Friday, June 13, 2008

Factor Analysis - Work Orientation Survey

The data used in this project has been taken from the "2005 - Work Orientation" SURVEY CONDUCTED BY the International Social Survey Programme.

Total number of cases/records: 43,440

No. of variables: 91

In the survey, questions were asked on job perception, job satisfaction, working conditions, job content, job commitment, etc. Which of these job parameters/variables are strongly related? Which of them can be grouped together? Which scores/ratings should be used to measure a respondent's overall job satisfaction, job commitment, job security etc.?

The original data was in text format and it was read using the SAS column input method. Based on the analysis objective, out of the total 43,440 records, only employed (both full-time & part-time) respondents were selected for the analysis.

In the employed data, there are 24268 records. And out of the 91 variables, all country specific variables were removed from the final dataset. From the remaining variables, 38 rating/likert scale variables were selected.

Assuming these as ordinal variables, the spearman rank correlation was considered to be the most appropriate correlation for generating the correlation matrix/output data which will be used for running the Factor Analysis. Based on the MSA values and the significant factor loadings, 8 variables were removed during the analysis procedures.

An interesting thing turned up while using both the default Pearson's and the more appropriate Spearman's correlation in the analysis. When the Spearman correlation was used, 8 factors were extracted. But when I tried to summarize the variables based on these factors, I was not satisfied as the variables have been divided into too many small groups without any pattern or consistency in their meanings.

But when I used the default Pearson's correlation (in proc factor), I got 4 factors only. But the best thing was that the related variables have been grouped together under each of these factors. For example - Job content, Job Security, Work-Life Balance, & Job Satisfaction were clubbed together. While Work Environment, Working Relations, Organization Image & Job Commitment came under one factor.

The second approach of using Pearson's correlation while running Factor Analysis was thus found to give a much better, useful, and meaningful result in spite of what textbooks say about using Pearson's correlation on rating scale variables.

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