Thanks for your reply, and the tweaks to the post. However:
[I] decided to keep the discussion short because the regression seemed to offer very limited practical significance (as you pointed out). Had I decided to give it more weight in my analysis then it certainly would be appropriate to offer a fuller explanation. Nonetheless, I should have been clearer about the limited usefulness of the regression, and noted it as the reason for the short discussion.
I think the regression having little practical significance makes it the most useful part of the analysis: it illustrates the variation in the dependent variable is poorly explained by all/âany of the variables investigated, that many of the associations found by bivariate assessment vanish when controlling for others, and gives better estimates of the effect size (and thus relative importance) of those which still exert statistical effect. Noting, essentially, âBut the regression analyses implies a lot of the associations we previously noted are either confounded or trivial, and even when we take all the variables together we canât predict welcomeness much better than taking the averageâ at the end buries the lede.
A worked example. The summary notes, âEAs in local groups, in particular, view the movement as more welcoming than those not in local groupsâ (my emphasis). If you look at the t-test between members and nonmembers thereâs a difference of ~ 0.25 âlikert levelsâ, which is one of the larger effect sizes reported.
Yet we presumably care about how much of this difference can be attributed to local groups. If the story is âEAs in local groups find EA more welcoming because they skew (say) male and youngâ, it seems better to focus attention on these things instead. Regression isnât a magic wand to remove confounding (cf.), but it tends to be better than not doing it at all (which is essentially what is being done when you test association between a single variable and the outcome).
As I noted before, the âeffect sizeâ of local group membership when controlling for other variables is still statistically significant, but utterly trivial. Again: it is ~ 1/â1000thof a likert level; the upper bound of the 95% confidence interval would only be ~ 2/â1000th of a likert level. By comparison, the effect of gender or year of involvement are two orders of magnitude greater. It seems better in the conclusion to highlight results like these, rather than results the analysis demonstrates have no meaningful effect when other variables are controlled for.
A few more minor things:
(Which I forgot earlier). If you are willing to use means, you probably can use standard errors/âconfidence intervals, which may help in the âthis group looks different, but small group sizeâ points.
Bonferroni makes a rod for your back given it is conservative (cf.); an alternative approach is false discovery rate control instead of family wise error rate control. Although minor, if you are going to use this to get your adjusted significance threshold, this should be mentioned early, and the result which âdoesnât make the cutâ should be simply be reported as non-significant.
It is generally a bad idea to lump categories together (e.g. countries, cause areas) for regression as this loses information (and so statistical power). One of the challenges of regression analysis is garden of forking path issues (even post-hocâsome coefficients âpop intoâ and out of statistical significance depending on which model is used, and once Iâve seen one, Iâm not sure how much to discount subsequent ones). It is here where an analysis plan which pre-specifies this is very valuable.
Iâm appreciating this exchange. I wonder if part of the problem stems from the word welcoming*, especially as selection bias naturally tends to neglect those who didnât feel welcome. This could especially be a problem for assessing how welcome women feel, if whatâs happening is that many quickly donât feel welcome and simply leave.
One way to overcome this would be to set up a contact list for a group of male and female people attending an intro event. Even 10 of each (and 5 others) could be useful, not for statistical significance but for an initial assessment at low cost in time and effort. This could be via email but better would be via phone also. You could follow up after the first activity, at the end of the session, a month later and a year later. It could be repeated on a small scale at several intro events, which might give more initial info than a large sample at one event, which might not be representative.
The most powerful tool might be telephoned âsemi-structured interviewsâ which is a well-established social science and participatory appraisal method. Again you wouldnât be looking for statistical significance but more for hypothesis generation, which could then be used in a follow up. eg if a lot of women were saying something like âI just didnât feel comfortableâ or âit was too âŚ.â that could suggest a more specific follow up study, or even lead directly to thoughts about a way to redesign intros.
It helps if such a survey wasnât conducted by someone seen as an organiser, and perhaps ideally a woman?
Thanks for your reply, and the tweaks to the post. However:
I think the regression having little practical significance makes it the most useful part of the analysis: it illustrates the variation in the dependent variable is poorly explained by all/âany of the variables investigated, that many of the associations found by bivariate assessment vanish when controlling for others, and gives better estimates of the effect size (and thus relative importance) of those which still exert statistical effect. Noting, essentially, âBut the regression analyses implies a lot of the associations we previously noted are either confounded or trivial, and even when we take all the variables together we canât predict welcomeness much better than taking the averageâ at the end buries the lede.
A worked example. The summary notes, âEAs in local groups, in particular, view the movement as more welcoming than those not in local groupsâ (my emphasis). If you look at the t-test between members and nonmembers thereâs a difference of ~ 0.25 âlikert levelsâ, which is one of the larger effect sizes reported.
Yet we presumably care about how much of this difference can be attributed to local groups. If the story is âEAs in local groups find EA more welcoming because they skew (say) male and youngâ, it seems better to focus attention on these things instead. Regression isnât a magic wand to remove confounding (cf.), but it tends to be better than not doing it at all (which is essentially what is being done when you test association between a single variable and the outcome).
As I noted before, the âeffect sizeâ of local group membership when controlling for other variables is still statistically significant, but utterly trivial. Again: it is ~ 1/â1000th of a likert level; the upper bound of the 95% confidence interval would only be ~ 2/â1000th of a likert level. By comparison, the effect of gender or year of involvement are two orders of magnitude greater. It seems better in the conclusion to highlight results like these, rather than results the analysis demonstrates have no meaningful effect when other variables are controlled for.
A few more minor things:
(Which I forgot earlier). If you are willing to use means, you probably can use standard errors/âconfidence intervals, which may help in the âthis group looks different, but small group sizeâ points.
Bonferroni makes a rod for your back given it is conservative (cf.); an alternative approach is false discovery rate control instead of family wise error rate control. Although minor, if you are going to use this to get your adjusted significance threshold, this should be mentioned early, and the result which âdoesnât make the cutâ should be simply be reported as non-significant.
It is generally a bad idea to lump categories together (e.g. countries, cause areas) for regression as this loses information (and so statistical power). One of the challenges of regression analysis is garden of forking path issues (even post-hocâsome coefficients âpop intoâ and out of statistical significance depending on which model is used, and once Iâve seen one, Iâm not sure how much to discount subsequent ones). It is here where an analysis plan which pre-specifies this is very valuable.
Iâm appreciating this exchange. I wonder if part of the problem stems from the word welcoming*, especially as selection bias naturally tends to neglect those who didnât feel welcome. This could especially be a problem for assessing how welcome women feel, if whatâs happening is that many quickly donât feel welcome and simply leave.
One way to overcome this would be to set up a contact list for a group of male and female people attending an intro event. Even 10 of each (and 5 others) could be useful, not for statistical significance but for an initial assessment at low cost in time and effort. This could be via email but better would be via phone also. You could follow up after the first activity, at the end of the session, a month later and a year later. It could be repeated on a small scale at several intro events, which might give more initial info than a large sample at one event, which might not be representative.
The most powerful tool might be telephoned âsemi-structured interviewsâ which is a well-established social science and participatory appraisal method. Again you wouldnât be looking for statistical significance but more for hypothesis generation, which could then be used in a follow up. eg if a lot of women were saying something like âI just didnât feel comfortableâ or âit was too âŚ.â that could suggest a more specific follow up study, or even lead directly to thoughts about a way to redesign intros.
It helps if such a survey wasnât conducted by someone seen as an organiser, and perhaps ideally a woman?
an alternative might be âsatisfactionâ ?