually spurious. Wildlife Society Bulletin, 29, 311–316. (pdf)
  • Bosco, F. A., Aguinis, H., Field, J. G., Pierce, C. A., & Dalton, D. R. (in press). HARKing’s threat to organizational research: Evidence from primary and meta-analytic sources. Personnel Psychology. (pdf)
  • Gardner, M. R. (1982). Predicting novel facts. British Journal for the Philosophy of Science, 33, 1-15. (pdf)
  • Harker, D. (2008). On the predilections for predictions. British Journal for the Philosophy of Science, 59, 429-453. (pdf)
  • Hitchcock, C., & Sober, E. (2004). Prediction versus accommodation and the risk of overfitting. British Journal for the Philosophy of Science, 55, 1-34. (pdf
  • Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Bulletin, 2(3), 196-217. (pdf)
  • Kerr, N. L. (2011). HARK! A herald sings…but who’s listening? In R. M. Arkin (Ed.), Most Underappreciated: 50 Prominent Social Psychologists Talk About Hidden Gems(pp. 126-131). New York, NY: Oxford University Press. (pdf)
  • Leung, K. (2011). Presenting post hoc hypotheses as a priori: Ethical and theoretical issues. Management and Organization Review, 7, 471-479. (pdf
  • Lipton, P. (2001). Inference to the best explanation. In W. H. Newton-Smith (Ed.), A Companion to the Philosophy of Science (pp. 184-193). Walden, MA: Blackwell Publishers Ltd. (pdf
  • Lipton, P. (2005). Testing hypotheses: Prediction and prejudice. Science, 307, 219-221. (pdf)
  • Roese, N. J., & Vohs, K. D. (2012). Hindsight bias. Perspectives on Psychological Science, 7(5), 411-426.(pdf)
  • Simon, H. A. (1955). Prediction and hindsight as confirmatory science. Philosophy of Science, 22, 227-230. (pdf)
  • Wagenmakers, E.-J., Wetzels, R., Borsboom, D., & van der Maas, H. L. J. (2011). Why psychologists must change the way they analyze their data: The case of psi. Journal of Personality and Social Psychology, 100, 426–432. (pdf)
  • Wagenmakers, E. J., Wetzels, R., Borsboom, D., van der Maas, H. L., & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspectives on Psychological Science, 7(6), 632-638. (pdf
  • White, R. (2003). The epistemic advantage of prediction over accommodation. Mind, 112, 653-683. (pdf)
John Ioannidis's Work that I have Cited
  • Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafó, M. R. (2013).  Power failure:  Why small sample size undermines the reliability of neuroscience. Nature Neuroscience, 14,  365-376 (pdf) (errata pdf)
  • Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nozek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Empirical evidence for low reproducibility indicates low pre-study odds. Nature Neuroscience, 14, 877. (pdf)
  • Fanelli, D., &  Ioannidis J. P. A. (2013) U.S. studies may overestimate effect sizes in softer research. Proceedings of the National Academy of Sciences, 110, 15031–15036. (pdf)
  • Chavalarias, D., Wallach, J. D., Li, A. H. T., & Ioannidis J. P. A. (2016). Evolution of reporting p values in the biomedical literature, 1990-2015. Journal of the American Medical Association, 315(11), 1141-1148. (pdf)
  • Ioannidis, J. P. A. (1998).  
    Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. Journal of the American Medical Association, 279(4), 281–286. (pdf)
  • Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2, e124. (pdf)
  • Ioannidis, J. P. A. (2005). Concentration of the most-cited papers in the scientific literature: Analysis of journal ecosystems. PLoS One, 1.
