Hence, we decided to conduct a second varimax rotation with the 10 items left. This time, the result showed three factors were extracted with Factor 1 consisted of items 8, 9, 10, and Whereas factor 2 consisted of items 1, 2, and 3 and finally factor 3 consisted of items 4, 5, and 6.
Analyzing the items of each factor, we noticed that factor 1 could not be logically labeled, which consisted of item This item is related to Islamic dimension and does not logically related to the existing factor. Thus, we finally conducted the third varimax rotation for the 9 items left after discarding item The results of the analysis showed there were three latent variables emerged.
This indicated that the three underlying dimensions accounted for Thus, the solution of three latent structures of maternal involvement seemed to be the best. The dimensions can be logically labelled as reading encouragement for factor 1, homework monitoring for factor 2, and school relationship for factor 3. The variance of the first dimension, the largest eigenvalue was 2. Inspection on the scree plot also pointed out the 9-items measured three factors, with the sharp break point after the three factors.
In addition, all of the directions of loadings were positive and free from factorial complexity. The results of the final factor solution are illustrated in Table 2. In summary, the study suggests that there are three underlying dimensions of maternal involvement construct among mothers of a selected Integrated Primary Islamic Schools. The dimensions are maternal reading encouragement, maternal homework monitoring, and maternal school relationship.
Figure 3 shows the results of the three-factor measurement model of Maternal Accountability. The negative correlations were due to the negatively worded items for factor named Reliable. The loadings range was. Succinctly, construct validity for maternal accountability is supported. Moreover, there were no offending estimates, suggesting that the hypothesized model of Maternal Piety was admissible. The result above was achieved after taking into consideration the Modification Index MI.
Figure 4. Faith, the second latent construct, was a priori set to be the reference indicator due to the conceptually sound indicator of Piety. Hence, the p-value was not estimated and presumed that Faith was significant indicator of Piety. The details are outlined in Table 4. In summary, the hypothesized model of Maternal Piety was a second order construct for tri-dimensional measurement model; comprising of the Call for Virtue Virtue , Forbidding Vices F-Vices , and Faith in hereafter Faith.
It was supported by all the estimated fit statistics. Illustratively, Faith best indicated by item MSC4, mom reminds me about hereafter. AmosTM Arbuckle, J. AMOS 4. Armsden, G. The inventory of parent and peer attachment: Individual differences and their relationship to psychological well- being in adolescence. Journal of Youth and Adolescence, 16, DeVellis, R. Scale development: Theory and Applications. Newbury Park, California: Sage Publication. Hair, J. Multivariate Data Analysis 6th Edn.
New Jersey: prentice Hall. Kline, P. An Easy Guide to Factor Analysis. London: Routledge. Lantos, E. A confirmatory factor analysis approach to construct validation. Educational and Psychological Measurement, 41, Sidek Mohd. Pembinaan inventori personaliti muslim InPm untuk kegunaan belia Malaysia. Jurnal Pekama, 12, Measuring the unmeasurable: Maternal piety scales. Siti Aishah Hassan. Stevens, J. Thompson, B.
Thorndike, R. Measurement and Evaluation in Psychology and Education 6th Edn. New Jersey: Prentice Hall. Open navigation menu. Close suggestions Search Search. User Settings. Skip carousel. Carousel Previous. Carousel Next. What is Scribd? Efa Vs Cfa. Uploaded by Ali Bajwa. Document Information click to expand document information Description: efa vs cfa comparison.
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Search inside document. Table 2 Rotated Component Matrix In summary, the study suggests that there are three underlying dimensions of maternal involvement construct among mothers of a selected Integrated Primary Islamic Schools. Exploratory Factor Analysis versus Confirmatory Factor Analysis Example 2: Principle Component Analysis with Oblique Rotation A brief report writing style for exploratory factor analysis Example 3: Confirmatory Factor Analysis - Single Order Measurement Model A brief report writing style for confirmatory factor analysis Figure 3 shows the results of the three-factor measurement model of Maternal Accountability.
Documents Similar To efa vs cfa. Vaibhav Sehgal. Daniel Sloan. Arwinder Kaur. Vi Keka. Madrid M. Richa Mukhi. Sophie Veys. Silvi Julya Ind. Saurabh Raj. Amsal Id. L N Firmansah. Impact Journals. Chandra Sinniah. It includes 25 personality self report items. The authors built these items following the big 5 personality structure. The first step is to test if the dataset is suitable for carrying out factor analysis. There are two. The test provides probability that the correlation matrix has significant correlations among at least some of the variables in a dataset, a prerequisite for factor analysis to work.
The Kaiser-Meyer-Olkin KMO statistic, which can vary from 0 to 1, indicates the degree to which each variable in a set is predicted without error by the other variables. A value of 0 indicates that the sum of partial correlations is large relative to the sum correlations, indicating factor analysis is likely to be inappropriate. A KMO value close to 1 indicates that the sum of partial correlations is not large relative to the sum of correlations and so factor analysis should yield distinct and reliable factors.
As we can see, the 25 items nicely spread on the 5 latent factors, the famous big 5. Based on this model, we can now predict back the scores for each individual for these new variables:. When running a factor analysis FA , one often needs to specify how many components or latent variables to retain or to extract. This decision is often motivated or supported by some statistical indices and procedures aiming at finding the optimal number of factors.
There are a huge number of methods exist to statistically address this issue, and they can sometimes give very different results. Unfortunately, there is no consensus on which method to use, or which is the best. The Method Agreement procedure, first implemented in the psycho package Makowski , proposes to rely on the consensus of methods, rather than on one method in particular.
One can provide a dataframe, and the function will run a large number of routines and return the optimal number of factors based on the higher consensus.
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