Could I ask to clarify which question you are looking at? I assume maybe the importance for retention question? There we observe that non-white respondents are more likely to select EAGx specifically (about twiceas large a percentage of non-white respondents selected EAGx) and indeed I expect this is driven by geography for the reasons you say. There is no significant difference for EAG, though a slightly higher percentage of non-white respondents selected that as well.)
To answer your question about the analyses: the chi-square tests just look at whether there are differences in the proportions of white/non-white, male/non-male respondents selecting different categories don’t attempt to control for other characteristics. So you should just read these as identifying differences between these groups, rather than as necessarily showing that these differences are explained by the groupings themselves. Note that just knowing the proportions, even if they’re not causal may still be action-relevant, i.e. we might want to know what programs are actually helping a larger number of non-white EAs, even if this is ultimately explained by some third factor). In contrast, in the models at the end looking at predictors of NPS and change in level of interest in EA we do try to control for different influences simultaneously.
Hi Linda. Thanks for the question!
Could I ask to clarify which question you are looking at? I assume maybe the importance for retention question? There we observe that non-white respondents are more likely to select EAGx specifically (about twice as large a percentage of non-white respondents selected EAGx) and indeed I expect this is driven by geography for the reasons you say. There is no significant difference for EAG, though a slightly higher percentage of non-white respondents selected that as well.)
To answer your question about the analyses: the chi-square tests just look at whether there are differences in the proportions of white/non-white, male/non-male respondents selecting different categories don’t attempt to control for other characteristics. So you should just read these as identifying differences between these groups, rather than as necessarily showing that these differences are explained by the groupings themselves. Note that just knowing the proportions, even if they’re not causal may still be action-relevant, i.e. we might want to know what programs are actually helping a larger number of non-white EAs, even if this is ultimately explained by some third factor). In contrast, in the models at the end looking at predictors of NPS and change in level of interest in EA we do try to control for different influences simultaneously.