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Chi-Squared Test Where Continuous Data Has Been Transformed Into Categorical Data
Using Continuous data as categorical
Statistical analysis can provide a robust approach to assess data, using the raw data to generate useful results which may provide insights into the phenomena being examined. For the results to be meaningful and useful it is essential that the most appropriate method tests are utilized (Bryman & Bell, 2011). In the research undertaken by Razmjou et al., (2009) a decision way made to transform one type of variable, changing it from a continuous variable into a categorical variable. BMI, which is a continuous variable, was changed into categories of normal, overweight, and obese (Razmjou et al., 2009). Usually, transforming continuous data into categorical data results in a loss of detail, so is usually ill-advised, but in some circumstances may help to more useful data (Bryman & Bell, 2011). The circumstances under which continuous variables may benefit from being transformed into categorical variables include conditions under which there may be nonlinearity, for example the presence of a quadratic or exponential curve upwards or downwards, or when you may be a floor threshold effect, or ceiling effect (Bruce, 2009).
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Therefore nonlinearity is a strong enough reason for the potential loss of data resulting from the classification process. However, while BMI may be continuous, but it does have upper and lower limits of thresholds, and therefore may benefit from categorisation as the results may not be monotonic (Bruce, 2009).
Furthermore, it maybe argued that the division of the sample into the three categories reflects the standard categories which are utilised within the medical profession, with the various categories also having other associated characteristics. Effectively, by dividing the data into these common use categories, the results may be classified in a manner which would be most useful to medical professionals who are using it, with the ability to categorise individuals based on predetermined groups, rather than looking up…
References
Bruce, N. (2009). Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics. Hoboken: Wiley-Interscience.
Bryman, A., & Bell, E. (2011). Business Research Methods. Oxford: Oxford University Press.
Curwin, J., & Slater, R. (2006). Quantitative Methods for Business Decisions. London: Thompson Business Press.
Razmjou, H; Davis, A M, Jaglal, S B; Holtby, R; Richards, R, (2009), Cross-sectional analysis of baseline differences of candidates for rotator cuff surgery: a sex and gender perspective, BMC Musculoskeletal Disorders, 10(26), retrieved 17th November 2015 from http://www.biomedcentral.com/1471-2474/10/26
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