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S01:E06
This podcast examines the fundamentals of data in research, distinguishing between raw data (unprocessed facts and observations) and information (processed, organized data with context). The hosts explore the two main types of data—qualitative (categorical) and quantitative (numerical)—and their various subtypes including nominal, ordinal, interval, and ratio data.
The conversation covers different rating scales used in research such as binary, Likert, semantic differential, and Guttman scales, explaining how each captures different aspects of people’s opinions or behaviors. The hosts discuss the normal distribution (bell curve) and ways data can deviate from normality through skewness and kurtosis.
The episode addresses practical aspects of working with data including database management, dealing with “dirty” or missing data, accessing public databases, choosing appropriate statistical tests, and using data mining techniques. The hosts use everyday examples like a bakery owner considering delivery options to illustrate abstract concepts in an accessible way.
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