What are index values?

Index values are a way of quantifying relative proportionality around a mean of 100. Index values have been used for centuries as a means of comparing values to a base condition.  One of the most common indices that most people would be familiar with is the Consumer Price Index which measures the price of commonly purchased items relative to a base year to determine the inflation rate.  Regardless of what is being measured, the base condition is always set to 100 – so, comparative values are either increasing (i.e. >100) or decreasing (i.e. <100).  The reason that we – and other persons concerned with data analysis – use index values is that they allow us to quickly extrapolate the difference between a value and a base condition.  For instance, a value of 110 is quickly recognized as being 10% greater than the base condition. Here’s an example of data in index value.

How does one derive an index value?

The math behind calculating an index is very straightforward.  First, you establish the base condition – this might be something as simple as the first year of operation for an analysis that compares performance since opening.  Then, you divide the subject value (i.e. the value that you are comparing to the base condition) by the base condition, and then you multiply this result by 100.  For example, if you were comparing year-over-year sales, and your first-year sales (i.e. the base condition) totaled $20,000, and second year sales totaled $25,000, you would divide $25,000 by $20,000 and multiply the result of 1.25 by 100 to create an index value of 125 for the second year’s sales.  Thus, the first year’s sales of $20,000 would equal 100, and the second year’s sales of $25,000 would equal 125.  The important thing to remember is that index values are used to ascribe a sense of relativity to numbers for the purpose of analysis and comparison.

What are scalar variables?

A scalar variable is a proposition that seeks to quantify the relativity of a response within a continuum.  These types of propositions inform degrees of agreement with certain statements (e.g. “On a scale of 1-10 with “1” being completely disagree and “10” being completely agree…) Additionally, scalar variables inform the development of Structural Equation Models.  Structural equation modeling is a means of assessing latent constructs – factors that are not observable and, therefore, less readily measured. In general, the scales utilized within the research processes developed by IMPACTS seek to maximize the variability of responses; thus, the scale deployed within the proposed qualitative and quantitative survey instruments typically range “1-100” (as opposed to the more traditional “1-5” or “1-10”). Here’s an example of a chart using scalar variables.

Can I get a copy of the NAAU?

Hold up! If you’re imaging that the NAAU is a traditional questionnaire or a one-after-another question survey, you’re mistaken. The NAAU collects a multiplicity of data types including demographic information, scalar variables, and open-ended queries. As is the case with most real-time, big data processes, the NAAU lives on servers so that it can be consistently updated, queried, organized, and referenced in meaningful ways. Printing the NAAU would result in tens of thousands of pages of uncategorized data. I work with the NAAU team to present the data and accompanying analysis in a way that is (hopefully) broadly comprehensible and actionable. 

May I request a data cut for my organization’s audiences, please?

You bet! Head over to my “Talks & Workshops” page to learn more about scheduling a visit to your organization, conference, or visitor-serving community to share-out information to help make strategic decisions.