References
Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An
alternative to Cohen’s standardized mean
difference effect size: A robust parameter and confidence interval in
the two independent groups case. Psychological Methods,
10(3), 317–328. https://doi.org/10.1037/1082-989X.10.3.317
Anaconda. (2022). 2022 state of data science report. https://know.anaconda.com/rs/387-XNW-688/images/ANA_2022SODSReport.pdf
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects
modeling with crossed random effects for subjects and items. Journal
of Memory and Language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005
Bakeman, R. (2005). Recommended effect size statistics for repeated
measures designs. Behavior Research Methods, 37(3),
379–384.
Bayes, T. (1763). An essay towards solving a problem in the doctrine of
chances. Philosophical Transactions of the Royal Society of
London, 53, 370–418. https://doi.org/10.1098/rstl.1763.0053
Behmer, L. P. (2017). Spatial and temporal aspects of speech
planning: An articulographic investigation [PhD thesis]. The
University of Nebraska-Lincoln.
Belenky, G., Wesensten, N. J., Thorne, D. R., Thomas, M. L., Sing, H.
C., Redmond, D. P., Russo, M. B., & Balkin, T. J. (2003). Patterns
of performance degradation and restoration during sleep restriction and
subsequent recovery: A sleep dose-response study. Journal of Sleep
Research, 12(1), 1–12. https://doi.org/https://doi.org/10.1046/j.1365-2869.2003.00337.x
Bloom, N., Han, R., & Liang, J. (2024). Hybrid working from home
improves retention without damaging performance. Nature,
630(8018), 920–925. https://doi.org/10.1038/s41586-024-07500-2
Bommarito, E., & Bommarito, I., Michael J. (2021). An empirical
analysis of the R package ecosystem. CoRR,
abs/2102.09904. https://arxiv.org/abs/2102.09904
Box, J. F. (1981). Gosset, Fisher, and the t distribution.
The American Statistician, 35(2), 61–66. https://doi.org/10.1080/00031305.1981.10479314
Bürkner, P.-C., & Vuorre, M. (2019). Ordinal regression models in
psychology: A tutorial. Advances in Methods and Practices in
Psychological Science, 2(1), 77–101. https://doi.org/10.1177/2515245918823199
Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley,
D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks
that define or explain statistical significance do so incorrectly.
Advances in Methods and Practices in Psychological Science,
2(3), 233–239.
Chetty, R., Jackson, M. O., Kuchler, T., Stroebel, J., Hendren, N., et
al. (2022). Social capital i: Measurement and associations with economic
mobility. Nature, 608(7921), 108–121. https://doi.org/10.1038/s41586-022-04996-4
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd ed.). Lawrence Erlbaum Associates.
Cornell, C., & Berger, M. P. F. (1992). The power of the
multivariate analogue of the t test when population variances are
unequal. Multivariate Behavioral Research, 27(4),
467–499. https://doi.org/10.1207/s15327906mbr2704_4
Cotton, R. (2013). Learning r: A step-by-step function guide to data
analysis. O’Reilly Media.
Cumming, G. (2013). Understanding the new statistics: Effect sizes,
confidence intervals, and meta-analysis. Routledge. https://doi.org/10.4324/9780203807002
Di Forti, M., Quattrone, D., Freeman, T. P., Tripoli, G.,
Gayer-Anderson, C., & al., et. (2019). The contribution of cannabis
use to variation in the incidence of psychotic disorder across europe
(EU-GEI): A multicentre case–control study. The Lancet
Psychiatry, 6(5), 427–436. https://doi.org/10.1016/S2215-0366(19)30048-3
Dobbie, W., Goldin, J., & Yang, C. S. (2018). The effects of
pretrial detention on conviction, future crime, and employment: Evidence
from randomly assigned judges. American Economic Review,
108(2), 201–240. https://doi.org/10.1257/aer.20161503
Engzell, P., Frey, A., & Verhagen, M. D. (2021). Learning loss due
to school closures during the COVID-19 pandemic. Proceedings of the
National Academy of Sciences, 118(17), e2022376118. https://doi.org/10.1073/pnas.2022376118
Eysenck, H. J., & Eysenck, S. B. G. (1963). Eysenck
Personality Inventory (EPQ,
EPI) [Database record]. APA PsycTests. https://doi.org/10.1037/t02711-000
Fisher, R. A. (1925). Statistical methods for research workers.
