4 ms·

When this is a concern, I do two analyses -- one with and one without the potentially confounding group. It is still better to obtain the data, since otherwise

When this is a concern, I do two analyses -- one with and one without the potentially confounding group. It is still better to obtain the data, since otherwise we knowingly have a biased sample. (Somewhat similar to only surveying people who "seem approachable.")

pdonis 4y ago
> When this is a concern, I do two analyses -- one with and one without the potentially confounding group. If this analysis tells you there is no significant difference between the subgroups, that's nice, because you can then just lump them into one and not worry about it any more. (This also tells you you didn't actually need to send out the second email, but what's done is done.) But if this analysis tells you there is a significant difference between the subgroups, you don't know if it's something that was there already or something that you created by sending out a second email to people who had not responded to the first. So you're not actually better off than if you hadn't sent the second email; you're worse off, because you now have a confounding variable present and you don't know what to do about it. > otherwise we knowingly have a biased sample You have a biased sample regardless of whether you send out a second email or not.
- psyklic 4y ago
  It seems you could possibly apply this same logic to exclude any subgroup. For example, you must sample the mall-goer population. Some people "seem approachable"/etc. ("respond to your first email"), and you approach them. You are worried that others will be annoyed if you approach. If this assumption is true (which it may not be), their responses could be biased and confounding. Should you still survey them? I believe the stats textbooks would say yes, since it will result in a more representative sample. I assume it would be a detriment to the survey if you exclude some groups merely because they aren't compatible with your survey-taking techniques. All surveys have some inherent measurement error, and there are some techniques to detect biases/inconsistencies without excluding entire groups.
  - pdonis 4y ago
    > It seems you could possibly apply this same logic to exclude any subgroup. No, just any subgroup that, by construction, is not reachable with whatever technique you are using. The key point is that there will always be such subgroups. See further comments below. > Should you still survey them? If you mean, should you still send out surveys the first time even though you don't know how people will respond? If you're going to do a survey at all, you have no choice, so this question is pointless. If you mean, should you continue to pester people who didn't respond the first time, on the grounds that "it will result in a more representative sample", I would question whether that is really the case. See further comments below. > I assume it would be a detriment to the survey if you exclude some groups merely because they aren't compatible with your survey-taking techniques. It's a "detriment" if you fail to realize that there is no way to avoid it. Taking a survey inherently means you get responses from people who respond to surveys and not from people who don't. Going back and asking a second time for a response from people who didn't respond the first time inherently means you get responses from people who will respond the second time and not from people who won't. And so on. There is no way to avoid the fact that any survey-taking technique will inherently exclude some proportion of the population. Once you realize this, you realize that the idea of "getting a more representive sample" is based on a false premise, that you can somehow "include" everyone if you just go about it the right way. You can't.
    - psyklic 4y ago
      "Getting a more representative sample" is premised on getting as representative a sample as possible, not necessarily perfectly. Using language such as "pester" is making assumptions about the group. In fact, many likely were busy or have just forgotten to reply. As I mentioned above, there are techniques for assessing biased/inconsistent responses.
      - pdonis 4y ago
        > "Getting a more representative sample" is premised on getting as representative a sample as possible But representative of what? You can't get a sample that is "representative" of non-responders--because they don't respond. If you ask once and include whoever responds in your sample, your sample is representative (you hope) of people who respond. If you ask non-responders again, your sample now includes people who responded when asked once, and people who responded when asked twice. But the "asked twice" part introduces an extra variable: did the fact that you asked them twice change something that you would rather had not been changed? Given that, it's not clear that the second sample is any more "representative" of anything useful than the first.
- cat_plus_plus 4y ago
  In terms of honest research, discovering uncertainties is as important as discovering trends, you just duly present these in your conclusions. If you are motivated to reach certain conclusions from the outset, you are looking for politics or marketing class, not statistics class.

pdonis 4y ago

> When this is a concern, I do two analyses -- one with and one without the potentially confounding group. If this analysis tells you there is no significant difference between the subgroups, that's nice, because you can then just lump them into one and not worry about it any more. (This also tells you you didn't actually need to send out the second email, but what's done is done.) But if this analysis tells you there is a significant difference between the subgroups, you don't know if it's something that was there already or something that you created by sending out a second email to people who had not responded to the first. So you're not actually better off than if you hadn't sent the second email; you're worse off, because you now have a confounding variable present and you don't know what to do about it. > otherwise we knowingly have a biased sample You have a biased sample regardless of whether you send out a second email or not.

psyklic 4y ago
It seems you could possibly apply this same logic to exclude any subgroup. For example, you must sample the mall-goer population. Some people "seem approachable"/etc. ("respond to your first email"), and you approach them. You are worried that others will be annoyed if you approach. If this assumption is true (which it may not be), their responses could be biased and confounding. Should you still survey them? I believe the stats textbooks would say yes, since it will result in a more representative sample. I assume it would be a detriment to the survey if you exclude some groups merely because they aren't compatible with your survey-taking techniques. All surveys have some inherent measurement error, and there are some techniques to detect biases/inconsistencies without excluding entire groups.
- pdonis 4y ago
  > It seems you could possibly apply this same logic to exclude any subgroup. No, just any subgroup that, by construction, is not reachable with whatever technique you are using. The key point is that there will always be such subgroups. See further comments below. > Should you still survey them? If you mean, should you still send out surveys the first time even though you don't know how people will respond? If you're going to do a survey at all, you have no choice, so this question is pointless. If you mean, should you continue to pester people who didn't respond the first time, on the grounds that "it will result in a more representative sample", I would question whether that is really the case. See further comments below. > I assume it would be a detriment to the survey if you exclude some groups merely because they aren't compatible with your survey-taking techniques. It's a "detriment" if you fail to realize that there is no way to avoid it. Taking a survey inherently means you get responses from people who respond to surveys and not from people who don't. Going back and asking a second time for a response from people who didn't respond the first time inherently means you get responses from people who will respond the second time and not from people who won't. And so on. There is no way to avoid the fact that any survey-taking technique will inherently exclude some proportion of the population. Once you realize this, you realize that the idea of "getting a more representive sample" is based on a false premise, that you can somehow "include" everyone if you just go about it the right way. You can't.
  - psyklic 4y ago
    "Getting a more representative sample" is premised on getting as representative a sample as possible, not necessarily perfectly. Using language such as "pester" is making assumptions about the group. In fact, many likely were busy or have just forgotten to reply. As I mentioned above, there are techniques for assessing biased/inconsistent responses.
    - pdonis 4y ago
      > "Getting a more representative sample" is premised on getting as representative a sample as possible But representative of what? You can't get a sample that is "representative" of non-responders--because they don't respond. If you ask once and include whoever responds in your sample, your sample is representative (you hope) of people who respond. If you ask non-responders again, your sample now includes people who responded when asked once, and people who responded when asked twice. But the "asked twice" part introduces an extra variable: did the fact that you asked them twice change something that you would rather had not been changed? Given that, it's not clear that the second sample is any more "representative" of anything useful than the first.
cat_plus_plus 4y ago
In terms of honest research, discovering uncertainties is as important as discovering trends, you just duly present these in your conclusions. If you are motivated to reach certain conclusions from the outset, you are looking for politics or marketing class, not statistics class.