Data Structures

Data can come to you in several different forms, and it will be useful to have a basic catalog of the different kinds of data so that you can recognize them and use appropriate techniques for each. A data set consists of observations on items, typically with the same information being recorded for each item. We define the elementary units as the items themselves (eg, companies, people, households, cities, TV sets) to distinguish them from the measurement or observation (eg, sales, weight, income, population, size). This chapter shows that data sets can be classified in five basic ways: One: By the number of pieces of information (variables) there are for each elementary unit. Univariate data have just one variable, bivariate data have two variables (eg, cost and number produced), and multivariate data have three or more variables. Two: By the kind of measurement (numbers or categories) recorded in each case. Quantitative data consist of meaningful numbers, whereas categorical data are categories that might be ordered (“ordinal data”) or not (“nominal data”). Three: By whether or not the time sequence of recording is relevant. Time-series data are more complex to analyze than are cross-sectional data because of the way in which measurements change over time. Four: By whether or not the information was newly created or had previously been created by others for their own purposes. If you (or your firm) controls the data-gathering process, the result is called primary data, whereas data produced by others is “secondary data.” Five: By whether the data were merely observed (an “observational study”) or if some variables were manipulated or controlled (an “experiment”). Advantages of an experiment include the ability to assess what is causing the reaction of interest.