Significance between samples

#1
I am not an expert user when it comes to SPSS so maybe this is more than I can handle. Here is the situation:

I have two products I am comparing, Product A and Product B. I use the typical 7-point scale of Strongly Dislike (1) to Strongly Like (7) with Neither Like Nor Dislike (4). I recoded the three dislike opinions as one value, the three like opinions as one value, and the neither as one value - 1,7, and 4 respectively. I run a crosstab on with my recoded variable against the product variable and get the combined results I am looking for.

Here is what I don't know how to do. Lets say 40% of the people "liked" Product A and 42% of the people like Product B. Is there something in SPSS that would allow me to take those two percentages and calculate their significance? I looked at the One-Way ANOVA under compare means, but that groups them all together. I need it broken down between my recoded values and the two products.

Is this possible? If it is possible and you are willing to help, I would be really grateful.
 
#3
You have a couple of choices:
1. Run an independent sample t-test, with the test variable being the rating and the group variable being the product. However, the test variable needs to be the full 7 point scale, not the one you recoded. Technically, you probably should use the Mann-Whitney test, but it will give you the same answer.
2. Run a chi-square test (when you run a cross-tabs, click on "statistics" and select chi-square). Here, you should use the recoded rating variable. Note that the chi-square will only tell you that there's a difference going on somewhere, you'll need to check the data to see where that difference could be. If you're only focusing on the top 3 box of the scale, I'd recode further into a binomial variable (either rated in the top 3 box, or not), and run the chi-square test on that.

If your results are actually 40% for product A and 42% for product B, I can tell you right away that the results are not statistically significant, unless if your sample size is huge (as in, the tens of thousands).