Gender College Study

Sample details Pages: 14 Words: 4194 Downloads: 7 Date added: 2017/06/26 Category Statistics Essay Tags: Gender Essay Did you like this example? This chapter presents the results of the study. Included are an analysis of the five research questions and the six hypotheses of the study. This chapter concludes with a summary of the information presented in this chapter concerning the quantitative statistical findings of this study. As previously indicated, job satisfaction is a term that is difficult to describe as a single construct, and the definition of job satisfaction varies between studies (Morice Murray, 2003; Protheroe, Lewis Paik, 2002; and Singer, 1995). In higher education, a number of researchers have discussed the importance of continuous research on job satisfaction among community college faculty (Bright, 2002; Green, 2000; McBride, Munday, Tunnell, 1992; Milosheff, 1990; Hutton Jobe, 1985; and Benoit Smith 1980). A reason suggested for the continuous study of community college faculty, is the value of data received from such studies in developing and improving community college faculty and their practices (Truell, Price, Joyner, 1998). The purpose of this study was to examine job satisfaction of community college instructional faculty in regards to their role as teachers. Don’t waste time! Our writers will create an original "Gender College Study | Education Dissertations" essay for you Create order Analysis of Research Questions Research question one sort to describe the sociodemographic characteristics of community college instructional faculty. This research question included three variables (gender, age, and race/ethnicity). Sociodemographic Characteristics Gender There were 371 participants in the sample, of which 188 were male and 183 were female. In regards to gender, the analysis showed that 51% of the sample size included males and 49% of the sample size were female. Table 1 identifies the frequency and percentage results as they relate to gender of community college faculty. Table 1. Gender Distribution of Community College Instructional Faculty Gender Percent Frequency Male 51% 188 Female 49% 183 Total 100% 371 Age The sample size consisted of 371 participants. For age, the analysis displayed that 16% of the faculty were both under 30 and between ages 30 and 34 while17% were between ages 35 and 39. 15% of community college instructional faculty were between 40 and 44, while 14% were in the age range of 45 to 50. The last age range consisted of participants who were 50 or over, which was 21%. Even though the largest percentage of faculty members are 50 or over, faculty members who are 34 or under total 32% which indicates that the majority of faculty are under the age of 34. Table 2 identifies the frequency and percentage results as they relate to the variable of age of community college faculty. Table 2. Age Distribution of Community College Instructional Faculty Age Percent Frequency Under 30 16% 60 30-34 16% 60 35-39 17% 65 40-44 15% 57 45-49 14% 51 50 and over 21% 79 Total 100% 371 Race and Ethnicity The sample size consisted of 371 participants. The variable race/ethnicity showed that 83% of the participants were White, Non-Hispanic; 7% were Black, Non-Hispanics; 3% were Asian, Non-Hispanics; 1% were both American Indian, Non-Hispanics and Pacific Islanders Non-Hispanics; 2% were More than one race, Non-Hispanic; and 5% were Hispanics. Over 80% of the participants (308) were White, Non-Hispanic. Table 3 identifies the frequencies and percentages for the variable of race/ethnicity. Table 3. Race/Ethnicity of Community College Instructional Faculty Race/Ethnicity Percent Frequency White, Non-Hispanic 83% 308 Black, Non-Hispanic 7% 25 Asian, Non-Hispanic 3% 11 American Indian, Non-Hispanic 1% 1 Pacific Islanders, Non-Hispanic 1% 1 More than one race, Non-Hispanic 2% 7 Hispanics 5% 18 Total 100% 371 Research question two sort to describe the nature of employment characteristics of community college instructional faculty. This research question included three variables (rank, employment status, and tenure status). Nature of Employment Characteristics Employment Status There were 371 participants in the sample, of which 126 were employed full time and 245 were employed part time. In regards to employment status, the analysis showed that 34% of the sample size was employed full time and 66% of the sample size were employed part time. Table 4 identifies the frequency and percentage results as it relates to employment status of community college faculty. Table 4. Employment Status Distribution of Community College Instructional Faculty Employment Status Percent Frequency Full time 34% 126 Part time 66% 245 Total 100% 371 Rank The sample size consisted of 371 participants. In regards to rank, the analysis displayed that 9% of the sample size was identified as professors. Associate professors were identified at 5% of the sample size while Assistant professors were identified at 4%. Instructors were identified as 45% of the participants and lecturers were identified at 2%. Faculty with other titles were identified at 30% and 5% of the participants answered the question as not applicable. More than 40% of the participants (167) were identified as instructors. Table 5 identifies the frequency and percentage results as they relate to the ranking of community college faculty. Table 5. Rank Distribution of Community College Instructional Faculty Rank Percent Frequency Professor 9% 30 Associate professor 5% 19 Assistant professor 4% 15 Instructor 45% 167 Lecturer 2% 7 Other titles 30% 111 Not applicable 5% 22 Total 100% 371 Tenure Status The sample size consisted of 371 