Stats text 12

12.2 A variables analysis approach to open data exploration

Another way to tackle analysis of the data is to explore the number and nature of the variables being presented. How many variables? What level of measurement? In introductory statistics one is usually either exploring basic statistics, running correlations, or comparing means.

Data is often organized into tables. In statistics columns are often variables while rows are individual data values. This is not always true, but in introductory statistics this is almost always true. If there is a single data column, then there is one variable. If there are two data columns, then there are two variables. The variable name and the units, if any, are usually listed in the first row of the table.

What can be analyzed, what can be done, depends in part on how many variables are present and the level of measurement. The following chart is for ratio level data. Note that basic statistics can be calculated for any ratio level variable. Remember that columns are variables.

There is a caveat in using this approach, one best captured by the article Ten Simple Rules for Effective Statistical Practice cited above:

While it is obvious that experiments generate data to answer scientific questions, inexperienced users of statistics tend to take for granted the link between data and scientific issues and, as a result, may jump directly to a technique based on data structure rather than scientific goal. For example, if the data were in a table, as for microarray gene expression data, they might look for a method by asking, “Which test should I use?” while a more experienced person would, instead, start with the underlying question, such as, “Where are the differentiated genes?” and, from there, would consider multiple ways the data might provide answers. Perhaps a formal statistical test would be useful, but other approaches might be applied as alternatives, such as heat maps or clustering techniques.

With that in mind, for the student in an introductory statistics course where the objective is to practice statistical operations, an data structures approach is arguably appropriate. The data structures do sometimes provide information on what can be done with the data.