Understanding Qualitative vs Quantitative Variables in Statistics

DB Week #1 In statistics, there are two different types of variables; qualitative and quantitative. Qualitative variables are “non-numeric characteristics” in which an individual is recorded: examples include hair color, name, and marital status (Lind, Marchal, & Wathen, 2019). In contrast, a numerically reported variable is quantitative and can be written in charts and graphs (Lind, Marchal, & Wathen, 2019). Examples of quantitative variables are balancing a checking account, a car battery life, and a high school GPA (Lind, Marchal, & Wathen, 2019). Quantitative variables have two subcategories, discrete and continuous. Qualitative variables are subjective and unstructured as opposed to the structured and conclusive quantitative variables. The real estate information provided shows examples of both qualitative and quantitative variables. The price of the real estate, as well as the square feet, distance, and bath(s), are all measured quantitative variables. The ratio level of measurement is the “meaningful 0 point and ratio between values” (Lind, Marchal, & Wathen, 2019, p. 11), which includes all of the above variables. The qualitative variables listed in the example include the township, garage, and pool, which are measured nominally. The nominal level of measurement is “observations of a qualitative variable” that “are measured and recorded as labels or names” (Lind, Marchal, & Wathen, 2019, p. 7). The presence or absence of qualitative variables (pool, township, garage) provides essential information for real estate owners (Rout & Shariff, 1999). Both qualitative and quantitative data can be used to determine which variables increase/decrease sales. Along with real estate, statistics are used in everyday life for insurance, sports, and the stock market. Statistics is especially important for researchers today who are studying Covid-19 and finding ways to prevent the disease's spread. Information discovered through statistics

includes how many people have conducted the virus, what state they are in, and the individuals' age. References Lind, D., Marchal, W., & Wathen, S. (2019). Basic statistics for business & economics. McGraw-Hill Education. Rout, P., & Shariff, S. (1999, May). Diagnostic value of qualitative and quantitative variables in thyroid lesions. Cytopathology, 10 (3), 171-179. https://onlinelibrary-wiley- com.ezproxy.regent.edu/doi/full/10.1046/j.1365-2303.1999.00092.x

Hello Bailey, I appreciated your example that displayed the relationship between both qualitative and quantitative variables. You brought to light statistics in everyday life, and I think that it is highly important for social research and understanding. One example of this importance of statistics can be seen in the data discovered on motorcycle crashes. In 2017, motorcyclist fatalities hit a total of 5,172, and 33% of which involved alcohol-impairment (Insurance Information Institute, 2020). This statistical research could then influence DMV’s to provide more information or highlight the importance of the dangers of riding while impaired by alcohol to motorcyclists. Statistics are essential for the public because policies and procedures can be adjusted or created according to research. References Insurance Information Institute. (2020). Facts + statistics: Motorcycle crashes. Insurance Information Institute. https://www.iii.org/fact-statistic/facts-statistics-motorcycle-crashes

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Hey Natasha! Your post was very informative. It is important to highlight the different types of qualitative and quantitative variables: nominal, ordinal, ratio, and interval. According to Biostatistics for the Clinician (n.d.), the important aspect of nominal variables is that “there is no measurement of distance between the values” (para. 7). Nominal and ordinal, which can be raked or ordered to some degree, fall under qualitative measurement (Biostatistics for the Clinician, n.d.). On the other hand, ratio and interval variables are both quantitative forms of measurement. The interval variable “represents something real” (para. 10); an example of this is the difference between temperatures (Biostatistics for the Clinician, n.d.). Ratio variables contain an absolute zero point, such as a person’s age (Biostatistics for the Clinician, n.d.). Each of these provides the researcher with a different amount of detail with ratio providing the most. I hope this information, along with the article I provided, is beneficial to you! References Biostatistics for the Clinician. (n.d.). Variables and measurements. UTHealth. https://www.uth.tmc.edu/uth_orgs/educ_dev/oser/L1_2.HTM

DB Week #1

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