Oliver; Boyd.
Fisher, R. A. (1935). The design of experiments. Oliver; Boyd.
Geisser, S. (1958). An extension of the greenhouse–geisser procedure for
multiple‐degree‐of‐freedom contrasts. Biometrika,
45(1/2), 275–277. https://doi.org/10.2307/2333591
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A.,
& Rubin, D. B. (2013). Bayesian data analysis (3rd ed.).
CRC Press. https://doi.org/10.1201/b16018
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other
stories. Cambridge University Press. https://doi.org/10.1017/9781139161879
Gelman, A., & Stern, H. (2006). The difference between
“significant” and “not significant” is not
itself statistically significant. The American Statistician,
60(4), 328–331. https://doi.org/10.1198/000313006X152649
Goodman, S. (2008). A dirty dozen: Twelve p-value misconceptions.
Seminar on Statistics.
Greenhouse, S. W., & Geisser, S. (1959). Methods in the analysis of
profile data. Psychometrika, 24(2), 95–112. https://doi.org/10.1007/BF02289823
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C.,
Goodman, S. N., & Altman, D. G. (2016). Statistical tests,
P values, confidence intervals, and power: A guide to
misinterpretations. European Journal of Epidemiology,
31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
Grolemund, G. (2014). Hands-on programming with r: Write your own
functions and simulations. O’Reilly Media.
Haller, H., & Krauss, S. (2002). Misinterpretations of significance:
A problem students share with their teachers. Methods of
Psychological Research, 7(1), 1–20.
Harford, T. (2021). How to make the world add up: Ten rules for
thinking differently about numbers. Bridge Street Press.
Healy, K. (2018). Data visualization: A practical introduction.
Princeton University Press. https://socviz.co/
Hedges, L. V. (1981). Distribution theory for glass’s estimator of
effect size and related estimators. Journal of Educational
Statistics, 6(2), 107–128. https://doi.org/10.2307/1164588
Hippel, P. T. von. (2005). Mean, median, and skew: Correcting a textbook
rule. Journal of Statistics Education, 13(2).
Huynh, H. (1970). Conditions under which mean square ratios in repeated
measurements designs have exact f‐distributions. Journal of the
Royal Statistical Society. Series B (Methodological),
32(2), 317–322.
Huynh, H., & Feldt, L. S. (1976). Estimation of the
box–greenhouse–geisser epsilon: A note on the violation of the
sphericity assumption. Psychometrika, 41(3), 351–360.
https://doi.org/10.1007/BF02293858
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An
introduction to statistical learning: With applications in r (2nd
ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1
Jones, L., Barnett, A. G., & Vagenas, D. (2025). Common
misconceptions held by health researchers when interpreting linear
regression assumptions: A cross-sectional study. BMJ Open,
15(1), e093111. https://doi.org/10.1136/bmjopen-2024-093111
Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli
as a random factor in social psychology: A new and comprehensive
solution to a pervasive but largely ignored problem. Journal of
Personality and Social Psychology, 103(1), 54–69. https://doi.org/10.1037/a0028347
Keselman, H. J. (1980). Testing repeated measures hypotheses when the
sphericity assumption is not met. Psychological Bulletin,
88(2), 320–325. https://doi.org/10.1037/0033-2909.88.2.320
Keselman, H. J., Algina, J., & Kowalchuk, R. K. (2001). The analysis
of repeated measures designs: A review. British Journal of
Mathematical and Statistical Psychology, 54(1), 1–20. https://doi.org/10.1348/000711001159357
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N.,
Shablack, H., Jonides, J., & Ybarra, O. (2013). Facebook
Use Predicts Declines in
Subjective Well-Being in
Young Adults. PLoS ONE,
8(8), e69841. https://doi.org/10.1371/journal.pone.0069841
Lakens, D. (2022). Sample size justification. Collabra:
Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267
Lambert, B. (2018). A student’s guide to bayesian statistics.
