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Data Analysis in Quantitative Research

  • Yong Moon JungEmail author
Reference work entry

Abstract

Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility. Conducting quantitative data analysis requires a prerequisite understanding of the statistical knowledge and skills. It also requires rigor in the choice of appropriate analysis model and the interpretation of the analysis outcomes. Basically, the choice of appropriate analysis techniques is determined by the type of research question and the nature of the data. In addition, different analysis techniques require different assumptions of data. This chapter provides introductory guides for readers to assist them with their informed decision-making in choosing the correct analysis models. To this end, it begins with discussion of the levels of measure: nominal, ordinal, and scale. Some commonly used analysis techniques in univariate, bivariate, and multivariate data analysis are presented for practical examples. Example analysis outcomes are produced by the use of SPSS (Statistical Package for Social Sciences).

Keywords

Quantitative data analysis Levels of measurement Choice of analysis model SPSS 

References

  1. Armstrong JS. Significance tests harm progress in forecasting. Int J Forecast. 2007;23(2):321–7.CrossRefGoogle Scholar
  2. Babbie E. The practice of social research. 14th ed. Belmont: Cengage Learning; 2016.Google Scholar
  3. Brockopp DY, Hastings-Tolsma MT. Fundamentals of nursing research. Boston: Jones & Bartlett; 2003.Google Scholar
  4. Creswell JW. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks: Sage; 2014.Google Scholar
  5. Fawcett J. The relationship of theory and research. Philadelphia: F. A. Davis; 1999.Google Scholar
  6. Field A. Discovering statistics using IBM SPSS statistics. London: Sage; 2013.Google Scholar
  7. Grove SK, Gray JR, Burns N. Understanding nursing research: building an evidence-based practice. 6th ed. St. Louis: Elsevier Saunders; 2015.Google Scholar
  8. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RD. Multivariate data analysis. Upper Saddle River: Pearson Prentice Hall; 2006.Google Scholar
  9. Katz MH. Multivariable analysis: a practical guide for clinicians. Cambridge: Cambridge University Press; 2006.CrossRefGoogle Scholar
  10. McHugh ML. Scientific inquiry. J Specialists Pediatr Nurs. 2007;8(1):35–7. Volume 8, Issue 1, Version of Record online: 22 FEB 2007CrossRefGoogle Scholar
  11. Pallant J. SPSS survival manual: a step by step guide to data analysis using IBM SPSS. Sydney: Allen & Unwin; 2016.Google Scholar
  12. Polit DF, Beck CT. Nursing research: principles and methods. Philadelphia: Lippincott Williams & Wilkins; 2004.Google Scholar
  13. Trochim WMK, Donnelly JP. Research methods knowledge base. 3rd ed. Mason: Thomson Custom Publishing; 2007.Google Scholar
  14. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Boston: Pearson Education.Google Scholar
  15. Wells CS, Hin JM. Dealing with assumptions underlying statistical tests. Psychol Sch. 2007;44(5):495–502.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Centre for Business and Social InnovationUniversity of Technology SydneyUltimoAustralia

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