How can I calculate the p-value (statistical significance) if my hypothesis asks for specific mean values?

(Cross-Post with no answer yet: ANOVA test mean against predefined constant)

I suppose this can be somehow calculated using ANOVA, but I could only find examples where the hypothesis states that all means are the same.

I.e. I have four groups, and my hypothesis states that the means will be:

Quote:

x1=1

x2=2

x3=3

x4=4

Example:

Suppose the following (simplified) example:

I collect data by asking people on the street to order fruits according to their preference. There are four fruits (groups) to order: apple, banana, coconut, pear

So if participant P1 likes apples A the most, followed by coconut C, then pear P and bananas B as least preferred, I note the following data (position in the ordered list):

Quote:

P1: A=1, B=4, C=2, P=3

Quote:

P2: A=2, B=4, C=3, P=1

Quote:

A | B | C | P

----------------------------------------

1 | 4 | 2 | 3

2 | 4 | 3 | 1

... and a lot more data

Quote:

xA = 1

xB = 2

xC = 3

xP = 4

Thanks! ]]>

I'm working on my thesis and I have to compare how two constructs relate to each other.

I have two Likert scales, one for each construct(behaviour). First one has 4 subscales and 21 items total and the second one has 3 subscales and 25 items total. How should I treat this data in SPPS, as ordinal or scale? I've read that if the Likert scale describes a behavior or a trait (like in my case), it can be treated as scale in SPSS.

Also, how should I calculate the value of the whole scale? Calculate the means for each of the subscales, sum them and divide by the number of subscales?

Last question, my hypothesis is: Participants who score high on Construct1 , also score high on Construct2. How should I test that?

I would really appreciate any help! ]]>