     (pdf)
  • Ioannidis, J. P. A. (2005). Contradicted and initially stronger effects in highly cited clinical research.  Journal of the American Medical Association, 294, 218-228. (pdf)
  • Ioannidis, J. P. A., Trikalinos, T. A., & Zintzaras, E. (2006). Extreme between-study homogeneity in meta-analysis could offer useful insights. Journal of Clinical Epidemiology, 59(10), 1023-1032. (pdf)
  • Ioannidis, J. P. A., Patsopoulos, N. A., & Evangelou, E. (2007). Uncertainty in heterogeneity estimates in meta-analysis. British Medical Journal, 335(7626), 914-916. (pdf)
  • Ioannidis, J. P. A. (2008). Why most discovered true associations are inflated. Epidemiology, 19, 640-648. (pdf)
  • Ioannidis, J. P. A. (2011). More time for research:  Fund people, not projects. Nature, 477, 429-531 (pdf)
  • Ioannidis, J. P. A. (2011). Excess significance bias in the literature on brain volume abnormalities. Archives of General Psychiatry, 68, 773–780.  (pdf)
  • Ioannidis, J. P. A. (2012). Scientific communication is down at the moment, please check again later. Psychological Inquiry, 23(3), 267-270. (pdf)
  • Ioannidis, J. P. A. (2012). Why science is not necessarily self-correcting.  Perspectives on Psychological Science, 7(6), 645-654. (pdf)
  • Ioannidis, J. P. A. (2013). Scientific inbreeding and same-team replication: Type D personality as an example. Journal of Psychosomatic Research, 73(6), 408-410.
    (pdf)
  • Ioannidis, J. P. A. (2014). Estimates of the continuously publishing core in the scientific workforce. PLoS One, e101698. (pdf)
  • Ioannidis, J. P. A. (2014). How to make more published research true. PLoS Medicine, 11(10), e1001747. doi: 10.1371/journal.pmed.1001747. (pdf)
  • Ioannidis, J. P. A. (2016) Why most clinical research is not useful. PLoS Medicine, 13(6): e1002049. doi:10.1371/journal.pmed.1002049 (pdf)
  • Ioannidis, J. P. A., Cappelleri, J. C., & Lau, J. (1998) Issues in comparisons between meta-analyses and large trials. Journal of the American Medical Association, 279, 1089–93. (pdf)
  • Ioannidis, J. P. A.,
    Fanelli, D., Dunne, D. D., & Goodman, S. N. (2015). Meta-research: Evaluation and Improvement of research methods and practices. PLoS Biology, 13(10),  e1002264. (pdf)
  • Ioannidis, J. P. A., & Garber, A. M. (2012). Individualized cost effectiveness analysis. 
    PLoS Medicine, 8(7),  e1001058. (pdf)
  • Ioannidis, J. P. A., Greenland, S., Hlatky, M. A.,. Khoury, M. J., Macleod, M. R., Moher, D., Schultz, K. F., & Tibshirani, R. (2014). Increasing value and reducing waste in research design, conduct, and analysis. The Lancet, 383, 166-175. (pdf)
  • Ioannidis, J. P. A., & Khoury, M. J. (2011). Improving validation practices in “omics” research. Science, 334, 1230–1232. (pdf)
  • Ioannidis, J. P. A., & Lau, J. (2001).  Evolution of treatment effects over time: empirical insight from recursive cumulative meta-analyses. Proceedings of the National Academy of Science of the United States of America, 98(3), 831–6. (pdf)
  • Ioannidis, J. P. A., & Panagiotou O. A. (2011). Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. Journal of the American Medical Association, 305, 2200–2210. (pdf)
  • Ioannidis, J. P. A., Tarone, R., & McLaughlin, J. K. (2011). The false-positive to false-negative ratio in epidemiologic studies. Epidemiology, 22(4), 450-456. (pdf)
  • Ioannidis, J. P. A., & Trikalinos, T. A. (2005). Early extreme contradictory estimates may appear in published research: The Proteus phenomenon in molecular genetics research and randomized trials. Journal of Clinical Epidemiology, 58, 543-549. (pdf)
  • Ioannidis, J. P. A., & Trikalinos, T. A. (2007). An exploratory test for an excess of significant findings. Clinical Trials, 4, 245–253. (pdf)
  • Ioannidis, J. P. A., & Trikalinos, T. A. (2007).  The appropriateness of asymmetry tests for publication bias in meta-analyses: A large survey. Canadian Medical Association Journal, 176, 1091-1096. (pdf)
  • Nicholson, J. M., & Ioannidis, J. P. A. (2012). Research grants: Conform and be funded. Nature, 492, 34-36. (pdf).
