Creating concise, informative summaries out of huge lists of raw data is a common task and spreadsheets of old have long troubled our weary eyes and tired minds with endless rows and columns of unserviceable data. Digging deep into the data with a standard spreadsheet required knowledge of spreadsheet formulas, math skills, and a tedious mind. The powers that be created the pivot table to replace this unnecessary work, forecasters everywhere rejoiced and were glad.
Data spreadsheets can contain a lot of information and sometimes it may be difficult to obtain summarized information in a simple fashion. Let’s say you’ve compiled a long list of data – for example, sales figures. A pivot table is a spreadsheet tool that gathers cross tabulation data and allows the user to select exclusively desired information from the columns and rows such as individual products or a sales date. Not only will a pivot table highlight desired information, but it can easily “pivot” variables around its axis to gain a different perspective on the data you are working with. The spreadsheet is now in the power of your hands.
The pivot table you will be working with will look and function similarly to the spreadsheet template, but there are many more functions behind those columns and rows. Once data has been populated in the pivot table spreadsheet, it is time to run the data and view the numbers. Sums, averages, standard deviations, and counts are just a few functions the pivot table can produce instantly! Pivot tables make the data seem easy to understand and they can be used within any level of data mining: nominal, ordinal, interval or ratio. The pivot table will become a good friend in helping prepare for reports since its functionality in a spreadsheet allots for more research time, and perhaps, resting those weary eyes and tired minds.
Essentially, a pivot table is a cross-tabulation report – an analysis type that survey research analysts have long known the benefits of. The few differences between the pivot table and a cross-tabulation report are seen in the pivot table’s deficiency of research power. The pivot table establishes an interdependent relationship between the two tables of values but does not identify casual relationships. Also, the pivot table’s power is indicative to the survey size. For example, a 20 question survey with a sample population of 100 can be effectively analyzed with pivot tables within a spreadsheet but when sample population and survey size increases over time or between markets then a more powerful, specialized survey analysis tool is required.