Omera Shahnawaz, as a very quick answer, I recommend you look at the pattern matrix. However, before doing so I think you should ensure that you have conducted EFA according to best practice. Inter alia, that involves using the scree test and parallel analysis to identify the likely number of factors in your data, rotating the factors appropriately, and sequentially removing items that do not load satisfactorily.

All the best with your research.

Hi Omera,

I hope you are doing well! In addition to the recommendation of Robert, you could also force extract one factor (using Principle Axis Factoring) and look at the pattern matrix. You can argue that any item with a loading of an absolute value of less than .4 is not part of the strongest factor of the scale, and therefore not part of the unidimensional version of the scale.

However, because you have a factor structure already picked out, it would be more appropriate to conduct a Confirmatory Factor Analysis. I would recommend using a CFA and then checking correlational residuals to determine which items, if any, are causing the unidimensional model to have a poor global fit.

Hope this helps!

Best,

Bryant

Lots of ways to check for this. A good review article is:

@article{AuerswaldMoshagen2019,

author = {Auerswald, Max and Moshagen, Morten},

year = {2019},

title = {How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions},

journal = {Psychological Methods},

volume = {24},

pages = {468--491},

doi = {10.1037/met0000200},

}

Going by the question, you must be looking at an SPSS solution and were not sure which tale to look at and report.

Pattern matrix. (However, ensure that the EFA is properly executed before reporting the solutions).

Thank you everyone for your response and for helping me out. Using PCA and varimax in rotation, I can't see any pattern matrix appearing in my results.I have been researching on it. An Empirical Comparison of Statistical

Construct Validation Approaches by Sanjay L. Ahire and Sarv Devaraj is suggesting the same technique. I can only see a pattern matrix when using Principal Axis Component and Promax in rotation. I have looked few other papers and they are all suggesting to go for PCA and varimax for unidimensionality.

Omera Shahnawaz, a number of authoritative methodologists recommend AGAINST using PCA as an extraction method and also varimax as a rotation method. I can give you references concerning that if you'd like.

Please don't do what other researchers do just because other researchers do what they do. In my view, a lot of researchers follow each other like sheep.

I suspect you're better off using principal axis factoring as your method of extraction within EFA (PCA is not really exploratory factor analysis), and also that you use an oblique rotation such as promax because your items are likely to be related to each other.

Robert Trevethan Thank you for such a quick response. Can you provide any references as it will help me in understanding.

Omera Shahnawaz, here you go:

Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10, 1–9.

Fabrigar, L. R., Wegener, D. T., MacCallum, R., C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272—299. https://doi.org/10.1037/1082-989X.4.3.272

Gaskin, C. J., & Happell, B. (2014). On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use. International Journal of Nursing Studies, 51(3), 511–521. https://doi.org/10.1016/j.ijnurstu.2013.10.005

Matsunaga, M. (2010). How to factor-analyze your data right: Do’s, don’ts, and how-to’s. International Journal of Psychological Research,3(1), 97–110. https://doi.org/10.21500/20112084.854

I hope they're helpful. Interestingly, despite these articles being quite "old", researchers seem to be unaware of them and, as I wrote in my previous post, researchers perpetuate practices that have been recommended against.

For recent works on FA and EFA, you might might check out the following pieces.

Finch, W. H. (2020). Exploratory factor analysis. SAGE Publications, Inc. https://doi.org/10.4135/9781544339900

Lorenzo-Seva, U., & Ferrando, P. J. (2020). Not positive definite correlation matrices in exploratory item factor analysis: Causes, consequences and a proposed solution. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 138-147. https://doi.org/10.1080/10705511.2020.1735393

Schreiber, J. B. (2021). Issues and recommendations for exploratory factor analysis and principal component analysis. Research in Social and Administrative Pharmacy, 17(5), 1004-1011. https://doi.org/10.1016/j.sapharm.2020.07.027

Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11. https://doi.org/10.12691/ajams-9-1-2