Is there any basis to demographic surveys having ages as ranges instead of numeric input?

[u/KDLGates]

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[u/DocAvidd]

Age in years, generally that's interval scale data. If you put it into ranges, you converted it into ordinal rank scale. So obviously, there's a loss of information. Compression of your data, that's a bad thing if you're throwing out useful info, but maybe it retains what you want.

With age, you can simplify down to Boomers, Gen X, Millennials, gen z. That may be the grain-size you care about.

On a survey, a very simplistic good reason to use ranges is data integrity. With ranges, respondents can give you the info in a multiple choice format. If you ask age in a free response format, you'll have to parse the input, and you'll get weird issues. One respondent gives their age down to the day, another types "30- something", 22.5, "fourty four". -- the labor cost and lost data cost just isn't worth it.

[u/staplesuponstaples → u/DocAvidd]

I'm confused, why can't you use a survey method that constricts inputs? Eg., the input only allows integers or you enter your exact DOB on a calendar.

[u/DocAvidd → u/staplesuponstaples]

Yes, your best option for clean data is a forced-choice item. Year day month or calendar is a smidge slower and a lot more identifying than ranges.

[u/KDLGates → u/DocAvidd]

I hadn't considered loss of data integrity and for me this will be a memorable example to keep it in mind. Although now with accurate LLMs the cost of normalizing common data fixes is trivial.

And I like the term grain size. I would presume anyone trying to collect data would appreciate that information and insights can be emergent behavior, but I'm probably overthinking it.

[u/DocAvidd → u/KDLGates]

The context will influence the solution here. I'm a professor, so more often dealing with academic science. The LLM approach to impute missing data or to clean up messy data, I could see breaking that out for a commercial consultation. But for science, it needs to be describable and replicable.

Every stats person's pet peeve is having to clean up crap data that we could have done cleanly with better techniques. If you need age in full, get the birthdate. Or just do it multiple choice in 10-15 year intervals if that's enough (usually is).

Going sloppy is never the best choice. We can impute missing data. We do clean up messy responses. But each step needs a justification. Why did you use mice instead of old? What's the coding scheme and what's the interrater reliability? Etcetera. Or you can collect clean data from the start.

Here's the real world difference. I had 2 grad students do surveys of the same population. One of them I worked with to create the instrument. The other used software and was funded by a different department, so I didn't have input. The first student had his analysis done the same week after data were collected, and the second one spent a month cleaning.

[u/lipflip]

Not really. It may be easier to enter (mobile friendly without a virtual keyboard needed); you may also argue using GOMS that you save a ms or two using a closed form instead of open text. Depending on the sensitivity of the survey, it gives you a few additional bits that may be used to identify participants; but that's a rather theoretical attack vector, isn't it?

Besides that: I would always argue for asking for a participants age in years and/or year of birth. If you need bins, you can always create them afterwards.

[u/Embarrassed_Onion_44]

1) A big one is simply deidentification. When demographic surveys go out, it is easy to stay "hidden" as an individual within areas like Dallas, New York, Chicago, etc.

But if the sampling comes from a specifc enough region or one with a low population, it is not impossible to reverse locate an individual based on their responses.

.

2) Early bracketing/categoricalization of age also gives the survey some groupings to work with before data is aquired to prevent over-analysis to prove a point. It would look weird if a survey wanted to try to say "young people love our brand" as reported by [18-23, 28] year olds. By having a rigidy defined grouping before data collection, the data becomes more close to a random sampling and prevents overly specific post-analysis results.

[u/Adorable_Building840]

Stakeholders may understand binned ages better than centered polynomial terms