References
Agresti, A. (1996). An introduction to categorical data analysis. Wiley.
Agresti, A. (2002). Categorical data analysis (2nd ed.). Wiley.
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for Educational and Psychological Testing. American Educational Research Association.
Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27, 17–21.
Bickel, P. J., Hammel, E. A., & O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–404.
Boncz, I. (2015). Research methodology basics [Kutatásmódszertani alapismeretek]. Pécsi Tudományegyetem.
Box, J. F. (1987). Guinness, gosset, fisher, and small samples. Statistical Science, 2, 45–52.
Brodey, B. B., First, M., Linthicum, J., Haman, K., Sasiela, J. W., & Ayer, D. (2016). Validation of the NetSCID: An automated web-based adaptive version of the SCID. Comprehensive Psychiatry, 66, 67–70. https://doi.org/10.1016/j.comppsych.2015.10.005
Brown, M. B., & Forsythe, A. B. (1974). Robust tests for equality of variances. Journal of the American Statistical Association, 69, 364–367.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
Cowan, N. (2015). George Miller’s magical number of immediate memory in retrospect: Observations on the faltering progression of science. Psychological Review, 122(3), 536–541. https://doi.org/10.1037/a0039035
Cramér, H. (1946). Mathematical methods of statistics. Princeton University Press.
Edwards, M. C., Slagle, A., Rubright, J. D., & Wirth, R. J. (2018). Fit for purpose and modern validity theory in clinical outcomes assessment. Quality of Life Research, 27(7), 1711–1720. https://doi.org/10.1007/s11136-017-1644-z
Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press.
Evans, J. St. B. T., Barston, J. L., & Pollard, P. (1983). On the conflict between logic and belief in syllogistic reasoning. Memory and Cognition, 11, 295–306.
Fisher, R. A. (1922a). On the interpretation of from contingency tables, and the calculation of . Journal of the Royal Statistical Society, 84, 87–94.
Fisher, R. A. (1922b). On the mathematical foundation of theoretical statistics. Philosophical Transactions of the Royal Society A, 222, 309–368.
Fisher, R. A. (1925). Statistical methods for research workers. Oliver; Boyd.
Fox, J., & Weisberg, S. (2011). An R companion to applied regression (2nd ed.). Sage.
Garcia-Romeu, A., Barrett, F. S., Carbonaro, T. M., Johnson, M. W., & Griffiths, R. R. (2021). Optimal dosing for psilocybin pharmacotherapy: Considering weight-adjusted and fixed dosing approaches. Journal of Psychopharmacology, 35(4), 353–361. https://doi.org/10.1177/0269881121991822
Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60, 328–331.
Hays, W. L. (1994). Statistics (5th ed.). Harcourt Brace.
Hogg, R. V., McKean, J. V., & Craig, A. T. (2005). Introduction to mathematical statistics (6th ed.). Pearson.
Howitt, D., & Cramer, D. (2020). Understanding statistics in psychology with SPSS (Eighth edition). Pearson.
Hsu, J. C. (1996). Multiple comparisons: Theory and methods. Chapman; Hall.
Hubley, A. M. (2014). Divergent Validity. In A. C. Michalos (Ed.), Encyclopedia of Quality of Life and Well-Being Research (pp. 1675–1676). Springer Netherlands. https://doi.org/10.1007/978-94-007-0753-5_766
Jeffreys, H. (1961). The theory of probability (3rd ed.). Oxford.
Johnson, V. E. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, 48, 19313–19317.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.
Keynes, J. M. (1923). A tract on monetary reform. Macmillan; Company.
Krajcsi, A. (2021). Advancing best practices in data analysis with automatic and optimized output data analysis software [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/hnmsq
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583–621.
Larntz, K. (1978). Small-sample comparisons of exact levels for chi-squared goodness-of-fit statistics. Journal of the American Statistical Association, 73, 253–263.
Lehmann, E. L. (2011). Fisher, Neyman, and the creation of classical statistics. Springer.
Levene, H. (1960). Robust tests for equality of variances. In I. O. et al (Ed.), Contributions to probability and statistics: Essays in honor of harold hotelling (pp. 278–292). Stanford University Press.
Lyon, J. D., & Tsai, C.-L. (1996). A comparison of tests for heteroscedasticity. Journal of the Royal Statistical Society: Series D (The Statistician), 45(3), 337–349.
Marks, D., & Yardley, L. (Eds.). (2004). Research methods for clinical and health psychology. SAGE.
McGrath, R. E., & Meyer, G. J. (2006). When effect sizes disagree: The case of and . Psychological Methods, 11, 386–401.
McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12, 153–157.
Meehl, P. H. (1967). Theory testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103–115.
Merriam-Webster. (2022). Petrichor, cromulent, and other words the internet loves. https://www.merriam-webster.com/words-at-play/internets-favorite-words
Michalos, A. C. (Ed.). (2014). Encyclopedia of quality of life and well-being research. Springer.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50, 157–175.
Sahai, H., & Ageel, M. I. (2000). The analysis of variance: Fixed, random and mixed models. Birkhauser.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591–611.
Sokal, R. R., & Rohlf, F. J. (1994). Biometry: The principles and practice of statistics in biological research (3rd ed.). Freeman.
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677–680.
Stigler, S. M. (1986). The history of statistics. Harvard University Press.
Student, A. (1908). The probable error of a mean. Biometrika, 6, 1–2.
Taylor, S. E., & Stanton, A. L. (2021). Health Psychology (Eleventh). McGraw-Hill.
Welch, B. L. (1947). The generalization of “Student’s” problem when several different population variances are involved. Biometrika, 34, 28–35.
Yates, F. (1934). Contingency tables involving small numbers and the test. Supplement to the Journal of the Royal Statistical Society, 1, 217–235.