  • Smit, Y. Huibers, M. J. H., Ioannidis, J. P. A., van Dyck, R., van Tilburg, W., & Arntz, A. (2012). The effectiveness of long-term psychoanalytic psychotherapy - A meta-analysis of controlled trials. Clinical Psychology Review, 32(2), 81-92. (pdf)
  • Tatsioni, A, Bonitsis, N. G., & Ioannidis, J. P. A. (2005). Persistence of contradicted claims in the literature. Journal of the American Medical Association, 298(21), 2517-2526. (pdf)
  • Young, N. S., Ioannidis, J. P. A., & Al-Ubaydli, O. (2008). Why current publication practices may distort science. PLoS Medicine, 5, 1418-1422. (pdf)
Latent Variable Interactions and Nonlinear Effects
  • Bauer, D.J., Baldasaro, R. & Gottfredson, N.C. (2012). Diagnostic procedures for detecting nonlinear relationships between latent variablesStructural Equation Modeling: A Multidisciplinary Journal, 19, 157-177. (pdf)
  • Kelava, A., Moosbrugger, H., Dimitruk, P., & Schermelleh-Engel, K. (2008). Multicollinearity and MSEM for moderation missing constraints: A comparison of three approaches for the analysis of latent nonlinear effects. Methodology, 4, 51-66. (pdf)
  • Kelava, A., Werner, C., Schermelleh-Engel, K., Moosbrugger, H., Zapf, D., Ma, Y., Cham, H., Aiken, L. S., & West, S. G. (2011). Advanced nonlinear structural equation modeling: Theoretical properties and empirical application of the LMS and QML estimators. Structural Equation Modeling, 18, 465-491.  (pdf)
  • Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201-210. (pdf)
  • Klein, A. G., & Muthén, B. (2007). Quasi-maximum likelihood of structural equation modeling with multiple interaction and quadratic effects. Multivariate Behavioral Research, 42, 647- 673. (pdf)
  • Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions. Structural Equation Modeling, 13, 497-519. (pdf)
  • Marsh, H. W., Wen, Z., & Hau, K.-T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275-300. (pdf)
  • Moosbrugger, H., Schermelleh-Engel, K., Kelava, A., & Klein, A. G. (2009). Testing multiple nonlinear effects in structural equation modeling: A comparison of alternative estimation approaches. In T. Teo & M. S. Khine (Eds.), Structural equation modeling in educational research: Concepts and applications (pp. 103-135). Rotterdam: Sense Publishers.  (pdf)
  • Pek, J., Sterba, S.K., Kok, B.E. & Bauer, D.J. (2009). Estimating and visualizing nonlinear relations among latent variables: A semiparametric approachMultivariate Behavioral Research, 44, 407-436. (pdf)
  • Steinmetz, H., Davidov, E., & Schmidt, P. (2011). 
    Three approaches to estimate latent interaction effects: Intention and perceived behavioral control in the theory of planned behavior. Methodological Innovations Online, 6(1), 95-110. (pdf)
Longitudinal Analysis with Changing Scales
  • Edwards, M.C., & Wirth, R.J. (2009). Measurement and the study of changeResearch in Human Development, 6, 74-96 . (pdf)
  • McArdle, J. J., Grimm, K. J., Hamagami, F., Bowles, R. P., & Meredith, W. (2009). Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement. Psychological Methods, 14(2), 126-149. (pdf)
  • Pettit,  G. S., Keiley, M. K., Laird, R. D., Bates, R. D., & Dodge, K. A. (2007) Predicting the developmental course of mother-reported monitoring across childhood and adolescence from early practive parenting.  Journal of Family Psychology, 21(2), 206-217. (pdf) (Mplus slides)
Neuroscience Statistics
  • Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V., & van der Sluis, S. (2014). A solution to dependency:  Using multilevel analysis to accommodate nested data. Nature Neuroscience, 17(4), 491-496. (pdf)
  • Bennett, C. A., Baird, A. A., Miller, M. B., & Wolford, G. L. (2009). Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction.  Poster. (pdf)
  • Bennett, C. A., Baird, A. A., Miller, M. B., & Wolford, G. L. (2009). Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for proper multiple comparisons correction. Journal of Serendipitous and Unexpected Results, 1(1), 1-5. (pdf)
  • Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafó, M. R. (2013).  Power failure:  Why small sample size undermines the reliability of neuroscience. Nature Neuroscience, 14,  365-376 (pdf) (errata pdf)
  • Kim, J., Zhu, W., Chang, L., Bentler, P., & Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28, 85-93. (pdf)
  • Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E.-J. (2011).  Erroneous analysis of interactions in neuroscience: A problem of significance. Nature Neuroscience, 14(9), 1105-1107. (pdf)
  • Voodoo Correlations
    • Diener, E. (2009). Editor's introduction to Vul et al. (2009) and comments.  Perspectives on Psychological Science, 4(3), 272-273. (pdf)
    • Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzingly high correlations in fMRI studies of emotion, personality, and social cognition.  Perspectives on Psychological Science, 4(3), 274-290. (pdf
    • Nichols, T. E.. & Poline, J.-B. (2009). Commentary on Vul et al.'s (2009) "Puzzingly high correlations in fMRI studies of emotion, personality, and social cognition." Perspectives on Psychological Science, 4(3), 291-293. (pdf)
    • Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power -- Commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294-298. (pdf)
    • Lieberman, M. T., Berkman, E. T., & Wager, T. D. (2009). Correlations in social neuroscience aren't voodoo: Commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 299-307. (pdf)
    • Lazar, N. A. (2009) Discussion of "Puzzingly high correlations in fMRI studies of emotion, personality, and social cognition" by Vul et al. (2009). Perspectives on Psychological Science, 4(3), 308-309. (pdf)
    • Lindquist, M. A., & Gelman, A. (2009). Correlations and multiple comparisons in functional imagining: A statistical perspective (Commentary on Vul et al., 2009). Perspectives on Psychological Science, 4(3), 310-313. (pdf)
    • Barrett, L. F. (2009) Understanding the mind by measuring the braing: Lessons from measuring behavior (Commentary on Vul et al., 2009). Perspectives on Psychological Science, 4(3), 314-318. (pdf)
    • Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Reply to comments on "Puzzingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on Psychological Science, 4(3), 319-324. (pdf)

  • Ricean Distribution Materials
    • Gudbjartsson, H., & Patz, S. (1995). The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine, 34(6), 910-914. (pdf)
    • Lauwers, L., Barbé, K., Van Moer, W., & Pintelon, R. (2009, May 5-7)   Estimating the parameters of a Rice distribution: A Bayesian approachProceedings of the International Instrumentation and Measurement Technology Conference. (pp. 114-117). Singapore. (pdf)
    • Rice, S. O. (1944). Mathematical analysis of random noise. Bell System Technical Journal, 23(3), 282-332. (pdf, scan)
    • Sijbers, J., den Dekker, A. J., Scheunders, P., & Van Dyck, D. (1998). Maximum likelihood estimation of Rician distribution parameters. IEEE Transactions on Medical Imaging, 17(3), 357-361. (pdf)
    • Sijbers, J., den Dekker, A. J., Van Dyck, D., & Raman, E. (1998, February 11-14).  Estimation of signal and noise from Rician distributed data. Proceedings of of the International Conference on Signal Processing and Communications (pp. 140-142). Gran Canaria, Canary Islands, Spain. (pdf)
Pilot Studies
    • Arain, M., Campbell, M. J., Cooper, C. L., & Lancaster, G. A. (2010). What is a pilot or feasibility study? A review of current practice or editorial policy. BMC Medical Research Methodology, 10, 67. (pdf)
    • Arnold, D. M., Burns, K. E. A., Adhikari, N. K. J., Kho, M. E., Meade, M. O., & Cook, D. J. (2009). The design and interpretation of pilot trials in clinical research and critical care. Journal of Evaluation in Clinical Practice, 37, 1, S69-S74. (pdf)
    • Grimes, D. A., & Schultz, K. F. (2002). Descriptive studies: What they can and cannot do. The Lancet, 359(9301), 145-149. (pdf).
