The first step of analyzing this data set includes the determining of the scales of measurement (nominal, ordinal, ratio, & interval) for each given variable. Nominal measurements, as defined in the textbook, are labels/names used for the identification of an element. With the data presented in this case one can conclude X4 (First Generation Student), X5 (Gender), X7 (Ethnicity), and X8 (Personality Type) to be nominal measurements. Although these variables are identified in the data set as a number they are still considered nominal in that those numbers denote a category. First Generation Students are simple yes (1) or no (2) variables denoted by 1 and 2 as the rest of the variables follow suite with the same format. Ordinal measurements are similar in definition to nominal measurements with an added characteristic of rank or order within the category. With the data presented in this case one can conclude that X1 (Computer Knowledge) and X3 (Classification) are ordinal measurements. The X1 ranks the computer knowledge of students from very poor to excellent while the X3 variable ranks their classification as freshman, sophomore, junior, etc. The interval scale of measurement is defined in the text as an ordinal measurement with the added characteristic of a fixed unit of measurement. I did not find any variables in this data set to be part of an interval scale of measurement. Ratio measurements are defined as that of an interval; in addition, it has two values that are meaningful. With the data presented in this data set X2 (Internet Usage), X6 (Age), and Y are all ratio measurements. This can be confirmed by the fact that if any of these variables yielded a zero it would denote that nothing exists.
The second step of analyzing this data set is to classify the data as either qualitative or quantitative. Qualitative data is categorical and can be either nominal or