Can items of both independent variables and dependent variables altogether be factorized to get the unique construct through the CFA?
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.
You can use in combination. But better to use CFA separately on Independent and Dependent variables.
Thank you sir, I am impressed by your publication and achievement,
Dear sir,
Please try to solve my research Problem
Research on Retail service quality and customer loyalty is being carried out presently by me.
Brief Description of the Study:
In our Scope, Retail Service Quality is considered as the independent variable. Further, it has five dimensions or Variables, Which is measured by 22 questions or items (Physical Aspects, Reliability, Personal Interaction, Problem Solving and Policy).Meanwhile, Customer Loyalty is considered as the Dependent Variable, which is measured by 8 questions or items.
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.
Sampling Adequacy
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 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.
Why do you want to factorize the dependent variables? SEM is developed to solve for such relationships where there are 'n' independent variables and 'n' dependent variables.
However, given the number of variables you have , my guess is you would require a considerable number of respondents for getting good results.
I can't understand why you want to do this : it's impossible to verify convergent and discriminant validity like this..
Moreover, your constructs of retail service quality and customer loyalty are second order constructs with formative indicators (vs reflective) : to validate them, use for each measurement index the procedure of Mackenzie et al. (2011). My JBR (Definition and psychometric validation of a measurement index common to website and store images) that you can downloaded in Researchgate, give a very complete application of this method.
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