I wanted to provide a quick update on where we are on the Retirement Income Style Awareness ™ (RISA) investigation.
There was a pause in the action after the deadline because I took my summer break and we moved our offices in Virginia- we are still in McLean (only a mile from our previous place). Plus, the team was spent putting the launch together and we all benefitted from a brief respite.
When the dust settled, we began the data analysis. The participation rate was fantastic. The total sample size was about 1,400 people with a little over 1,000 full completions. Thank you!
Highlights from the Data
Male: 1,143 Female: 335
Married: 1,270 Single: 208
Retirement Cohort (top 3)
Less than 5yrs retired: 470
More than 10 yrs FROM retirement: 425
More than 5 yrs from retirement: 171
Age Range (top 3)
This sample is quite large and will allow us to do all of our intended analysis with quite a bit of rigor. In addition, we will be able to do split-sample testing which will further increase the confidence we have in our findings.
Over the last two weeks, we have been running what is known as exploratory factor analysis (EFA). What we effectively do here is put all of our questions into one big hopper, shake up the dataset (very technical term 😉), and see if the EFA is able to identify clusters of questions that match our hypothesized factors.
The clusters indicate that the questions within the cluster are identifying a ‘latent” factor. If the questions cluster based on how we envisioned they would (by our hypothesized factor-like Safety-First), then we can actually say that we are creating a tool that is able to identify the underlying. Exciting times.
Frankly, many assumptions in our field do not reach this level of analysis. Instead, practitioners end up talking about matters of their opinion like if they were matters of fact.
To avoid the overwhelm, I will discuss what our findings are indicating in the next entry.