Quick reality check: using correlation and Chi-square for hypotheses testing

I have two ordinal values on a 5 point Likert scale, let's say "Would you like flowers?" and "Would you like to go out to dinner with me?". My hypotheses is that there is a relationship between the two, i.e. people who would like flowers would also like to go out for dinner with me.

I can perform a Spearman's correlation to between ordinals to determine whether a relationship exists - that's all well and good (I hope). Then I can recode the Likert scale in a nominal Yes / No value using 'Agree' and 'Strongly Agree' as a 'Yes' and 'Strongly disagree', 'Disagree' and 'Neither' as a 'No'. Once that's done, I can then perform a chi-square test to determine whether this is a significant association. In effect I'm trying to test the efficacy of giving flowers for more dinner dates.

My question is whether this a good approach to test my hypotheses, using the correlation test to determine a relationship and then confirming the strength of the association?

• Something isn't right here. You said that you have two ordinal values on a 5 point scale such as "Do you like chocolate." The answer to this question should be "Yes" or "No" not an indication of agreement, so why are you having to recode data into Yes/No? I suspect that your original question isn't Do you like chocolate, but something like, "How much do you agree with the following statements. . . I like chocolate." Is that the case or am I missing something? – StatsStudent Aug 1 '15 at 14:38
• Sorry, I just tried to give a simple example. Your assertion is correct that the Likert ordinal is used for that type of question, but then recoded to determine the efficacy / strength of the association. I guess my question is whether the chi-square should be used within this context or am I over thinking things? I've re-edited the question, hopefully that makes things clearer... – SeanCocteau Aug 1 '15 at 14:52
• So you can certainly used the Chi-Squared test, but the question is, what are you really trying to find out? If there is an association between those who agree/disagree with the chocolate and beer statements? If that's the case, there is no need to recode at all (unless you are doing this to make this a bit simpler). You could simply run a Chi-Squared test without any recoding at all (assuming you meet the Chi-Squared test assumptions as sufficient size and sufficiently large expected cell counts). Otherwise, an exact test, or category collapsing, would be preferable. – StatsStudent Aug 1 '15 at 15:06
• I'd also use the Yates' continuity correction when performing your Chi-Squared test if you use the 2x2 table approach. – StatsStudent Aug 1 '15 at 15:08
• Yes, the recode is the way to go. It sounds like your study might be underpowered and this may be way you are not seeing statistically significant results. A power analysis might warranted. – StatsStudent Aug 1 '15 at 15:52