Handbook of Research Methods in Health Social Sciences pp 1–15 Cite as
Data Analysis in Quantitative Research
- Yong Moon Jung 2
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- First Online: 28 December 2017
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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).
- Quantitative data analysis
- Levels of measurement
- Choice of analysis model
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Armstrong JS. Significance tests harm progress in forecasting. Int J Forecast. 2007;23(2):321–7.
Article Google Scholar
Babbie E. The practice of social research. 14th ed. Belmont: Cengage Learning; 2016.
Google Scholar
Brockopp DY, Hastings-Tolsma MT. Fundamentals of nursing research. Boston: Jones & Bartlett; 2003.
Creswell JW. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks: Sage; 2014.
Fawcett J. The relationship of theory and research. Philadelphia: F. A. Davis; 1999.
Field A. Discovering statistics using IBM SPSS statistics. London: Sage; 2013.
Grove SK, Gray JR, Burns N. Understanding nursing research: building an evidence-based practice. 6th ed. St. Louis: Elsevier Saunders; 2015.
Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RD. Multivariate data analysis. Upper Saddle River: Pearson Prentice Hall; 2006.
Katz MH. Multivariable analysis: a practical guide for clinicians. Cambridge: Cambridge University Press; 2006.
Book Google Scholar
McHugh ML. Scientific inquiry. J Specialists Pediatr Nurs. 2007; 8 (1):35–7. Volume 8, Issue 1, Version of Record online: 22 FEB 2007
Pallant J. SPSS survival manual: a step by step guide to data analysis using IBM SPSS. Sydney: Allen & Unwin; 2016.
Polit DF, Beck CT. Nursing research: principles and methods. Philadelphia: Lippincott Williams & Wilkins; 2004.
Trochim WMK, Donnelly JP. Research methods knowledge base. 3rd ed. Mason: Thomson Custom Publishing; 2007.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Boston: Pearson Education.
Wells CS, Hin JM. Dealing with assumptions underlying statistical tests. Psychol Sch. 2007;44(5):495–502.
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Jung, Y.M. (2018). Data Analysis in Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences . Springer, Singapore. https://doi.org/10.1007/978-981-10-2779-6_109-1
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DOI : https://doi.org/10.1007/978-981-10-2779-6_109-1
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IMAGES
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Data analysis is simply the process of converting the gathered data to meanin gf ul information. Different techniques such as modeling to reach trends, relatio nships, and therefore conclusions to ...
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There are a number of factors to be considered. Basically, the choice of analysis model in quantitative data analysis is determined by (1) the nature of the variable or the level of measurement of the variable to be analyzed, (2) the types of research question, and (3) the types of analysis. 4 Y. M. Jung.
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features with research on youth conflict and on being homeless. You will quickly learn that there is no one way to analyze textual data. To quote Michael Quinn Patton (2002), "Qualitative analysis transforms data ... Qualitative data analysis is an iterative and reflexive process that begins as data are being collected rather . than after ...
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