Smart strategy in statistics: choosing appropriate test tools for data and hypotheses in quantitative research

Jurnal Penelitian Pendidikan Indonesia July 15, 2024 Volume 10, Number 2 4 authors

Abstract

The selection of appropriate statistical test tools is a major challenge in quantitative research, and errors in their selection can affect the validity and reliability of research results. This study aims to develop a systematic strategy for selecting appropriate statistical test tools based on the type of data and hypotheses used, and provide practical guidance for researchers of various skill levels. The research method used is descriptive research with a qualitative approach, where data is collected through literature studies and case studies on various quantitative studies. The selected test tools were analyzed using content analysis techniques to identify the match with the data characteristics. The results showed that the selection of appropriate test tools improved the accuracy and efficiency of statistical analyses, and the strategy applied helped make it easier for researchers to determine the appropriate statistical test tools through clear groupings based on data types and hypotheses. The implication of this research is the importance of in-depth understanding of the basic assumptions of test tools as well as the application of this strategy to improve the quality and credibility of quantitative research in various disciplines.

Authors

  1. Nana Darna

    Universitas Galuh

  2. Iwan Setiawan

    Universitas Galuh

  3. M. Aziz Basari

    Universitas Galuh

  4. Kasman Kasman

    Universitas Galuh

Keywords

Statistical Testing ToolsData TypesHypothesis TypesQuantitative

References

  1. Adams, K. A., & McGuire, E. K. (2022). Research methods, statistics, and applications. Sage Publications.
  2. Alem, D. D. (2020). An overview of data analysis and interpretations in research. International Journal of Academic Research in Education and Review, 8(1), 1–27.
  3. Alnaimat, F., Al-Halaseh, S., & AlSamhori, A. R. F. (2024). Evolution of Research Reporting Standards: Adapting to the Influence of Artificial Intelligence, Statistics Software, and Writing Tools. Journal of Korean Medical Science, 39(32). https://doi.org/DOI: https://doi.org/10.3346/jkms.2024.39.e231
  4. Balling, L. W., & Hvelplund, K. T. (2015). Design and statistics in quantitative translation (process) research. Translation Spaces, 4(1), 170–187. https://doi.org/https://doi.org/10.1075/ts.4.1.08bal
  5. Bishara, A. J., & Hittner, J. B. (2017). Confidence intervals for correlations when data are not normal. Behavior Research Methods, 49, 294–309. https://doi.org/https://doi.org/10.3758/s13428-016-0702-8
  6. Cooksey, R. W. (2020). Illustrating statistical procedures: Finding meaning in quantitative data. Springer Nature. https://books.google.co.id/books?hl=id&lr=&id=udTkDwAAQBAJ&oi=fnd&pg=PR6&dq=This+confusion+can+be+caused+by+a+lack+of+understanding+of+the+assumptions+underlying+each+statistical+method,+limited+access+to+statistical+training,+or+the+complexity+of+the+data+itself.&ots=RJvBm7kGRt&sig=I8oAetIdxZzNrDqbzsmYdsQVfz4&redir_esc=y#v=onepage&q&f=false
  7. Darna, N., & Herlina, E. (2018). Memilih metode penelitian yang tepat: bagi penelitian bidang ilmu manajemen. Jurnal Ekonologi Ilmu Manajemen, 5(1), 287–292. https://jurnal.unigal.ac.id/ekonologi/article/view/1359
  8. Emmert-Streib, F., & Dehmer, M. (2019). Understanding statistical hypothesis testing: The logic of statistical inference. Machine Learning and Knowledge Extraction, 1(3), 945–962. https://doi.org/https://doi.org/10.3390/make1030054
  9. Frias‐Navarro, D., Pascual‐Llobell, J., Pascual‐Soler, M., Perezgonzalez, J., & Berrios‐Riquelme, J. (2020). Replication crisis or an opportunity to improve scientific production? European Journal of Education, 55(4), 618–631. https://doi.org/https://doi.org/10.1111/ejed.12417
  10. Goertzen, M. J. (2017). Introduction to quantitative research and data. Library Technology Reports, 53(4), 12–18. https://journals.ala.org/index.php/ltr/article/view/6325/8274
  11. Greenland, S. (2017). For and against methodologies: some perspectives on recent causal and statistical inference debates. European Journal of Epidemiology, 32, 3–20. https://doi.org/https://doi.org/10.1007/s10654-017-0230-6
  12. 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
  13. Hahs-Vaughn, D. L., & Lomax, R. (2020). An introduction to statistical concepts. Routledge. https://doi.org/10.4324/9781315624358
  14. Jamieson, M. K., Govaart, G. H., & Pownall, M. (2023). Reflexivity in quantitative research: A rationale and beginner’s guide. Social and Personality Psychology Compass, 17(4), e12735. https://doi.org/https://doi.org/10.1111/spc3.12735
