Factor analysis attempts to bring inter correlated variables together under more general, underlying variables. This is often used in developing a scale for understanding the nature of the construct.

Your research is either examining the correlation or cause- effect relationship between the independent and the dependent variable. You need to choose an analysis appropriate to a research question.

Anyway thanks for your concern. My real question is that, 

Whether  items of both independent variable (Retail Service Quality) and Dependent Variable (Customer Loyalty) together can be factorized to get the unique construct through the Exploratory Factor Analysis?

Pl see the attachments.May be of some help.  

Chapter A note on the Factor Analysis in Forestry Sciences

No, It should not. You should try to reduce the dimension of dependent variables based factor analysis.

And thus, you can explain the relationship between the independent with the reduced no of dependent construct.

Thanks for your attachment,

In my case, Retail service quality is considered as the independent variable , which is measured by 22 questions or items under Five Dimensions (Physical Aspects, Reliability, Personal Interaction, Problem Solving and Policy).

Meanwhile, Customer Loyalty is measured by 8 questions or items under the one dimension as Customer loyalty.

In that scenario, we have six dimensions (Physical Aspects, Reliability, Personal Interaction, Problem Solving, Policy and customer Loyalty) , which are measured by 30 questions altogether.

My question is that,

Can I put all the 30 questions altogether to factorize to find out the Unique constructs?

Or

Can I put the items of the independent variable (22 questions) only to factorize the model?

Based on the second question, once we factorize the questions of the independent variable, can we use those factorized construct as independent variable to predict the dependent variable (Un Factorized Construct) as the Customer Loyalty.

Or

Whether We need to factorize both items of the independent and dependent variable separately

If you are sure that Customer loyalty is dependent variable. You may go for separate FA otherwise, go directly to FA. 

Thank you so much, 

What about for the CFA,

Whether We need to factorize both items of the independent and dependent variable separately 

You flag your problem only, keeping aside the FA or CFA, pl. Your problem and obj will let me understand that whether you should go separately or together. 

Thank you , Here, i documented the methodology with problem

Research Methodology

First of all, I intend to perform the EFA to factorize the items of independent variable (22 Questions), which will give the unique and uncorrelated factors under the independent variable.

Meanwhile, I don’t want to perform the EFA for whole 30 question or items. In which, 8 items are used to measure the dependent variable. Generally, there is a coloration or association between independent and dependent variables. Therefore, I only used 22 items under the independent variables to perform the EFA to construct the unique model.

Secondly, I wish to perform the CFA with the identified items of the factors under the independent variable via EFA and items of dependent variable, those are not factorized via EFA ( 8 items) to confirm the whole set of model , which contains both independent and dependent variable under the measurement model.

Thereafter, I also like to perform the Structural Equation Model (SEM) to find out the path way between independent variables (Physical Aspects, Reliability, Personal Interaction, Problem Solving and Policy) and dependent variable as Customer Loyalty.

Based on the issue you raised, you go for separate analysis and then go for SEM. This will serve the purpose as you intend. 

Thanks for your Suggestion Sir,

For that purpose,

What is the required level of Sample size for the advance statistical tools as Exploratory Factor Analysis and Structural Equation Model?

Whether Two thousand (2000) sample size ( Derived from the  population via formula)  is enough or too much or less to get the significant model

If it is too much, how we will tackle it smoothly to get the Significant Model.

What I could presume that it is perception based data.Therefore it will always be          nice to play with large dataset (that to from formula).  Do analysis without preconceived notion. 

Thank you sir

Once you have set out an objective to study cause-effect relationship between independent and dependent variables you cannot combine the two to evolve a new construct  for this study.

The purpose of exploratory factor analysis is to identify the underlying factor structure. Confirmatory factor analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables.

Your sample size would also depend on the population and the sampling technique used.

Dear Sivapalan,

The main purpose of exploratory factor analysis is to identify the structure of a construct for the very first time. That means, either there is no theory behind the factor structure or we are running the EFA for the first time in that particular sample. However, the CFA usually is used to confirm the obtained structure based on the EFA. In addition, CFA might be used  to confirm the structure of a pr-identified construct in the respective sample.

Regards,

Dr. Sanaz Aazami