R and Python Software
Daniel J. Denis · 2025
The rise of computers and computing power has revolutionized statistics and data analysis since at least the early 1980s, if not before. What used to take statisticians and scientists days to compute now takes, quite literally, milliseconds . For instance, computing a factor analysis in the 1930s took several hours , not seconds. Today, one can press a few button clicks (e.g., using SPSS, for instance) and obtain factor-analytic output in less than a second. While this obviously has advantages, it also comes with it the potential peril of possibly overlooking important details prior to conducting data analysis, such as the integrity of data, good theorizing, etc., at least as it pertains to scientific fields. Computing can be done so quickly now that it can be all too easy to pay insufficient attention to what is being computed and what is being measured . In business and healthcare analytics, for instance, the power of software has made it possible to analyze humongous databases and see patterns in data that were not possible before advanced computation and visualization. A business analyst can analyze terabytes of data relatively quickly to see emerging trends and advise management where to allocate marketing resources to maximize profits. As emphasized at the outset of this book, however, none of this awesome ability precludes the importance of ensuring the proper measurement and quality of data. Analyzing and concluding patterns from data only makes good sense if the data itself is of good quality and is tapping into the sought-after constructs. Poor measurement, or otherwise sketchy data can only mislead. Do people approve of your product? A rating scale of 1 through 5 suggests most folks may choose 4 or 5. However, that limited data does not tell you whether they approve of other products a lot more than yours. For instance, perhaps your product is at the lowest preference point of competing products, but you would never know it by simply asking a single question of how much people approve of your product. Hence, knowing what you are measuring and putting it into proper context is just as, if not more, important than before “big data” arrived on the scene. Big data equals big conclusions, which leads to big decisions, which may be faulty if not carefully considered because of lightning-fast computing speed and facility.