Is a chi-test appropriate here?

Morning,

Try to use ANOVA. If you expect some interaction among your predictors, use "full factorial". Otherwise define your own model with direct effects only.

Best,

Eugene 

Thanks for your response Eugene, I'll look into ANOVA. Can you please clarify direct effects only? Do you mean take aspects from the various statistical tests that best suit my application?

Imagine that you have 2 predictors (gender, position) and 1 outcome variable, eg. satisfaction. They are nominal/categorical. In SPSS you go to Analyze->General linear model -> Univariate. (if you have several outcome variables -> Mutlivariate). You add gender and position as "factors". (would they be ordinal or interval, you could add them as "covariates"). Above on the right of the window you see the button "Model". By default "full factorial" is set. It means, that SPSS will make following tests:

gender -> satisfaction

position -> satisfaction

gender*position -> satisfaction.

If you have more predictors, the number of tests will grow exponentially, since SPSS will try to calculate all possible moderations. For example, imagine that you have 1 more predictor - "country". Then the tests will look like this:

gender -> satisfaction

position -> satisfaction

country -> satisfaction

gender*position -> satisfaction.

gender*coutnry -> satisfaction

country*position -> satisfaction

gender*country*position -> satisfaction

So depending on your theory, you can also define your own model, where you test direct effects only, or in the event of too many predictors, only moderation effects which are of interest for your theory. For example, only:

gender -> satisfaction

position -> satisfaction

country -> satisfaction

gender*position -> satisfaction.

I hope it helped

Best,

Eugene

In Eugene's example, if your coding is whether or not "satisfaction" was mentioned (0/1), then a chi-sq test would be appropriate due to the use of two nominal-level variables. If you want to look at the frequency that each person mentioned satisfaction, then you would have an interval-level dependent variable, so ANOVA would be appropriate.

Either way, doing this kind of statistical analysis on qualitative data is controversial. From a quantitative point of view, this is due to the use of small, non-random samples. From a qualitative point of view, this is due to a departure from the goal of seeking an interpretation of meaning.

My own position lies somewhere in between both of those positions, since I think that examining basic patterns in qualitative data can be worthwhile, if it leads you to reanalyze the qualitative data to ask how and why the patterns arose. That does, however, raise the issue of how you recognize a pattern that is strong enough to be worth pursuing, and a statistical test might be one way to make such a choice.

Here is an article that I wrote on the subject some years ago:

Valuable suggestions have already been given by David so I would rather focus on second part of your question where you have asked for alternative of SPSS. Here is a wonderful online chi square calculator that I have used personally and found very easy-to-use and handy when having upto 10x10 table:

It really depends on what detail you have in your response scale of your questionnaire. If you have a binary response are you satisfied yes no are you can do a Chi Square test to identify whether more women are more satisfied. If you have a response scale that has 3 or more levels then you may need something more advanced i.e. like a t test and if you want to look at interactions between gender, position and your dependent variables then you need to move to a more complex multivariate test. The type of test you chose depends on the question you want to interrogate. If you could outline the hypotheses first that you want to test then it would help you to identify the method by which you can determine whether it is due to chance or not.

I quite agree with the answers above and want to add that besides the Chi-square, Fishers exact test would suffice given the non parametric nature of the generated data and and sample size. 

You can try it.

GOOD LUCK FROM CHRIS.

When vairables level of measurment are nominal, then chi square will be appropriate. 

It is not parametric, the data could be skewed, sample size is small. Thus, used it when the parametric test cant be performed.

What needs to come out clear first is how you intend to measure your outcome variable (satisfaction). will it be qualitative categories or quantitative measure??

scenario 1: Assume it is quantitative- Since the sample is small and non random , u may have to resort to non parametric version of factorial Anova and of course face the penalty that comes along with non parametric tests.

Scenario 2: Assume it is qualitative: Say it is binary ( reporting satisfaction, 1 and  not reporting satisfaction, o): Here , i would use binary logistic regression as opposed to chi-square because handling interactions of the predictors may get  statistically  damn fuzzy and cryptic.

Note: Chi squared test would be an ideal candidate if you had two categorical variables only so that you just contingency table. You may want to try your hands out with 3 or more categorical variables using chi-square tests. But you may get completely lost in interpretation. Trust me on this. Just a humble advice and guidance, my friend.