    • Kraemer, H.C., Mintz, J., Noda, A., Tinklenberg, J., & Yesavge, J.A. (2006). Caution regarding the use of pilot studies to guide power calculations for study proposals. Archives of General Psychiatry, 63, 484–489. (pdf)
    • Lancaster, G. A., Dodd, S., & Williamson, P. R. (2004)..  Design and analysis of pilot studies: recommendations for good practice.Journal of Evaluation in Clinical Practice, 10(2), 307-312. (pdf)
    • Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research.  Journal of Psychiatric Research, 45(5), 626-629. (pdf)
    • Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L. & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how.  BMC Medical Research Methodology, 10, 1. (pdf)
Replication Crisis (non-comprehensive, updated periodically):
  • Hanson, R. C. (1958). Evidence and procedure characteristics of "reliable" propositions in social science. American Journal of Sociology, 63(4), 357-370. (pdf)
  • Kahneman, D. (2014). A new etiquette for replication. Social Psychology, 45(4), 310-311.  (pdf)
  • Nosek,, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., 
  • Buck, S., 
  • Chambers, C. D., 
  • Chin, G., 
  • Christensen, G., 
  • Contestabile, M., 
  • Dafoe, A., 
  • Eich, E., 
  • Freese, J., 
  • Glennerster, R., 
  • Goroff, D., 
  • Green, D. P., 
  • Hesse, B., 
  • Humphreys, M., 
  • Ishiyama, J., 
  • Karlan, D., 
  • Kraut, A., L
  • upia, A., 
  • Mabry, P., 
  • Madon, T. A., 
  • Malhotra, N., 
  • Mayo-Wilson, E., 
  • McNutt, M., 
  • Miguel, E., 
  • Levy Paluck, E., 
  • Simonsohn, U.,
  • Soderberg, C., 
  • Spellman, B. A.,
  • Turitto, J.,
  • VandenBos, G., 
  • Vazire, S.,
  • Wagenmakers, E. J., W
  • ilson, R., &
  • Yarkoni, T. (2015, June 26). Scientific standards: Promoting an open science research culture. Science, 348(6242), 
  • 1422-1425. (pdf) (Supplementary material)

Scientific Utopia
  • Psychological Inquiry, 2012
    • Nosek, B. A., & Bar-Anan, Y. (2012), Scientific utopia: I. Opening scientific communication. Psychological Inquiry, 23(3), 217-243. doi: 10.1080/1047840X.2012.692215 (pdf)
    • Adolph, K. E., Gilmore, R. O., Freeman, C., Sanderson, P., & Millman, D. (2012). Toward open behavioral science. Psychological Inquiry, 23(3), 244-247. doi: 10.1080/1047840X.2012.705133. (pdf)
    • Assendorpf, J. B. (2012). Does open scientific communication increase the quality of knowledge? Psychological Inquiry, 23(3), 248-250. doi: 10.1080/1047840X.2012.700578. (pdf)
    • Cirasella, J. (2012). A librarian's defense of the practical over the perfect in scholarly communication. Psychological Inquiry, 23(3), 251-252. doi: 10.1080/1047840X.2012.706203. (pdf)
    • Cooper, J. (2012). Missteps on the road to a scientific utopia. Psychological Inquiry, 23(3), 253-255. doi: 10.1080/1047840X.2012.704802. (pdf)
    • Crocker, J. (2012). Improving science by improving scientific communication: The view from the APA publication and communication board.  Psychological Inquiry, 23(3), 256-257. doi: 10.1080/1047840X.2012.702371. (pdf)
    • Dumming, D. (2012). What do we really want?   Psychological Inquiry, 23(3), 258-260. doi: 10.1080/1047840X.2012.704803. (pdf)
    • Fendley, P. (2012). Seeking the road to utopia. Psychological Inquiry, 23(3), 261-262. doi: 10.1080/1047840X.2012.705130. (pdf)
    • Giner-Sorolla, R. (2012). Will we march to utopia, or be dragged there? Past failures and hopes for publishing our science. Psychological Inquiry, 23(3), 263-266. doi: 10.1080/1047840X.2012.706506. (pdf)
    • Ioannidis, J. P. (2012). Scientific communication is down at the moment, please check again later. Psychological Inquiry, 23(3), 267-270. doi: 10.1080/1047840X.2012.699427. (pdf)
    • Iyer, R., & Graham, J. (2012). Leveraging the wisdom of crowds in a data-rich utopia.  Psychological Inquiry, 23(3), 271-273. doi: 10.1080/1047840X.2012.705244. (pdf)
    • King, L. A. (2012). A dinosaur comments on the coming apocalypse: Does anybody else see that asteroid?  Psychological Inquiry, 23(3), 274-276. doi: 10.1080/1047840X.2012.704804. (pdf)
    • Lilienfeld, C. O. (2012). Scientific utopia or scientific dystopia? Psychological Inquiry, 23(3), 277-280. doi: 10.1080/1047840X.2012.704807. (pdf)
