Application of statistical software in quantitative sports marketing studies

Document Type : Original Article


Associate Professor of Sports Management, Payame Noor University, Tehran, Iran.


With the increasing growth of scientific research in the field of physical education and sports science, the use of statistical software has become obvious. In fact, choosing a type of statistical software according to its features and considering ethical issues may require payment. Today, many statistical softwares are provided for free. Some are in the category of general software that has the ability to perform a wide range of statistical analysis, and on the other hand, small and limited software that has been developed to perform a specific analysis. Some of these softwares have been created only for calculations and have the role of a software calculator, while others are in the form of plugins and can not be run independently. Also, some software has been developed only for a specific research group such as agriculture, social sciences, physiology, biomechanics, etc. This section introduces general statistical software that has the ability to perform a wide range of statistical analysis. The purpose of this study is to review free statistical software in physical education and sports sciences, which are: SPSS ،Lisrel ،Amos ،Pls ،EQS ،Jasp ،Jamovi ، MaxStat ،PSPP ،R ،Stata ،S-Plus ،Excel ،Statistician ،Minitab ،Atlas ،Xlstat ، Origin،Systat ,Past. Statistical software has special features depending on their nature. Accordingly, the choice of a free statistical software depends to a large extent on the capabilities of the statistical test desired by the software by the software, but in commercial versions due to the breadth and variety of commands and statistical commands in the software, this factor is a limited factor. Is not considered a supplier. One of the most important and essential things in choosing a statistical software is the user interface of that software. In fact, the user interface of & statistical softwares is considered graphically or selectively oriented. But the user interface of R statistical software is textual and commands are given to the software through the programming language. In this regard, learning and understanding software with a graphical user interface is possible in a few hours, but in order to learn R software, you need to spend more time. Some of these statistical softwares such as R and PSPP have help and training packages that do not help the user to learn the basics of the software. Also, many softwares have online guides and educational pages in cyberspace, such as statistical softwares R, PSPP, Vista and OpenStat (Hakimi, 2014). Choosing a statistical package by consumers is often a difficult process and it is necessary to compare different products in order to select the most appropriate one, and this selection should be done based on clear criteria and in accordance with the goals set by researchers (Lina et al. , 2021). In this regard, in order to compare free statistical software, after downloading and installing this software from publishing sites, using the guide in the software as well as sample data, their efficiency and ability in different commands were compared. In order to evaluate the use of statistical software, the number of articles published in ScienceDirect database was measured. Figure 1 shows the number of articles published in 2020 that statistical software was used to analyze the data. Based on these results, SPSS software has the highest usage and MINITAB and SYSTAT software are in the next ranks. The results showed that all these softwares are legally free and without payment and all the results extracted from them can be published. Also Lisrel, Amos, Pls, EQS software are most used in the field of structural equation modeling. In this study, several software packages with their capabilities, compatibility, usefulness and limitations are explained, so what is certain is that researchers use different statistical packages to analyze similar data. Each packet has basic statistical capabilities that can be used, but each packet has its own advantages and disadvantages for using different types of data analysis. Many statistical software developers try to make the graphical user interface capabilities more user-friendly. According to the results, SPSS and Minitab are still the most popular tools for statistical data analysis, especially for those with low statistical or mathematical competencies. The results show that free and open source statistical software have good capabilities, but how they can be used in more advanced ways is questionable. All statistical software has almost the same results (the number of decimals or some decimals may vary at best), although they follow different algorithms in each case. In addition, more information is provided in each software than in other packages. Therefore, according to the need, users should choose their software carefully. When processing and analyzing data, it is important to choose the appropriate statistical method to obtain correct decisions and & interpretations. Underestimating the importance of statistical methods and proper organization of relevant experiments can lead to incorrect scientific results.On the other hand, the large number of software packages and the fact that they offer similar or relatively identical opportunities suggest a complex choice that often cannot be made effectively due to the lack of a benchmarking system. Conducting studies such as the present study can help researchers select software that facilitates their work as much as possible in achieving the stated research goal. The choice of statistical software packages for learning should be based on the suitability of the software for all the analyzes that the researcher may want to analyze. Therefore, it is recommended to select at least two softwares to provide a broader and stronger learning analysis of the research. Finally, the researcher's opinion, after analyzing the field data to a great extent, is that statistical software has made a significant contribution to humanities research, especially in the field of demographic and data analysis. This was achieved by using a scientific approach to solve fundamental problems in research that is data analysis. Although some other factors such as literature review, methodology and findings have affected the quality of research work, but it is quite clear that the impact of statistical software packages on research analysis and findings can not be estimated.


Abatan S. M., & Olayemi M. S., (2014). The Role of Statistical Software in Data Analysis. International Journal of Applied Research and Studies. Tehran: Samt Publications. Vol.(3), 22.
Abbas, FM., Alkarkhi, Wasin, AA., & Alqaraghuli. (2020). R Statistical saftware. Applied statistics for environmenttal science with R. PP:11-27. (in persian)
Adetola O.G. (2013). Learning Statistical Package Workbook. Nigeria (Unpublised).
