Get Help to Conduct Exploratory Factor Analysis in SPSS

Factor analysis is a collection of methods whose aim is to define the underlying structure of a data set and achieve the objectives of summarizing the data. The goal of factor analysis is to describe the relationship between variables in terms of unobservable random qualities called factors. Our professional experts offer help to conduct exploratory factor analysis in SPSS services for our clients. The main focus of this article is the exploratory factor analysis and a highlight of the key areas, such as how to run an EFA test in the Statistical Package of Social Sciences (SPSS), factor analysis interpretation, and an example for reference.

Factor analysis assumptions

• There are no outliers in the data set.
• Variables should be linear.
• Normality of the data set.
• Adequate sample size. [KMO test is used to test for this]
• Lack of multicollinearity among the variables
• The data set should be metric, i.e., on an interval scale

Types of factor analysis

Factor analysis is divided into two main sections: Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA aims to discover the nature of variables influencing the factors, while CFA tests whether the specified variables are influencing the factors in the predicted way.

How to Run EFA Test in SPSS

To conduct an EFA test in Statistical Package for Social Sciences [SPSS], ensure that all the assumptions are met and follow these steps.

Step 1

Open the file in SPSS and click on Analyze> Dimension reduction > Factor. A window will open, and all the variables will be on the left. Select the variables to be included in the EFA analysis and move them to the variables section on the right side.

Step 2

Click on the Descriptives button. In the section marked Correlation matrix, click on Coefficients and KMO and Barlett's test of sphericity and click Continue. These test for the adequate sample size assumption.

Step 3

Click on the Extractions button. There are many extraction methods but the commonly used ones are Principal Axis Factoring and Principal Components. Choose the preferred one, depending on your analysis.

Step 4

Analyze the correlation matrix. Click on Unrotated factor solution and scree plot. SPSS software allows users to specify the number of variables. Choose variables with eigenvalues that are greater than 1 because those less than 1 do not carry enough information. Click on Continue.

Step 5

Click on the Rotation button and choose the rotation strategy you would like to use. SPSS has five options for the rotation strategy which are varimax, Promax, Equamax, Quartimax, and direct Oblimin. Promax is advisable to use as it is quick and simpler.

Step 6

Click on Options, then in the Coefficient Display Format section, click on Suppress small coefficients and put 0.4 as the value in the box labeled Absolute value below. SPSS hides the values that are < 0.4. To run the analysis, click on Continue, then Ok.

Interpretation of EFA Analysis Output

After the analysis is done, the output opens in a second window called the output file. So, how do you interpret this output?

Stage 1- Testing assumptions

The first step is testing the assumptions for EFA analysis. The correlation efficiency in the correlation matrix should be > 0.3 in magnitude. The presence of an efficiency lesser than 0.3 means that reliable and distinct factors cannot be produced. Check the KMO measure of sample size, which should be 0.6 or above. Barlett's Test of Sphericity should be less than 0.05.

Stage 2- Factor extraction

Only the extracted values are important for interpretation. The popular method for deciding on the extraction of factors is the Kaisers eigenvalue greater than 1 method. All factors less than 1 are extracted, and those greater than 1 are retained. The Scree test can also be used here. It graphically presents the eigenvalues in descending order. It has an elbow point at which the last significant break takes place; only factors above this point should be retained.

Stage 3- Factor Interpretation

The last step is to interpret the factors. Before interpreting, check for cross-loadings. These are items with coefficients greater than 0.04 on more than 1 dimension. Any items that are cross-loading are deleted from the analysis then the factors are interpreted. To interpret the factors, examine the loading pattern to determine the factor with the most influence on each variable. Loadings close to 1 show that the factor influences the variable, while those close to 0 show that the factor has a weak influence on the variable.

Exploratory Factor Analysis Example

A food-industry company hired a data analyst to conduct an EFA to identify the factors that contribute to consumer satisfaction. The analyst designed a questionnaire stating variables such as affordable pricing and quality products. They then conducted an EFA to identify the latent factors contributing to these situations.

Why Choose Our Services for Help to Conduct Exploratory Factor Analysis?

Conducting EFA analysis requires a good understanding of statistical methods and SPSS software. Getting professional help can make this process easier without handling the technical difficulties. So, why should you choose our services for EFA analysis?

1. Our expert statisticians have extensive experience in conducting EFA analysis and using SPSS software. They customize the EFA analysis to your specific research topic and choose extraction and rotation methods suitable to your data set.
2. Our statisticians ensure that your results are reliable by checking for factor analysis assumptions needed to carry out the EFA test. They handle outliers' detection and data cleaning before conducting your analysis.
3. Interpreting EFA results requires knowledge of the interpretation methods. Our statisticians are knowledgeable about these procedures and give a detailed interpretation of your factor loadings, thus helping you understand your data.

Summary

EFA analysis is a statistical method used to reduce a large set of variables to a small set of underlying factors. It simplifies data sets, thus revealing the core structures. EFA helps researchers understand how different things are connected and also discover hidden insights. However, conducting EFA analysis requires a deep understanding of EFA terminologies and SPSS software. Hiring experts to carry out your data analysis is advisable. Our statisticians make the process more manageable, and you are assured of accurate results. Don't let the complexities of EFA analysis hold you back. Contact our experienced statisticians for professional help with data analysis. Request a free quote and get started now.

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