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 (Physical Aspects, Reliability, Personal Interaction, Problem Solving and Policy).
Meanwhile, Customer Loyalty is considered as the Dependent Variable.
Brief Description About the statistical techniques:
Advance statistical techniques as Exploratory Factor Analysis and Confirmatory Factor Analysis will be used.
Our questions:
Question No 01:
Whether Questions or 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?
“According to the Malhotra (2004), No Distinction in terms of independent and dependent variable is made in factor analysis. Further, He documented that, Main purpose of the exploratory factor analysis is to identify a new, smaller set of uncorrelated variables to replace the original set of correlated variables. Based on his statement, we can’t factorize the Questions or items of both independent variable (Retail Service Quality) and Dependent Variable (Customer Loyalty) together to construct the model. It means that, as usual there is a relationship between dependent and independent variable”
“In contrast, the same author Malhotra (2004) noted that factor analysis is an inter dependence technique in that an entire set of interdependent relationships is examined”
Based on the above viewpoints,
I am confused on whether questions or 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?
Question No 02:
Researchers in the field of management and marketing suggested that, before performing the confirmatory factor analysis, exploratory factor analysis may be used to construct the unique model, which gives the real pathway to confirm the model via confirmatory factor analysis. Meanwhile, many researchers in the marketing field performed the confirmatory factor analysis with the questions or items of both the independent variable and dependent variable together to confirm the model.
Can questions or items of both independent variable (Retail Service Quality) and dependent variable (Customer Loyalty) together be factorized to get the unique construct through the Confirmatory Factor Analysis?
In that scenario,
Question No 03:
What is the main purpose of both exploratory and confirmatory factor analysis?
Thanks,
Achchuthan, S
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.
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
thankyou all for the suggestions. i still have a doubt, when we do CFA do we need to take both dependent and independent variables?
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