According to Plichta and Kelvin (2013), “Research design is the art and science of conducting studies that maximize reliability and validity of the findings and minimize bias or error” (p. 12). Just as in art, a seemingly minor error can affect an entire design. Your research design determines the type of variables you use to develop your data analysis plan, as well as the method to assess the relationship between them. As you may remember from your previous statistical courses, variables can be divided into categorical or continuous. Yet, there are additional ways to define data known as levels of measurement or measurement scales. The categorical variables can be defined as nominal, dichotomous, or ordinal, whereas the continuous variables are defined as interval or ratio. Each level of measurement has its own properties, strengths, and limitations for grouping (Plichta & Kelvin, 2013). The levels of measurement that you select will affect the type of statistical testing method and, ultimately, the research study design.
For this week’s Discussion, select two levels of measurements for comparison and secondary data analysis decisions. Consider how each level of measurement may impact your secondary data analysis decision. Post
a comparison of two different levels of measurement. Then, explain how each level of measurement relates to secondary data analysis decisions. Provide one example of when each level would be appropriate. Support your response with current literature.