|Abstract or Summary
- The purpose of the study was to identify a list of skills, knowledges and related factors that are common to fourteen occupational cluster areas representative of the work community of Oregon. PROCEDURES A list of skills, knowledges and related factors needed for successful employment was derived from existing task analyses of the
key occupations within the fourteen cluster areas. A Jury Panel of Experts refined the list into the 59 response items utilized on the Likert survey instrument. Respondents were asked to prioritize the five major reasons why people are terminated from employment. The sample consisted of 386 firms representing fourteen occupational areas which were Accounting, Agriculture, Child Care, Construction, Electricity/Electronics, Food Service, Forest Products, Graphic
Communications, Health Occupations, Industrial Mechanics, Marketing, Metals, Secretarial-Clerical and Service Occupations. The sample was stratified by firm size and region. A table of random numbers was utilized to select sample firms within the size and geographical groupings. Three hypotheses were tested by analysis of variance procedures. The hypotheses were:
HoI: There is no significant difference in skills, knowledges and related factors needed for successful employment among fourteen occupational cluster areas as identified
by employers of representative firms. Ho2: There is no significant difference in skills, knowledges and related factors for successful employment among five size groupings of firms as identified by employers of representative firms. Ho3: There is no significant interaction effects between the fourteen cluster areas and the five size groupings of
firms. P <.01 was selected as an indicator of significance in evaluating the data. Other statistical treatments utilized in evaluating the data were Least Significant Difference Test, Scheff4 Test, Newman-Keuls Test and Pearson Product-Moment Correlation Coefficients. FINDINGS Significant differences determined by the analysis of variance were: 1. Forty-six of the fifty-nine criterion statements rejected the null hypotheses for cluster area. 2. The null hypotheses was retained for all criterion
statements for size of firm. 3. Two of the fifty-nine response items rejected the null
hypothesis for interaction effects between firm size and cluster. A one-way analysis of variance was conducted to determine if
differences exist between geographic location of firms. There were no regional differences at the P <.O1 level of significance. The fifty-nine criterion statements were individually evaluated according to their acceptance by the respondents. On the basis of these evaluations, the following findings were made: 1. Fifteen criterion statements were highly accepted by all fourteen occupational cluster groups and designated as
extremely important to job success. 2. Twenty-two criterion statements with high level of acceptance among most occupational areas were designated as very important. 3. Fifteen criterion statements with above average acceptance were designated as moderately important. 4. Seven criterion statements were below average in overall
acceptance and designated as slightly important. Each respondent was asked to prioritize the five most common reasons why persons were fired by their firm. In rank order these were: poor work performance, 37.6 percent; absenteeism, 25.7 percent; insubordination, 13.3 percent; dishonesty, 11.1 percent; inability to work with others, 9.6 percent; and miscellaneous, 2.9 percent.
RECOMMENDATIONS On the basis of this study, it is recommended that: 1. Task analyses be reviewed routinely to assure that cluster
curriculum is up-to-date and flexible. 2. Cluster programs in Oregon consider including in their curriculum all criterion statements identified as extremely important. 3. Cluster programs in Oregon review, revise and include
in curriculum the criterion statements identified as very important. 4. Cluster programs in Oregon should include, in their
curriculum, methods by which students can practice what they are learning enabling them to judge the difference between good work performance and poor work performance.