Data Analysis in Quantitative Research
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- Yong Moon Jung 2
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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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Centre for Business and Social Innovation, University of Technology Sydney, Ultimo, NSW, Australia
Yong Moon Jung
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Correspondence to Yong Moon Jung .
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School of Science and Health, Western Sydney University, Penrith, NSW, Australia
Pranee Liamputtong
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Jung, Y.M. (2019). Data Analysis in Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_109
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DOI : https://doi.org/10.1007/978-981-10-5251-4_109
Published : 13 January 2019
Publisher Name : Springer, Singapore
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Online ISBN : 978-981-10-5251-4
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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.