Confirmatory factor analysis refers to a multivariate statistical technique that helps to evaluate how best the measured variables signify the number of constructs. Researcher uses the tool of confirmatory factor analysis to identify the appropriate number of factors in the given set of data and establish the relationship between latent and measured variables. The method is essential for providing information to either reject or accept the measurement theory. Confirmatory factor analysis can be used to assess the validity of the reflective scale; however, the right procedure should be followed to attain accurate results. Reflective measures indicate high inter-correlations; therefore, the most common method used for evaluating the validity of such tests is confirmatory factor analysis. Besides, Cronbach’s alpha is considered a practical approach to assess the unidimensionality of the reflective scale using the existing correlations.
The first steps in determining the validity of reflective scales using factor analysis method are defining the individual construct. In this case, a pretest in conducted to identify the construct items which are then defined theoretically. The confirmatory test is carried out on the specific measurement model to establish the constructs. Secondly, the overall measurement model is developed to identify the unidimensionality that exists within the construct error variance. Thirdly, the study is designed to give empirical outcomes for the research. Finally, the evaluation of the measurement model validity is performed by making a comparison between the real model and the measurement model.
There are several benefits associated with using the factor analysis method. One of the advantages of using the confirmatory factor analysis approach is that the technique provides an aggregate solution using data with high power reduction. The method focus on correcting the errors likely to be experienced in measurement, this helps to minimize the double-counting. Confirmatory factor analysis is suitable for identifying important underlying dimensions between variables. Lastly, the component score obtained in the analysis can be saved for future study in both model testing and inferences.
Variables developed from reflective scales require the assessment of reliability to predict the factors in subsequent objective models. Researchers are supposed to determine whether a similar set of items would be able to give the same responses. The preferable method used to test reliability is Cronbach’s alpha. This is conducted by administering the test repeatedly to confirm whether the variable derived from the same items indicate uniform responses. Cronbach’s alpha helps to determine the level of reliability by measuring the average correlation or internal consistency of a set of items in the research. Reflective scales are essential for probing underlying constructs formulated by the researcher in a survey. Cronbach’s alpha helps the researcher to answer questions concerning the reliability as functional scales are stretched to cover the realm underlying the prediction.
In conclusion, confirmatory factor analysis is an essential tool for evaluating the validity and reality of construct variables in statistical research. Most of the studies involving variables that need to be used for future analysis apply reliability testing using appropriate methods. For instance, the use of Cronbach’s alpha is considered the preferable approach to determine the stability and reliability of variables in the data set. If the reflective scales record poor reliability, then the researcher will conclude that individual items should be modified or changed to meet the desired reliability.
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