SAGE Publications.
Langsrud, Ø. (2003a). ANOVA for unbalanced data: Use type II instead of
type III sums of squares. Statistics and Computing,
13(2), 163–167. https://doi.org/10.1023/A:1023260610025
Langsrud, Ø. (2003b). ANOVA for unbalanced data: Use
Type II instead of Type III sums of squares.
Statistics and Computing, 13(2), 163–167. https://doi.org/10.1023/A:1023260610025
Liddell, T. M., & Kruschke, J. K. (2018). Analyzing ordinal data
with metric models: What could possibly go wrong? Journal of
Experimental Social Psychology, 79, 328–348. https://doi.org/10.1016/j.jesp.2018.08.009
Mauchly, J. W. (1940). Significance test for sphericity of a normal
n-dimensional distribution. Annals of Mathematical Statistics,
11(2), 204–209. https://doi.org/10.1214/aoms/1177731915
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing
experiments and analyzing data: A model comparison perspective (3rd
ed.). Routledge. https://doi.org/10.4324/9781315642956
McElreath, R. (2020). Statistical rethinking: A bayesian course with
examples in R and Stan (2nd ed.). CRC
Press. https://doi.org/10.1201/9780429029608
Oertzen, T. von, Brandmaier, A. M., & Tsang, S. (2010).
Zero-inflated and hurdle models for count data in psychology: A
tutorial. European Journal of Developmental Psychology,
7(5), 554–572.
Pomiechowska, B., Glawcinski, I., Kompatsiaris, I., & Wagemans, J.
(2021). Nonverbal cues that convey social status: A cross-cultural
analysis of eye gaze and posture in dyadic interactions. Journal of
Nonverbal Behavior, 45(4), 481–504. https://doi.org/10.1007/s10919-021-00404-z
Posit PBC. (2024). Benefit corporation annual report: 2024 annual
report. Posit PBC. https://posit.co/about/pbc-report-2024/
Reinhart, A. (2015). Statistics done wrong: The woefully complete
guide. No Starch Press.
Rowntree, D. (2018). Statistics without tears: An introduction for
non-mathematicians (Revised edition). Penguin Books.
Taleb, N. N. (2007). The black swan: The impact of the highly
improbable. Random House.
Thompson, D. M., Wu, J. Y. Y., Yoder, J. A., & Hall, A. B. (2020).
Universal vote-by-mail has no impact on partisan turnout or vote share.
Proceedings of the National Academy of Sciences,
117(25), 14052–14056. https://doi.org/10.1073/pnas.2007249117
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Vanhove, J. (2021). Collinearity isn’t a disease that needs curing.
Meta-Psychology, 5. https://doi.org/10.15626/MP.2021.2548
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on
p-values: Context, process, and purpose. The American
Statistician, 70(2), 129–133.
Westfall, P. H. (2014a). Kurtosis as peakedness, 1905–2014. r.i.p.
The American Statistician, 68(3), 191–195. https://doi.org/10.1080/00031305.2014.917055
Westfall, P. H. (2014b). Kurtosis as peakedness, 1905–2014.
R.I.P. The American Statistician, 68(3),
191–195. https://doi.org/10.1080/00031305.2014.917055
Wickham, H., & Grolemund, G. (2023). R for data science: Import,
tidy, transform, visualize, and model data (2nd ed.). O’Reilly
Media. https://r4ds.hadley.nz/
Wright, D. B. (1999). Modelling clustered data in autobiographical
memory research: The multilevel approach. Applied Cognitive
Psychology, 13(4), 337–350. https://doi.org/10.1002/(SICI)1099-0720(199908)13:4<337::AID-ACP582>3.0.CO;2-6
YouGov. (2025). Methodology: How does YouGov conduct
public opinion research. https://yougov.co.uk/about/panel-methodology
Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models
for count data in R. Journal of Statistical
Software, 27(8), 1–25. https://doi.org/10.18637/jss.v027.i08