To get a good answer, you must write a good question. Answering a statistics question without context is like boxing blindfolded. You might knock your opponent out, or you might break your hand on the ring post.

What goes into a good question?

1. Tell us the PROBLEM you are trying to solve. That is, the substantive problem, not the statistical aspects.

2. Tell us what math and statistics you know. If you’ve had one course in Introductory Stat, then it won’t make sense for us to give you an answer full of mixed model theory and matrix algebra. On the other hand, if you’ve got several courses or lots of experience, then we can assume you know some basics.

3. Tell us what data you have, where it came from, what is missing, how many variables, what are the Dependent Variables (DVs) and Independent Variables (IVs) – if any, and anything else we need to know about the data. Also tell us which (if any) statistical software you use.

4. Are you thinking of hiring a consultant, or do you just want pointers in some direction?

5. THEN, and ONLY THEN tell us what you’ve tried, why you aren’t happy, and so on.

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It is common that a variable that loads highly on two factors. In the ideal world, each variable would load highly on one and only one factor, but… life is not ideal! It means that that variable is a part of two latent variables or factors. The sign of the loading is fairly arbitrary.

As to further analysis, it depends on your goals and what you are trying to do and so on.

Peter

Dear Sir,

I have seen a book example that, Friedman test can be used as a nonparametric analogue of ‘Two way ANOVA without replication’. My question is whether Friedman test can also be used as a nonparametric analogue of ‘Two way ANOVA with replicates data.

Thanks in advance and regards,

Alam

I don’t see why not.

We are two graduate students who got involved in an interesting project focused on cultural values and their influence on social media. The study is focused around 15 hypotheses that we have worded based on our theoretical framework. Since neither of us can be called stat geniuses, though one of us did completely an introductory class on statistics, we’re writing here to ask how to best go about handling the hypothesis testing.

Here is an example of a hypothesis: Low context cultures will use more factual communication than high context cultures

The data is all the textual information across 8 facebook walls throughout the world. The definition of Low/High context cultures is made clear by our theory, and the same goes for factual communication. From here, we have counted the occurrences of factual communication from the walls. This enables us to calculate a frequency for each wall, but this will hardly be substantial enough.

Our question is this. What approach would you suggest taking using descriptive statistics and what approach using inferential statistics?

I hope it makes sense, and thanks in advance!

Best regards, Denmark

Sounds like you could use crosstabulations for descriptive statistics and a bunch of logistic regressions for inferential statistics

Peter