Akindutire, A.F. (2013). Usage of Mobile Phone: Deception, Deviance and Fraud (A case study of Ambrose-Alli).
Allen, T., (2019). Software overview and methods review: Minitab. In Introduction to Engineering Statistics and Lean Six Sigma, Springer, London. (pp. 575-600).
AlNuaimi, B.K., Khan, M., & Ajmal, M.M. (2021). The role of big data analytics capabilities in greening e procurement: a higher order PLS-SEM analysis. Technol. Forecast. Soc. Change 169, 120808.
Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, (567), PP:305–307.
Arbuckle, J. L. (2007). Amos User Guide. Chicago, Marketing department, SPSS, Inc.
Ariff, M., Yeow, S. M., Zakuan, N., Jusoh, A., & Bahari, A. Z. (2012). The effects of computer Self-Efficacy and technology acceptance model on behavioral intention in Internet Banking System. Procedia-Social and Behavioral Sciences,Vol(57), PP:448-452.
Chi Ch., Gardiner, J., Houang, R., & Yan-Liang Y. (2020). Comparing multiple statistical software for multiple-indicator, multiple-cause modeling: an application of gender disparity in adult cognitive functioning using MIDUS II dataset. BMC Medical Research Methodology, Vol (20), PP:275. 26
Chris McGann. (2014). Role of Statistical Software, eHow Contributor /facts_ 6923793 _role-statistical-software.html
Ganesh D., & Justin P., (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting & Social Change, 173, PP:1-11.
Julián, G., Garrido, A., Distante, D., & Rossi, G., (2016). Assessing refactorings for usability in e-commerce applications. Empirical Software Engineering 21 (3): PP:1224-1271.
Hair Jr, F., J., Matthews, L.M., Matthews, R.L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. Int. J. Multivar. Data Anal. 1 (2), PP:107–123.
Hakimi mofrad, R., & Hakimi mofrad, R., (2014). An overview of free statistical software.University Journal of E-Learning. Vol.5(1), PP:21-28. (in persian)
Hammer, O., Harper, D.A.T., & Paul D. R., (2001). Past: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica, vol. 4, issue 1, art. 4: 9, PP:178.
Hilary I., Okagbue., Pelumi E., Oguntunde., Emmanuela C.M. Obasi., & Elvir M., Akhmetshin. (2021). Trends and usage pattern of SPSS and Minitab Software in Scientific research. International Conference on Recent Trends in Applied Research. IOP Publishing, 1734
Hooman, H.A., (2009). Structural equation modeling using LISREL software. Vol.5(2), PP:167-202(in persian)
Hothorn T., & Hothorn MT. (2017). The maxstat Package. pub/ cran/ web/packages/ maxstat/maxstat.pdf
Hsu, M. K., Wang, S. W., & Chiu, K. K. (2009). Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Computers in Human Behavior, 25, PP:412-420.
IBM Corp. (2016). IBM SPSS Amos for Windows. IBM Corp., Armonk, NY. Version 24.0. J¨oreskog, K.G., S¨orbom, D., LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International, Chicago, IL, US.
James G., Moberly., Matthew T., Bernards., & Kristopher V. Waynant. (2018). Key features and updates for Origin 2018. Journal of Cheminformatics. Vol. 10(5).
JASP Team. (2019). JASP (Version 0.11.1) [Computer software]. & Jonathon Love., Ravi Selker., Maarten Marsman., Tahira Jamil., Damian Dropmann., Josine Verhagen., Alexander Ly., Quentin F., Gronau., Martin Šmíra., Sacha Epskamp., Dora Matzke., Anneliese Wild., Patrick Knight., Jeffrey N. Rouder., Richard D. Morey., & Eric-Jan Wagenmakers. (2019). JASP: Graphical Statistical Software for Common Statistical Designs. Journal of Statistical Software, vol. 88(2), PP:1-17.
Julien Lauzon-Gauthier, Petre Manolescu, Carl Duchesne. (2018). The Sequential Multi-block PLS algorithm (SMB-PLS): Comparison of performance and interpretability. Chemometrics and Intelligent Laboratory Systems
Kiraz, E., & Ozdemir, D. (2006). The Relationship between educational ideologies and technology acceptance in preservice teachers. Educational Technology & Society, 9(2), PP:152-165.
Kirkpatrick, Lee A., & Brooke C. Feeney. (2010). A Simple Guide to SPSS for Version 17.0. Edition: 10. John- David Hague: USA.
Klara Rybenska., Josef Sedivy., & Lucie Kudova. (2014). Comparative analysis of the use of open source software in teaching of data processing. International Journal of Education and Information Technology. Vol. 8, PP:130- 137.
Latikka, R., Turja, T., & Oksanen, A. (2019). Self-efficacy and acceptance of robots. Computers in Human Behavior, 93, PP:157-163.
