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In my psych class we were testing to see if groups of men or groups of women would be more helpful in an emergency situation (we are testing for the bystander effect), and we are testing to see if group size is a factor (large group vs small group). we recorded either yes (the group did help) or no (the group did not help).

What test should I use (chi squared, one way anova, two way anova, etc.)?

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    $\begingroup$ What have you done to try to solve the problem on your own? $\endgroup$ – John Nov 17 '14 at 23:50
  • $\begingroup$ How would you use anova on "yes" vs "no" outcomes? If you have both sex (M/F) and group size as factors, how would it be one-way? Can you clarify the situation? Note also (for improved clarity of ideas) that you're not talking about how helpful people were, only whether they helped or not, so you're really comparing how likely they are to help, rather than how helpful they were when they did help (comparing existence rather than degree). Careful wording will reduce the likelihood of confusing a reader. I note also that your title says "my study", but your body text says "we are". What's up? $\endgroup$ – Glen_b -Reinstate Monica Nov 18 '14 at 3:33
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If your underlying question is "Are small groups more or less likely to help", then you want to produce a model that has output like "chance of helping" in terms of inputs relating to the dependent variables. If you expect a possibility of a sex (M/F) effect, you should include it as a covariate.

The most obvious choice would be a logistic regression model, with DVs of sex and group size.

It might also be possible to do it using chi-square, but if you're allowing for both DVs, you'll have a 2x2x$k$ table, where $k$ is the number of group sizes. One advantage of the logistic regression is it's easier to model group size as continuous, and if there are more than two group sizes, you can more easily test for some form of ordering (i.e. deal with effects like like "as the group size increases, people are less likely to help").

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