Methodology

@atopal mentioned in the last meeting that we've got no frame to use for sampling means we cannot do probability sampling.

As a solution, we can redefine our population by limiting it to some group in which we can get a complete list of members so we can do random sampling and generalize findings to that group. This takes more time and effort given the situation. I'm also guessing it could be hard to get that list and to initially redefine the population given the survey goals.

## Suggestions

Non-probability sampling techniques such as what was applied in the past years can be used. *Results from this, however, should be interpreted with caution*. Data from non-probability sampling techniques cannot be used to generalize results to the entire population. The good news is, we can still do descriptive analysis and doing this will still be aligned to the survey's goal and data usage as indicated [here](https://survey.devographics.com/en-US/survey/state-of-css/2023) (particularly the italicized ones as shown below).

> What is the survey's goal?
> The survey's goal is to track the evolution of upcoming features and libraries, and *help developers decide which new technologies to focus on*.

> How will this data be used?
> *All data collected will be released openly for any developer or company to consult. Browser vendors also use this data to prioritize focus areas and inform their roadmaps*.

The question now is: **How can we can collect the data that we need to allow us to perform the analysis method that is assumed to address the survey goals, given the target launch date?**

We can do the following:
1. get as many **target respondents** as possible by making sure we make the survey available to them, esp. to the subgroups we are interested about
- anybody who writes CSS, whether regularly or occasionally, as part of their job, as a student, or just for fun as indicated [here](https://survey.devographics.com/en-US/survey/state-of-css/2023)
- respondents who have hopefully, 100% completion rate so that we can
2. obtain data that would allow us to describe the patterns in the results in context (data on demographics will be valuable on this). This means, we get results that are not generalizable to the population of people who write CSS but we are capable of describing the diversity of choices within the group of respondents (descriptive analysis)
- through clustering or
- simple correlations in plots made possible by demographic data

To add, given the past results we need to make sure we make the questionnaire available to communities of female CSS coders, and other subgroups of interest from which we got low number of respondents. @SachaG, @atopal let me know your thoughts!

_Originally posted by @shhaine in https://github.com/Devographics/surveys/issues/70#issuecomment-1579074566_