Can a measure of association show a strong relationship while a test of significance shows that the results are not significant? If yes to either or both , can you provide some relevant examples illustrating how such situations might occur.

Trying to work on a project for school and this concept keeps stumping me. ]]>

It would be great to get your help on a stats problem I have as I am very new to the field.

Say I conduct a survey which aims to see how much people who are part of a social benefits scheme like the scheme. The total number of recipients of the scheme is 250 yet I can get 96 people to fill in an answer.

The answers are:

Yes, like the scheme 30%

No, don't like the scheme 70%

My question is, how do I know if my sample size is big enough at a particular confidence interval. Is it a case of finding a 'margin of error' based on the population and sample size. For example, since I can only get 96 people to fill out the form, at a confidence interval of 95% I can get a margin of error of +-9 (for example).

Many thanks for your help ]]>

I'm carrying out some research in linguistics. I am trying to show if some discourse markers (e.g. causal, additive, etc.) are significantly associated to particular genres (news, conversation, etc.).

My problem/doubt is that everywhere I look for info. about chi-squared test for independence, the examples show 2 by 2 or 3 by 3 tables. Mine would be of 12 rows (different types of connectors) and 5 columns (different genres). Is there any problem with that? Should I use another technique? (I'm using SPSS, just in case)

Thank you in advance. ]]>