    • Mooneyham, B. W., Franklin, M. S., Mrazek, M. D., & Schooler, J. W. (2012). Moderinzing science: Comments on Nosek and Bar-Anan (2012). Psychological Inquiry, 23(3), 281-284. doi: 10.1080/1047840X.2012.705246. (pdf)
    • Moore, D. A., & Tenney, E. R. (2012). Cheaper and better: Why scientific advancement demands the more to open access publishing. Psychological Inquiry, 23(3), 285-286. doi: 10.1080/1047840X.2012.705247. (pdf)
    • Mudditt, A., & Hogg, M. A. (2012). Scientific utopia: That which cannot exist? Psychological Inquiry, 23(3), 287-290. doi: 10.1080/1047840X.2012.704855. (pdf)
    • Nelson, L. D., Simmons, J. P., & Simonsohn, U. (2012). Let's publish fewer papers. Psychological Inquiry, 23(3), 291-293. doi: 10.1080/1047840X.2012.705245. (pdf)
    • Petty, R. E. (2012). Let's try and fix the current publishing system before making dramatic changes. Psychological Inquiry, 23(3), 294-297. doi: 10.1080/1047840X.2012.705132. (pdf)
    • Reis, H. T. (2012). The future of scientific publication in psychology: Utopias and dystopias. Psychological Inquiry, 23(3), 298-300. doi: 10.1080/1047840X.2012.704854. (pdf)
    • Saxe, R. (2012). How should  we manage peer review and why? Psychological Inquiry, 23(3), 301-302. doi: 10.1080/1047840X.2012.707635. (pdf)
    • Spellman, B. A. (2012). Scientific utopia ... or too much information? Comment on Nosek and Bar-Anan. Psychological Inquiry, 23(3), 303-304. doi: 10.1080/1047840X.2012.701161. (pdf)
    • Yarkoni, T. (2012). Beginning at Nosek and Bar-Anan's end: Let's put open evaluation first. Psychological Inquiry, 23(3), 305-307. doi: 10.1080/1047840X.2012.706204. (pdf)
    • Nosek, B. A., & Bar-Anan, Y. (2012). Scientific communication is changing and scientists should lead the way. Psychological Inquiry, 23(3), 308-314. doi: 10.1080/1047840X.2012.717907.(pdf)
  • Other Readings
    • Nosek, B. A., Spies, J. R., & Motyl, M. (2012). Scientific utopia: II. Restructuring incentives and practices to promote truth over publishability. Perspectives on Psychological Science, 7(6), 615-631. doi: 10.1177/1745691612459058. (pdf)
    • Miguel, E., Camerer, C., Casey, K., Cohen, J., Esterling, K. M., Gerber, A., Glennerster, R., Green, D. P., Humphreys, M., Imbens, G., Laitin, D., Madon, T., Nelson, L., Nosek, B. A., Peterson, N., Sedlmayer, R., Simmons, J. P., Simonsohn, U., & van der Laan, M. (2014). Promoting transparency in social science research. Science, 343(6166). 30-31.(pdf)
  • Links
Other Readings

Articles I've Told At Least Ten People to Read
  • De Groot, A. D. (1956/2014). The meaning of "significance" for different types of research. Translated and annotated by Eric-Jan Wagenmakers, Denny Borsboom, Josine Verhagen, Roger Kievit, Marjan Bakker, Angelique Cramer, Dora Matzke, Don Mellenbergh, and Han L. J. van der Maas. Acta Psychologica, 148, 188-194. (pdf)
  • Funder, D. C, Levine, J. M., Mackie, D. M., Morf, C. C, Vazire, S., & West, S. G. (2014). Improving the dependability of research in personality and social psychology: Recommendations for research and educational practice. Personality and Social Psychology Review, 18, 3 - 12  (pdf)
  • Gelman, A., & Stern, H. (2006). The difference between "significant" and "not significant" is not itself statistically significant. The American Statistician, 60(4), 328-331. (pdf)
  • Ioannidis, J.P.A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. (pdf)
  • Lykken, D. T. (1968)Statistical significance in psychological research, 70(3, part 1), 151-159. (pdf)
  • MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19-40. (pdf)
  • Murayama, K., Pekrun, R., & Fiedler, K. (2014). Research practices that can prevent an inflation of false-positive rates. Personality and Social Psychology Review, 18(2), 107-118(pdf)
  • Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44(10), 1276-1284. (pdf)
  • Wagenmakers, E.-J., Wetzels, R., Borsboom, DD., van der Maas, H. L. J., & Kievit, R. A. (2012).An agenda for purely confirmatory research. Perspectives on Psychological Science, 7(6), 632-638. (pdf)
Multilevel Structural Equation Modeling References (a page with a lot of references)

Network Analysis References
    • Denny Borsboom
      • Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64, 1089-1108. (pdf)
      • Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121. (pdf)
      • Borsboom, D., Cramer, A. O. J., Schmittman, V. D., Epskamp, S., & Waldrop, L. K. (2011).  The small world of psychopathology. PLOS ONE, 6(11), e27407. (pdf)
      • Bringham, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLOS ONE, 8(4), e60188 (pdf)
      • Cramer, A. O. J., .Borsboom, D., Aggen, S. H., & Kendler, K. S. (2012). The pathoplasticity of dysphoric episodes.  Differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychological Medicine, 42(5), 957-965. (pdf)
      1. Cramer, A. O. J., Waldorp, L. J., van der Maas, H., & Borsboom, D. (2010). Comorbidity: A network perspectiveBehavioral and Brain Sciences, 33, 137-193.(pdf)
      • Costantini, G., Epskamp, S., Borsboom, D., Perugini, M, Mõttus, R., Waldorp, L. J., & Cramer, A. O. J. (in press). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality. doi: http://dx.doi.org/10.1016/j.jrp.2014.07.003. (pdf)
      • Epskamp, S., Cramer, A.O.J., Waldorp, L.J., Schmittmann, V.D. and Borsboom, D. (2012) qgraph: Network Visualizations of Relationshipsin Psychometric Data.Journal of Statistical Software, 48(4), 1-18. (pdf)
      • Frewen, P. A., Scmittman, V. D., Bringmann, L. F., & Borsboom, D. (2013). Perceived causal relations between anxiety, posttraumatic stress, and depression: Extension to moderation, mediation, and network analysis. European Journal of Psychotraumatology, 4, 20656. (pdf)
      • Schmittman, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., and Borsboom, D. (2011). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43-53. (pdf)
      • van Borkulo, C. D, Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014).A new method for constructing networks from binary data. Scientific Reports, 4, 5918, DOI: 10.1038/srep05918 (pdf)
      • The Psychosystems Project link
    • Tom Snijders
    • Stanley Wasserman
    • Software Packages
    • More to come...
Not exactly statistical per se, but...  Paul Meehl related articles
    • Meehl's James McKeen Cattell Fellow Award from the American Psychological Society (now Association for Psychological Science) delivered May 23, 1998, Washington, D.C., entitled "The Power of Quantitative Thinking" (pdf)
    • "In appreciation of" link
    • Applied and Preventive Psychology, Volume 11, Issue 1 (2004) [in honor of Meehl]
      • Paul Meehl’s search for the optimal epistemology for the behavioral and social sciences (pdf)
      • Paul Meehl and the evolution of statistical methods in psychology (pdf)
      • The myth of open concepts: Meehl’s analysis of construct meaning versus black box essentialism (pdf)
      • Sir Karl, Sir Ronald, (Sir) Paul, and the human element in the progress of soft psychology (pdf)
      • Comment on “Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology” (pdf)
      • Falsification and the protective belt surrounding entity-postulating theories (pdf)
      • Taking theoretical risks in a world of directional predictions (pdf)
      • Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology (pdf)
      • Another quasi-30 years of slow progress (pdf)
      • The philosophical legacy of Meehl (1978): confirmation theory, theory quality, and scientific epistemology (pdf)
      • Paul Everett Meehl (pdf)
      • A few dissents from a magnificent piece of work (pdf)
      • Tabular asterisks, Paul Meehl, and looking at the data (pdf)
      • Commentary on Meehl (pdf)
      • Constructs, operational definition, and operational analysis (pdf)
      • Commentary on Meehl’s theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology (pdf)
      • The fallacy of the null hypothesis in soft psychology (pdf)
      • Statistical significance testing, construct validity, and clinical versus actuarial judgment: an interesting (seeming) paradox (pdf)
    • Psychological Methods, Volume 7, Issue 3 (2002)
      • The Path Analysis Controversy: A new statistical approach to strong appraisal of verisimilitude (pdf)
      • Comments on the Meehl-Waller (2002) procedure for appraisal of path analysis models (pdf)
      • The priority of just-identified, recursive models (pdf)
