References
Adair, G. (1984). The hawthorne effect: A reconsideration of the
methodological artifact. Journal of Applied Psychology,
69, 334–345.
Agresti, A. (1996). An introduction to categorical data
analysis. Wiley.
Agresti, A. (2002). Categorical data analysis (2nd ed.). Wiley.
Akaike, H. (1974). A new look at the statistical model identification.
IEEE Transactions on Automatic Control, 19, 716–723.
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.
Box, G. E. P. (1953). Non-normality and tests on variances.
Biometrika, 40, 318–335.
Box, J. F. (1987). Guinness, gosset, fisher, and small samples.
Statistical Science, 2, 45–52.
Brown, M. B., & Forsythe, A. B. (1974). Robust tests for equality of
variances. Journal of the American Statistical Association,
69, 364–367.
Campbell, D. T., & Stanley, J. C. (1963). Experimental and
quasi-experimental designs for research. Houghton Mifflin.
Chronbach, L. J. (1951). Coefficient alpha and the internal structure of
tests. Psychometrika, 16(3), 297–334.
Cochran, W. G. (1954). The χ2 test of goodness of
fit. The Annals of Mathematical Statistics, 23,
315–345.
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd ed.). Lawrence Erlbaum.
Cramer, H. (1946). Mathematical methods of statistics.
Princeton University Press.
Dunn, O. J. (1961). Multiple comparisons among means. Journal of the
American Statistical Association, 56, 52–64.
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.
Evans, M., Hastings, N., & Peacock, B. (2011). Statistical
distributions (3rd ed). Wiley.
Everitt, B. S. (1996). Making sense of statistics in psychology. A
second-level course. Oxford University Press.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J.
(1999). Evaluating the use of exploratory factor analysis in
psychological research. Psychological Methods, 4,
272–299.
Fisher, R. A. (1922a). On the interpretation of χ2 from contingency
tables, and the calculation of p. 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.
Gelman, A., & Stern, H. (2006). The difference between
“significant” and “not significant” is not
itself statistically significant. The American Statistician,
60, 328–331.
Geschwind, N. (1972). Language and the brain. Scientific
American, 226(4), 76–83.
Hays, W. L. (1994). Statistics (5th ed.). Harcourt Brace.
Hedges, L. V. (1981). Distribution theory for glass’s estimator of
effect size and related estimators. Journal of Educational
Statistics, 6, 107–128.
Hedges, L. V., & Olkin, I. (1985). Statistical methods for
meta-analysis. Academic Press.
Hewitt, A. K., Foxcroft, D. R., & MacDonald, J. (2004).
Multitrait-multimethod confirmatory factor analysis of the attributional
style questionnaire. Personality and Individual Differences,
37(7), 1483–1491.
Hogg, R. V., McKean, J. V., & Craig, A. T. (2005). Introduction
to mathematical statistics (6th ed.). Pearson.
Holm, S. (1979). A simple sequentially rejective multiple test
procedure. Scandinavian Journal of Statistics, 6,
65–70.
Hróbjartsson, A., & Gøtzsche, P. (2010). Placebo interventions for
all clinical conditions. Cochrane Database of Systematic
Reviews, 1. https://doi.org//10.1002/14651858.CD003974.pub3
Hsu, J. C. (1996). Multiple comparisons: Theory and methods.
Chapman; Hall.
Ioannidis, J. P. A. (2005). Why most published research findings are
false. PLoS Med, 2(8), 697–701.
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.
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction.
Psychological Review, 80, 237–251.
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.
Kruschke, J. K. (2011). Doing Bayesian data analysis: A
tutorial with R and BUGS. Academic Press.
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in
one-criterion variance analysis. Journal of the American Statistical
Association, 47, 583–621.
Kühberger, A., Fritz, A., & Scherndl, T. (2014). Publication bias in
psychology: A diagnosis based on the correlation between effect size and
sample size. Public Library of Science One, 9, 1–8.
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.
Lee, M. D., & Wagenmakers, E.-J. (2014). Bayesian cognitive
modeling: A practical course. Cambridge University Press.
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.
McGrath, R. E., & Meyer, G. J. (2006). When effect sizes disagree:
The case of r and d. Psychological Methods,
11, 386–401.
Meehl, P. H. (1967). Theory testing in psychology and physics: A
methodological paradox. Philosophy of Science, 34,
103–115.
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.
Peterson, C., & Seligman, M. (1984). Causal explanations as a risk
factor for depression: Theory and evidence. Psychological
Review, 91, 347–374.
Pfungst, O. (1911). Clever hans (the horse of mr. Von osten): A
contribution to experimental animal and human psychology (C. L.
Rahn, Trans.). Henry Holt.
Rosenthal, R. (1966). Experimenter effects in behavioral
research. Appleton.
Sahai, H., & Ageel, M. I. (2000). The analysis of variance:
Fixed, random and mixed models. Birkhauser.
Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of
Psychology, 46, 561–584.
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.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty:
Heuristics and biases. Science, 185(4157), 1124–1131.
Welch, B. L. (1947). The generalization of
“Student’s” problem when several different
population variances are involved. Biometrika, 34,
28–35.
Welch, B. L. (1951). On the comparison of several mean values: An
alternative approach. Biometrika, 38, 330–336.
Wilkinson, L., Wills, D., Rope, D., Norton, A., & Dubbs, R. (2006).
The grammar of graphics. Springer.