Factor Analysis as a Statistical Method - Statswork

## Factor analysis support from Experts at Stats work

### Factor analysis support from Experts at Stats work

Factor analysis is used to extract the factors from independent variables. Generally, this analysis is used in developing questionnaires. In case if your data contains so many variables, you can perform this analysis in order to reduce the number of variables from the data. This analysis groups variables with similar features together. The reduced factors can be used for further analysis.

### What are the values are extracted from this test and their usage?

• Kaiser-Meyer-Olkin measure must be greater than 0.5
• Bartlett’s test of sphericity should has the p-value less than 0.05
• From the total variance explained table, we can estimate the amount of variance explained by each factor.

### What Statistical consultation Services we offer in Factor analysis support?

In case if you have to do the analysis based on the items of questionnaire and the questionnaire contains so many items and you are required to extract the factors from your questionnaire data, then you can use the factor analysis. In order to determine underlying dimensions of multi-item measurement scales used in this study, principal components analysis with varimax rotation using SPSS 20.0 was performed for all constructs in the analysis: meaningful work, sense of community, alignment with organizational values and mission, job satisfaction, organisational commitment and adaptability. Minimum Eigen values of 1.0 were used to determine the number of factors for each scale and with loading above 0.50 on a single factor was retained. Initially, the factorability of 30 items was examined.

The above table presents the results of the factor analysis and a detailed description of each item for each of the six main factors. Factor loadings ranged from .89 to 0.442. All the factors accounted for 61-76% of the variance.

The screen plot graphs the eigenvalue against the factor number. From the sixth factor on, you can observe that the line is almost flat, meaning the each successive factor is accounting for smaller and smaller amounts of the total variance.