Integrating R Into Statistics and Data Analysis Education: Learnings From the Development and Evaluation of a Teaching Concept for Communication Science

Abstract

Despite the increasing use of the statistical programming language R in statistics and data analysis (SDA), its implementation in communication science education is limited. Experiences, recommendations, and a critical exchange are therefore scarce. The following contribution addresses this very gap. At the Department of Journalism and Communication Research of the Hanover University of Music, Drama and Media, we have transitioned the SDA education to R. We share our concept and demonstrate its success. The results of an online survey indicate that students perceived most teaching elements as helpful, recognizing both opportunities and challenges. We present key learnings designed to assist others in successfully integrating R into SDA education.

Keywords

statistics and data analysis communication science education programming language R SPSS

Get full access to this article

View all access options for this article.

References

Academy of Active Learning Arts and Sciences. (n.d.). Updated definition of flipped learning. https://aalasinternational.org/updated-definition-of-flipped-learning/

Atteveldt

Trilling

Arcila

(2022). Computational analysis of communication: A practical introduction to the analysis of texts, networks, and images with code examples in Python and R. Wiley Blackwell. http://cssbook.net/

Bandura

(1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Baumer

Cetinkaya-Rundel

Bray

Loi

Horton

N. J.

(2014). R Markdown: Integrating a reproducible analysis tool into introductory statistics. Technology Innovations in Statistics Education, 8(1), 1–22. https://doi.org/10.5070/T581020118

Bryant

Pribanic-Smith

E. J.

(2010). A historical overview of research in communication science. In Berger

C. R.

Roloff

M. E.

Roskos-Ewoldsen

D. R.

(Eds.), The handbook of communication science (pp. 21–36). Sage.

Felten

(2013). Principles of good practice in SoTL. Teaching & Learning Inquiry. The ISSOTL Journal, 1(1), 121–125. https://journalhosting.ucalgary.ca/index.php/TLI/article/view/57376/43149

Hall

Vance

E. A.

(2010). Improving self-efficacy in statistics: Role of self-explanation & feedback. Journal of Statistics Education, 18(3), 1–22. https://doi.org/10.1080/10691898.2010.11889583

Hayes

A. F.

(2020). Statistical methods for communication science. Routledge.

Hendricks

K. S.

(2016). The sources of self-efficacy: Educational research and implications for music. Update: Applications of Research in Music Education, 35(1), 32–38. https://doi.org/10.1177/8755123315576535

10.

Kaufmann

Ninaus

Weiss

E. M.

Gruber

Wood

(2022). Self-efficacy matters: Influence of students’ perceived self-efficacy on statistics anxiety. Annals of the New York Academy of Sciences, 1514(1), 187–197. https://doi.org/10.1111/nyas.14797

11.

Mayring

(2000). Qualitative content analysis. Forum Qualitative Sozialforschung Forum: Qualitative Social Research, 1(2), 20. https://doi.org/10.17169/fqs-1.2.1089

12.

Muenchen

R. A.

(n.d.). The popularity of data science software. https://r4stats.com/articles/popularity/

13.

Niemann-Lenz

Bruns

Hefner

Knop-Hülß

Possler

Reich

Reinecke

Scheper

Klimmt

(2019). Crafting a strategic roadmap for computational methods in communication science: Learnings from the CCS 2018 conference in Hanover. International Journal of Communication, 13, 3885–3893. https://www.ijoc.org/index.php/ijoc/article/view/10534

14.

Niemann-Lenz

Reinhard

(2023). Data Literacy Education mit R und Rstudio [Data Literacy Education with R and RStudio]. In Koch

Sievers

(Eds.), Datenkompetent. Beispielhafte Lehrformate und-strategien für die Data Literacy Education an deutschen Hochschulen. [Data Competence: Exemplary Teaching Formats and Strategies for Data Literacy Education at German Universities]. Stifterverband. https://www.datenkompetent.org/posts/r_studio/rstudio.html

15.

Peng

R. D.

(2022). R programming for data science. https://bookdown.org/rdpeng/rprogdatascience/

16.

Perepiczka

Chandler

Becerra

(2011). Relationship between graduate students’ statistics self-efficacy, statistics anxiety, attitude toward statistics, and social support. Professional Counselor, 1(2), 99–108. https://doi.org/10.15241/mpa.1.2.99

17.

Rode

J. B.

Ringel

M. M.

(2019). Statistical software output in the classroom: A comparison of R and SPSS. Teaching of Psychology, 46(4), 319–327. https://doi.org/10.1177/0098628319872605

18.

Scheper

Kathirgamalingam

(2022). Fünf Thesen zur Integration von CCS in der Lehre [Five theses on the integration of CSS in education]. Aviso, 22(2), 6–7.

19.

Tucker

M. C.

Shaw

S. T.

Son

J. Y.

Stigler

J. W.

(2023). Teaching statistics and data analysis with R. Journal of Statistics and Data Science Education, 31(1), 18–32. https://doi.org/10.1080/26939169.2022.2089410

20.

Ward

B. W.

(2013). What’s better—R, SAS^®, SPSS^®, or Stata^®? Thoughts for instructors of statistics and research methods courses. Journal of Applied Social Science, 7(1), 115–120. https://doi.org/10.1177/1936724412450570

21.

Wickham

Cetinkaya-Rundel

Gorlemund

(2023). R for data science. Import, tidy, transform, visualize, and model data (2nd ed.). O’Reilly. https://r4ds.hadley.nz/

22.

Zheng

Guo

(2020). Medical students’ motivation and academic performance: The mediating roles of self-efficacy and learning engagement. Medical Education Online, 25(1), 1742964. https://doi.org/10.1080/10872981.2020.1742964