Final answer:
Qualitative data, which is descriptive in nature, can be reduced to numbers through coding but this process can oversimplify the data's richness. Quantitative data, on the other hand, is inherently numerical and comes as either discrete or continuous data, suited for statistical analysis.
Step-by-step explanation:
Qualitative data refers to information that is descriptive and characterizes rather than measures. Examples include eye color, type of cuisine preferred, or a person's favorite song. In contrast, quantitative data are numerical and can be counted or measured, such as height, temperature, or the number of books in a backpack, and come in two types: discrete and continuous. Discrete data are countable in a finite amount of values - like the number of cookies in a jar. Continuous data represent measurements on a continuous scale, such as temperature or time.
Reducing qualitative data to numbers is not straightforward because it involves categorizing non-numerical information. However, it is possible to assign numbers to qualitative data for analysis through methods such as coding. For instance, if we are interested in hair color, we could assign a number to each color: 1 for black, 2 for brown, and so on. Yet this does not 'measure' hair color in the true sense; it merely allows us to count instances within categories.
Researchers often convert qualitative data into a quantitative format to apply statistical methods. Using surveys with multiple-choice questions can yield quantitative-type data from qualitative questions because responses are limited and can be translated into numerical counts. For example, the number of people who select 'yes' or 'no' on a questionnaire can be counted and analyzed statistically.
However, this transformation can oversimplify the richness of qualitative information. While qualitative data can often be coded and quantified for certain types of analysis, such as frequency counts, the complexity of the data is frequently reduced in the process.