Query1)
Can mirt exploratory factor analysis method be used for factor structure for marketing/management research studies because most of the studies that I have gone through are related to education test studies? My objective is to extract factors to be used in subsequent analysis (Regression/SEM) My data is comprised of questions like: Data sample for Rasch Factors Thinking about your general shopping habits, do you ever: a. Buy something online b. Use your cell phone to buy something online c. Watch product review videos online RESPONSE CATEGORIES: Yes = 1 No = 0 Data sample for graded Factors Thinking about ride-hailing services such as Uber or Lyft, do you think the following statements describe them well? a. Are less expensive than taking a taxi c. Use drivers who you would feel safe riding with d. Save their users time and stress e. Are more reliable than taking a taxi or public transportation RESPONSE CATEGORIES: Yes =3 Not sure = 2
No = 1 Query2) If we use mirt exploratory factor analysis using rasch model for dichotomous and graded for polytomous, do these models by default contain tetrachoric correlation for rash model and polychoric correlation for graded models? My objective is to extract factors to be used in subsequent analysis (Regression/SEM) Note: I am using R for data analysis
Dear Saeed Shah
Basically, The item response theory (IRT), also known as the latent response theory, is a set of mathematical models that seek to explain the link between latent traits (unobservable characteristics or attributes) and their manifestations (i.e. observed outcomes, responses or performance).
Have a look: https://www.publichealth.columbia.edu/research/population-health-methods/item-response-theory
Best Regards
Dr. Fatemeh Khozaei
I think it all depends on the nature of the problem you are researching on. This type of questions cannot be illicitly answer with yes or no.
Dear Saeed Shah,
From my point of view, there are many things here. Factor analysis will help you to organize your data into factors. However, which item is more difficult or easy to endorse is another point.
In attitude scales (most used in Marketing), I agree with the colleague Usman Abbo that your question is better answered using multiple scales anchored in 1 to 5 or 1 to 7 scales. In this case, using discrimination and difficulty parameters is very good to cover different questions. Thus, for instance, if you have a dimension, "how much I like the service" with 3 items distributed in different ranges of difficulty, it is the best configuration that you could have. Because if you have all easy items, you cannot differentiate your service, or you have the impression that your service is good when it is not, you have only a scale that isn't able to catch the phenomena in its range.
Another point is that the IRT analysis depends on the factor's item composition and the sample. For this reason, I think it is important to have a good scale where you choose the items based on the factor loading, but also based on the discrimination and difficulty of the item. But you will need different steps. Step 1 is the factor analysis, Step 2 conducts the IRT, and Step 3 chooses the best items for your study.
I hope I could help,
Amalia
Amalia Raquel Pérez Nebra
I would really appreciate that you spared some time to answer my question. May be I am unable to ask question properly but here my objective is to create factors from underlying battery of items with different scales. So my question is simple that can I use mirt to perform EFA to create factors to be used in subsequent analysis (Regression/SEM).
One more things I would like to know that what exactly "easy" and "difficult" items you mean in your given answer?
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