Lewis, James R. 2018. Measuring Perceived Usability: The CSUQ، SUS، and UMUX. International Journal of Human–Computer Interaction. Vol 34 (12), PP:1148-1156.
Ley, Christophe. Mike Tibolt., & Dirk Fromme. (2020). Data-Centric Engineering in modern science from the perspective of a statistician, an engineer, and a software developer. Data-Centric Engineering (2020), 1: e2
Lina Yordanova., Gabriela Kiryakova., Petya Veleva., Nadezhda Angelova., & Antoaneta Yordanova. (2021). Criteria for selection of statistical data processing software. IOP Conference Series: Materials Science and Engineering,
Martin A., Andresen. J.C. Barnes, & David R. Forde. (2021). R (Statistical Software). First published: 20 August 2021.
Minerva Sto, Tomas., Darin Jan, Tindowen., Marie Jean, Mendezabal., Pyrene, Quilang., & Erovita Teresita, Agustin. (2019). The Use of PSPP Software in Learning Statistics. European Journal of Educational Research, vol8(4), PP:1127-1136. 28  
Motaharinejad, H., & Vaziri shahre babak, B., (2016). Managerial and organizational factors affecting the acceptance of information technology in schools from the perspective of teachers. Bi-Quarterly Journal of Management Training of Organizations. Vol.5(2), PP:167-202(in persian)
Motevali, K., & Yaghoubi, Z., (2013). Application of SPSS software in chemistry.8th Chemistry Education Seminar in Iran. Faculty of Chemistry, Semnan University. (in persian)
Mueller, R.O., & Hancock, G.R. (2018). Structural equation modeling. The Reviewer’s Guide to Quantitative Methods in the Social Sciences 445–456.
Muenchen, Bob., & Sean, Mackinnon. (2019). Is Scholarly Use of R Use Beating SPSS Already[1]r-use-beating-spss-already/.
Murat Doğan Şahin., & Eren Can Aybek. (2019). Jamovi: An Easy to Use Statistical Software for the Social Scientists. International Journal of Assessment Tools in Education. Vol. 6(4), PP:670–692.
Navarro, D.J., & Foxcroft, D.R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners. (Version 0.70). doi: 10.24384/hgc3-7p15 [Available from].
Nazanin Nooraee., Geert Molenbergh., Edwin R., & van den Heuvel. (2014). GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R[1]repolr, SAS-GENMOD, SPSS-GENLIN. Computational Statistics and Data Analysis. Vol77, PP:70–83.
Nevin Cavusoglu. (2012). LISREL growth model on direct and indirect effects using cross-country data. Economic Modelling 29, PP:2362–2370.
Odusina E.K. (2011). Computer Application for Population analysis. JABU,Osun state." \o "Digital object identifier".
Ole Boe. (2015). Using LISREL V to perform a covariance structure analysis of a tripartite model of attitude. Procedia - Social and Behavioral Sciences 182, PP:360 – 363.
Parthasarathy, R. (2019). Applied Statistics Manual: A Guide to Improving and Sustaining Quality with Minitab. Quality Progress, 52(8), PP:62-62.
Pourbagher, H., (2015). Open source software R, A tool for data analysis and programming. Journal of Planting Science. Vol.5(2), PP:54-57. (in persian)
Rayat hasanabadi, A., & Mahdavi, M.J., (2013). A critique of the application of statistics in literary research with the introduction of SPSS software. Literary Criticism Quarterly. Vol.21, PP:191-213. (in persian)
Ringle, Christian M., Wende, Sven, Becker, & Jan-Michael. (2015). SmartPLS 3. B¨onningstedt: SmartPLS. Retrieved from.
 Sardareh, Sedigheh Abbasnasab., & Denny T. L. Brown,Paul . (2021). Comparing four contemporary statistical software tools for introductory data science and statistics in the social sciences. An international journal for statistics and data science teaching.
Sarstedt, M., Ringle, C.M., & Hair, J.F. (2017). Partial least squares structural equation modeling. Handb. Mark. Res. Vol 26 (1), PP:1–40.
Sauerbrei, W., C., Meier-Hirmer., A., & Benner., P. Royston. (2006). Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs. Computational Statistics & Data Analysis. Vol 50, PP:3464 – 3485.
Sunil Kumar, A. S., Panwar, Sudhir Kumar, M., Shamim, & Dushyant Mishra. (2018). Statistical Data Analysis Tools: Software Prospects for Crop Productivity. Eco-friendly Agro-biological Techniques for Enhancing Crop Productivity, PP:275-282.
Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, Vol 75, PP:127-135.
Venables WN, Ripley BD. (2013). Modern applied statistics with S-PLUS. Springer New York.
Woods Megan, Trena Paulus, & David P. Atkins. (2016). Advancing Qualitative Research Using Qualitative Data Analysis Software (QDAS)? Reviewing Potential Versus Practice in Published Studies using ATLAS.ti and NVivo, 1994–2013. Article first published online. Vol.34(5), pp:597-617