  15. Karunarathna, I., Gunasena, P., Hapuarachchi, T., & Gunathilake, S. (2024). The crucial role of data collection in research: Techniques, challenges, and best practices. Uva Clinical Research, 1–24. https://www.researchgate.net/profile/Indunil-Karunarathna/publication/383155720_The_Crucial_Role_of_Data_Collection_in_Research_Techniques_Challenges_and_Best_Practices/links/66bef1c6311cbb09493d6200/The-Crucial-Role-of-Data-Collection-in-Research-Techniques-Challenges-and-Best-Practices.pdf
  16. Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated algorithm selection: Survey and perspectives. Evolutionary Computation, 27(1), 3–45. https://doi.org/10.1162/evco_a_00242
  17. Kim, J., Ryu, J. W., Shin, H.-J., & Song, J.-H. (2017). Machine learning frameworks for automated software testing tools: a study. International Journal of Contents, 13(1), 38–44. https://doi.org/https://doi.org/10.5392/IJoC.2017.13.1.038
  18. Kotronoulas, G., Miguel, S., Dowling, M., Fernández-Ortega, P., Colomer-Lahiguera, S., Bağçivan, G., Pape, E., Drury, A., Semple, C., & Dieperink, K. B. (2023). An overview of the fundamentals of data management, analysis, and interpretation in quantitative research. Seminars in Oncology Nursing, 39(2), 151398. https://doi.org/https://doi.org/10.1016/j.soncn.2023.151398
  19. Kotronoulas, G., & Papadopoulou, C. (2023). A primer to experimental and nonexperimental quantitative research: the example case of tobacco-related mouth cancer. Seminars in Oncology Nursing, 39(2), 151396. https://doi.org/https://doi.org/10.1016/j.soncn.2023.151396
  20. Kula, F., & Koçer, R. G. (2020). Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework. Teaching Mathematics and Its Applications: An International Journal of the IMA, 39(4), 248–265. https://doi.org/https://doi.org/10.1093/teamat/hrz014
  21. Lai, P. C. (2018). Research methodology for novelty technology. JISTEM-Journal of Information Systems and Technology Management, 15, e201815010. https://doi.org/https://doi.org/10.4301/S1807-1775201815010
  22. Luo, G. (2016). A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics, 5, 1–16. https://doi.org/https://doi.org/10.1007/s13721-016-0125-6
  23. Makar, K., & Rubin, A. (2018). Learning About Statistical Inference BT - International Handbook of Research in Statistics Education (D. Ben-Zvi, K. Makar, & J. Garfield (eds.); pp. 261–294). Springer International Publishing. https://doi.org/10.1007/978-3-319-66195-7_8
  24. Mohammed, S., Rubarth, K., Piper, S. K., Schiefenhövel, F., Freytag, J.-C., Balzer, F., & Boie, S. (2023). A statistical method for predicting quantitative variables in association rule mining. Information Systems, 118, 102253. https://doi.org/https://doi.org/10.1016/j.is.2023.102253
  25. Pandey, P., & Pandey, M. M. (2021). Research methodology tools and techniques. Bridge Center. http://dspace.vnbrims.org:13000/jspui/bitstream/123456789/4666/1/Research Methodology Tools And Techniques.pdf
  26. Reddy, D., & Pulluru, K. (2024). Principles Of Statistics & Research Methodology. Academic Guru Publishing House. https://books.google.co.id/books?hl=id&lr=&id=sg8MEQAAQBAJ&oi=fnd&pg=PA1&dq=Inferential+statistics+or+inductive+statistics+is+a+tool+for+collecting+data,+managing+data,+drawing+errors,+and+taking+actions+based+on+sample+data,+and+the+results+are+utilized+or+generalized+for+the+population+&ots=gQd6oNt6Ji&sig=7a2sW_ZoUXvgbJPmOor2bAc0FfQ&redir_esc=y#v=onepage&q&f=false
  27. Saharan, V. A., Kulhari, H., Jadhav, H., Pooja, D., Banerjee, S., & Singh, A. (2020). Introduction to research methodology. In Principles of Research Methodology and Ethics in Pharmaceutical Sciences (pp. 1–46). CRC Press. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003088226-1/introduction-research-methodology-vikas-anand-saharan-hitesh-kulhari-hemant-jadhav-deep-pooja-surojit-banerjee-anupama-singh
  28. Saylors, R., & Trafimow, D. (2021). Why the increasing use of complex causal models is a problem: On the danger sophisticated theoretical narratives pose to truth. Organizational Research Methods, 24(3), 616–629. https://doi.org/https://doi.org/10.1177/1094428119893452
  29. Verma, J. P., & Abdel-Salam, A.-S. G. (2019). Testing statistical assumptions in research. John Wiley & Sons. https://books.google.co.id/books?hl=id&lr=&id=BuSLDwAAQBAJ&oi=fnd&pg=PP2&dq=ensuring+data+meets+the+assumptions+of+the+test+tool+used+to+produce+valid+results,+quantitative&ots=SwXSa5kP6O&sig=RCGgw3WJc3ZQ6Ns9pHw534oby8Y&redir_esc=y#v=onepage&q&f=false
  30. Watson, R. (2015). Quantitative research. Nursing Standard, 29(31).