      • Commentary on Meehl and Waller's (2002) Path Analysis and Verisimilitude (pdf)
      • Risky tests, verisimilitude, and path analysis (pdf)
    • Psychological Inquiry, Volume 1, Issue 2 (1990)
      • Editor's Note (pdf)
      • Appraising and Amending Theories: The Strategy of Lakatosian Defense and Two Principles that Warrant It (pdf)
      • The Meehilan Corroboration-Verisimilitude Theory of Science (pdf)
      • In Defense of Popperian Falsification (pdf)
      • Theory Corroboration and Football: Measuring Progress (pdf)
      • Judging Results and Theories (pdf)
      • View of a Supportive Empiricist (pdf)
      • A Trivial Disagreement? (pdf)
      • The Compleat Falsifier (pdf)
      • Clinical Versus Statistical Theory Appraisal (pdf)
      • Thoughts on Meehl's Vision of Psychological Research for the Future (pdf)
      • Can Theory Appraisal Be Quantified? (pdf)
      • The Limits of Knowledge: Bayesian Pragmatism Versus a Lakatosian Defense (pdf)
      • Meehl on Theory Appraisal (pdf)
      • Author's Response (pdf)
    • Meehl's Cliometric Metatheory Book (that was never published) in three pieces
      • Meehl, P. E. (1992) Cliometric metatheory: The actuarial approach to empirical, history-based philosophy of science. Psychological Reports, 71, 339-467. (pdf)
      • Meehl, P. E. (2002) Cliometric metatheory II: Criteria scientists use in theory appraisal and why it is rational to do so. Psychological Reports, 91, 339-404. (pdf)
      • Meehl,P. E. (2004) Cliometric metatheory III: Peircean consensus, verisimilitude, and asymptotic method. British Journal for the Philosophy of Science, 55, 615-643 (pdf)
    • Taxometrics
      • The Original Psychiatry Reports
        • Meehl, P. E. (1965). Detecting latent clinical taxa by fallible quantitative indicators lacking an accepted criterion (Report No. PR-65-2). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Meehl, P. E. (1968). Detecting latent clinical taxa, II: A simplified procedure, some additional hitmax cut locators, a single-indicator method, and miscellaneous theorems (Report No. PR-68-4). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry (link)
        • Meehl, P. E., Lykken, D. T., Burdick, M. R., & Schoener, G. R. (1969). Identifying latent clinical taxa, III. An empirical trial of the normal single-indicator method, using MMPI Scale 5 to identify the sexes. (Report No. PR-69-1). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Golden, R., & Meehl, P. E. (1973). Detecting latent clinical taxa, IV: Empirical study of the maximum covariance method and the normal minimum chi-square method, using three MMPI keys to identify the sexes (Report No. PR-73-2). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Golden, R., & Meehl, P. E. (1973). Detecting latent clinical taxa, V: A Monte Carlo study of the maximum covariance method and associated consistency tests (Report No. PR-73-3). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Golden, R., Tyan, S., & Meehl, P. E. (1974). Detecting latent clinical taxa, VI: Analytical development and empirical trials of the consistency hurdles theory (Report No. PR-74-4). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Golden, R., Tyan, S., & Meehl, P. E. (1974). Detecting latent clinical taxa, VII: Maximum likelihood solution and empirical and artificial data trials of the multi-indicator multi-taxonomic class normal theory (Report No. PR-74-5). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Golden, R., & Meehl, P. E. (1974). Detecting latent clinical taxa, VIII: A preliminary study in the detection of the schizoid taxon using MMPI items as indicators (Report No. PR-74-6). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
        • Golden, R., Tyan, S., & Meehl, P. E. (1974). Detecting latent clinical taxa, IX: A Monte Carlo method for testing taxometric theories (Report No. PR-74-7). Minneapolis: University of Minnesota, Research Laboratories of the Department of Psychiatry. (link)
      • Will Grove's Taxometrics Page
      • Paul Meehl's Taxometrics Page
      • John Ruscio's Taxometrics Page
      • Taxometrics Resources (new page)

Selected Support Letters/Biosketches

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