EFA: variables load on different components as hypothesis

Question

I am doing undegrad thesis sand I stumble on some major problems when doing factors analysis:

My research topic is to compare the attitude of people towards 2 types of products (namely A and B).

My naive original plan was to create then compare 2 homogenous SEM models for each type of products and the proposed model is based on prior researches as well as my own rationale, so I guess I would have to perform EFA on one part of the data and the CFA and SEM for the rest (N=150).

However, when doing EFA, the variables loading in unwanted way and for each type of product, each set of data has a different pattern, thus, it is impossible to carry out my plan.

Now, I am thinking of merging the answers for 2 types of products into 1, since I have 2 set of similar questions asking the same aspects but of different objects (A or B). Additionally, there would be another column to distinguish 1-for product A and 0-for product B.

Pro: With an unified poll of data, I will only need to make one model.

Cons: I am not sure yet how to make the comparison if doing so.

I cannot tell which 2 opinions come from whom (which I don't think necessary).

And, instead of having N =150, it would be N=300, I don't know whether this will lead to false statistical results.

SO...

1. CAN I SKIP DOING EFA and jump on CFA right away?

2. If not, will my solution of merging fix my problem?

3. If not gain, what else can I do?

Thank you in advance!!!

Answer 1

Merging the data (N=300) looks a better way. So you can build a structural model to regress attitude on product type (a dummy variable, or a single-item construct). This allows you to easily explain the difference in attitude towards different product types.

For your questions: if the measurement items have been widely used before, doing CFA should be fine. If the items are self-developed, you have to do EFA.