Data Types

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Qualitative and Quantitative Data:

Education schools research and analyze two main types of data: qualitative and quantitative. Scholars typically define their methodology by either of these categories or as a mixed method combining both. Qualitative data tells a story. Such data are non-numerical in nature. They also have a low level of measurability. Qualitative data can include language or opinions. For example, in education, classroom observations or interviews with parents of students regarding family history are qualitative. Quantitative data also tells a story but in a different way. Quantitative data are expressed numerically and possess a high degree of measurability. For instance, quantitative data includes test scores, class rank, and school student populations.

Nominal and Ordinal Data:

Qualitative data can be further subdivided into nominal and ordinal categories. The nominal category labels qualitative data without any inherent rank or order. This would include categorizing students in terms of nationality or gender as well nonbinary pronouns. Ordinal data does possess an innate sense of rank but needs to be converted numerically for quantitative analysis and hence are categorized as qualitative. For example, credit/no-credit grading, class rank, and level of educational attainment are ordinal data.

Discrete and Continuous Data:

Quantitative data is composed of two categories, including discrete and continuous. Discrete data can be measured and counted as finite values assigning to whole numbers or integers that cannot be decomposed into fractions or decimals. Examples are student attendance or number of in-service professional development days for faculty. Continuous data entails information that can be subdivided and assume a value within a range. Test-taking time or GPA are examples of continuous data.