ISSN 2520-6265 (print)

ISSN 2520-6273 (online)


A. G. Vinogradov, PhD D.Sc. (Candidate of Psychological Sciences), Associate Professor (docent), Social Psychology Department,
Taras Shevchenko National University of Kyiv
ORCID: 0000-0003-1250-3863


Article Information
Issue: 2(14) 2020, pages: 28-63
Language: Ukrainian
Received: 24.09.2020
1 stRevision: 26.09.2020
Accepted: 01.10.2020


The article belongs to a special modern genre of scholar publications, so-called tutorials – articles devoted to the application of the latest methods of design, modeling or analysis in an accessible format in order to disseminate best practices. The article acquaints Ukrainian psychologists with the basics of using the R programming language to the analysis of empirical research data. The article discusses the current state of world psychology in connection with the Crisis of Confidence, which arose due to the low reproducibility of empirical research. This problem is caused by poor quality of psychological measurement tools, insufficient attention to adequate sample planning, typical statistical hypothesis testing practices, and so-called “questionable research practices.” The tutorial demonstrates methods for determining the sample size depending on the expected magnitude of the effect size and desired statistical power, performing basic variable transformations and statistical analysis of psychological research data using language and environment R. The tutorial presents minimal system of R functions required to carry out: modern analysis of reliability of measurement scales, sample size calculation, point and interval estimation of effect size for four the most widespread in psychology designs for the analysis of two variables’ interdependence. These typical problems include finding the differences between the means and variances in two or more samples, correlations between continuous and categorical variables. Practical information on data preparation, import, basic transformations, and application of basic statistical methods in the cloud version of RStudio is provided.
Key words:tutorial, R, effect size, confidence intervals, statistical power, principles of Open Science.

References transliterated:

Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154–174.

Baker, F. B., & Kim, S.-H. (2017). The Basics of Item Response Theory Using R. Springer.

Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71(3), 425–440.

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.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd edition). Routledge. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A Global Measure of Perceived Stress. Journal of Health and Social Behavior, 24(4), 385–396. https://doi. org/10.2307/2136404

Cooper, H. M. (2020). Reporting Quantitative Research in Psychology: How to Meet APA Style Journal Article Reporting Standards (Second Edition, Revised). American Psychological Association.

Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. J. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13–29. https://

Cumming, G. (2011). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge.

Ellis, P. D. (2010). The Essential Guide to Effect Sizes: Statistical Power, MetaAnalysis, and the Interpretation of Research Results. Cambridge University Press.

Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. SAGE Publications Ltd.

Flake, J. K., & Fried, E. I. (2019). Measurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them [Preprint]. PsyArXiv. https://doi. org/10.31234/

Greiff, S., van der Westhuizen, L., Mund, M., Rauthmann, J. F., & Wetzel, E. (2020). Introducing New Open Science Practices at EJPA. European Journal of Psychological Assessment, 36(5), 717–720.

Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLOS Medicine, 2(8), e124.

Kline, R. B. (2013). Beyond Significance Testing: Statistics Reform in the Behavioral Sciences (Second edition). American Psychological Association.

Kline, R. B. (2019). Becoming a Behavioral Science Researcher: A Guide to Producing Research That Matters (Second edition). The Guilford Press.

Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 1–9.

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.

Publication Manual of the American Psychological Association: 7nth Edition. (2019). American Psychological Association. Rasch, D., Kubinger, K., & Yanagida, T. (2011). Statistics in Psychology Using R and SPSS.

Wiley. Schmuller, J. (2017). Statistical Analysis with R For Dummies. For Dummies.

Wilcox, R. (2017). Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction (2nd Edition). Chapman and Hall/CRC.

Kabakov, R. (2014). R v dejstvii. Analiz i vizualizacija dannyh v programme R. [R in Action: data analysis and visualization in R] Moscow : DMK Press. [in Russian].

Majboroda, R. Je. (2019). Komp’juterna statystyka: Pidruchnyk [Computer statistics: a textbook]. Kyiv : VPC «Kyjivsjkyj universytet». [in Ukrainian].

Mastickij, S. Je., & Shitikov, V. K. (2015). Statisticheskij analiz i vizualizacija dannyh s pomoshh’ju R [Statistical analysis and data visualization using R]. Moscow : DMK Press. [in Russian].

Mjetloff, N. (2019). Iskusstvo programmirovanija na R. Pogruzhenie v bol’shie dannye [Art of R programming. Dive into Big Data]. Saint-Petersburg : Piter. [in Russian].

O’Nil, K., & Shatt, R. (2019). Data Science. Insajderskaja informacija dlja novichkov. Vkljuchaja jazyk R [Data Science. Insider information for beginners including R language]. Saint-Petersburg : Piter. [in Russian].

Uikem, H., & Groulmund, G. (2016). Jazyk R v zadachah nauki o dannyh: Import, podgotovka, obrabotka, vizualizacija i modelirovanie dannyh [R for Data Science: Visualize, Model, Transform, Tidy, and Import Data]. Moscow : Vil’jams. [in Russian].

Shipunov, A. B., Baldin, E. M., Volkova, P. A., Korobejnikov, A. I., Nazarova, S. A., Petrov, S. V., & Sufijanov, V. G. (2012). Nagljadnaja statistika. Ispol’zuem R! [Visual Statistics: Use R!] Moscow : DMK Press. [in Russian].