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With the empirical approach, numerical RIASEC profiles were developed for all the careers in the database using discriminant functions – a type of classification analysis used to produce probabilities of group membership. In this case, the discriminant analysis would yield probabilities of group membership for each of the RIASEC categories.
A stepwise discriminant variable selection procedure was carried out on 67 predictor variables to identify a smaller predictor set. (Predictor variables included data on worker functions, general educational development, aptitudes, temperaments, GOE codes, physical demands, and environmental conditions.) The analysis was carried out using two independent development samples – one from a study conducted by Holland in 1982 and the other from a presentday study of 150 careers. The stepwise selection procedure considered the relative contribution of each of the 67 variables with respect to overall discriminatory power of the model (as measured by Wilks’ Lambda). After analysis, seven predictor variables were chosen for the final model, based on their discriminatory power as well as performance of overall error rates and average squared canonical correlations. The final discriminant model was then run on all the careers in the database using the 7 identified predictor variables.
In general, this approach was very successful in identifying primary group membership, but there were concerns about the reliability of nonprimary group probabilities. This statistical method is generally applied to the identification of single group membership, rather than probabilities across multiple categories. Therefore, a complementary approach would be needed to assess and verify numerical profiles across the all six RIASEC categories, including both primary and nonprimary groups.






Judgment Method 


In order to overcome the inherent limitations of the empirical approach, an additional approach, called the judgment method, was used to generate numerical profiles. For this method, three expert judges rated occupations on how descriptive and characteristic they were of each of the six RIASEC work environments. Using a sevenpoint scale, the judges assessed the RIASEC categories for each career in the database. Profiles were then developed from the mean scores of the three judges and submitted for review by a secondary panel.
In general, the ratings from trained judges proved both effective and reliable. In particular, it proved more effective than the empirical method in deriving numerical profiles for the five nonprimary RIASEC categories. Statistically, the posterior probability levels compared to the discriminant method were much less extreme among the six RIASEC categories, providing reasonable proportions for all six categories. Crossclassification tables and values for Cohen’s Kappa were also examined to assess the degree of agreement between the two methods. Cohen’s Kappa is a measure of agreement between method pairs that involve unordered categories, with higher values indicating a higher level of agreement, and values above .70 considered acceptable. The two methods described here had a Kappa of .72.





















Interesting Fact 



The state with the largest agricultural industry is California, which has a production more than double the secondplace state. 



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