participants. In regards to tenure status, the analysis showed that 18% of the faculty were tenured; 6% of faculty were on a tenure track, but are not tenured; and 76% of faculty are not on a tenure track. More than 70% of the participants (282) were identified as faculty not on a tenure track. Table 6 identifies the frequency and percentage results as they relate to the tenure status of community college faculty. Table 6. Tenure Status of Community College Instructional Faculty Tenure Status Percent Frequency Tenured 18% 67 On tenure track, but not tenured 6% 22 Not on tenure track 76% 282 Total 100% 371 Job Satisfaction of Community College Instructional Faculty Research question three was designed to describe the job satisfaction of community college instructional faculty based on the eight components (Authority to make decisions; Benefits; Equipment/facilities; Instructional support; Overall; Salary; Technology-based activities; and Workload) of job satisfaction from the National Study of Postsecondary Faculty Survey NSOPF: 04. The sample size consisted of 366 participants. In regards to job satisfaction, the analysis showed that 73% of the faculty were very satisfied with authority to make decision; 34% of faculty were somewhat satisfied with benefits; 44% of faculty were very satisfied with equipment and facilities; 40% were somewhat satisfied with instructional support; 55% were very satisfied with overall job satisfaction; 42% were somewhat satisfied with salary; 53% were very satisfied with technology-based activities; and 50% of faculty were very satisfied with workload. Table 6 identifies the frequency and percentage results as they relate to the job satisfaction of community college faculty. Table 7. Job Satisfaction of Community College Instructional Faculty Satisfaction Percent Frequency Authority to Make Decisions Very satisfied 73% 268 Somewhat satisfied 22% 81 Somewhat dissatisfied 4% 14 Very dissatisfied 1% 4 Total 100 366 Benefits Very satisfied 27% 106 Somewhat satisfied 34% 127 Somewhat dissatisfied 19% 70 Very dissatisfied 18% 67 Total 100 371 Equipment/facilities Very satisfied 44% 161 Somewhat satisfied 38% 140 Somewhat dissatisfied 14% 51 Very dissatisfied 4% 15 Total 100 366 Instructional support Very satisfied 37% 134 Somewhat satisfied 40% 147 Somewhat dissatisfied 17% 62 Very dissatisfied 6% 23 Total 100 366 Job overall Very satisfied 55% 203 Somewhat satisfied 38% 141 Somewhat dissatisfied 6% 22 Very dissatisfied 1% 5 Total 100 371 Salary Very satisfied 29% 106 Somewhat satisfied 42% 157 Somewhat dissatisfied 18% 67 Very dissatisfied 11% 41 Total 100 371 Technology-based activities Very satisfied 53% 195 Somewhat satisfied 35% 129 Somewhat dissatisfied 9% 32 Very dissatisfied 3% 10 Total 100 366 Workload Very satisfied 50% 187 Somewhat satisfied 34% 127 Somewhat dissatisfied 11% 41 Very dissatisfied 4% 17 Total 100 371 Predictive Relationship between Sociodemographic Characteristics, Nature of Employment Characteristics and Job Satisfaction Research questions four and five examined the predictive relationship between gender, nature of employment, (rank, employment status, and tenure status) and job satisfaction of community college instructional faculty. Associated with this research question were six hypotheses. The hypotheses were tested using a multiple linear regression model that included two independent variables (gender and rank, gender and employment status, and gender and tenure status) and the eight components of the dependent variable, job satisfaction (Authority to make decisions regarding instructional practice, Benefits, Equipment/facilities for instructional use, Instructional support, Overall satisfaction, Salary, Technology-based activities, and Workload). The findings for each of the hypotheses are discussed below. Gender, Rank, and Job Satisfaction H01:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and rank. Ha1:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and rank. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = 0.280, p = .756 (See Table 8). A non-significant relationship was found between gender, rank, and component one. The coefficients were: t = -.321 (gender) and -.670 (rank), df = 363, and p .05 for both gender (.748) and rank (.504). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 8. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .234 2 .117 .280 .756 Residual 151.878 363 .418 Corrected Total 152.112 365 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Benefits), F (2, 363), = 4.203, p = .016. The total model produced an r-square value of 0.023 (See Table 9). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .050 (gender) and 2.897 (rank), df = 363, and p .05 for gender (.960) and p.05 for rank (.004). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 9. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F p Regression 9.431 2 4.716 4.203 .016 Residual 407.247 363 1.122 Corrected Total 416.678 365 R-Square = 0.023 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 1.045, p = .353. The total model produced an r-square value of 0.006 (See Table 10). The r-square value indicated that approximately 1% of the variation in equipment/facilities for instructional use was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .793 (gender) and -1.225 (rank), df = 363, and p .05 for both gender (.428) and rank (.221). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Instructional support), F (2, 363), = .370, p = .691. The total model produced an r-square value of 0.002 (See Table 11). Table 10. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 1.441 2 .721 1.045 .353 Residual 250.187 363 .689 Corrected Total 251.628 365 R-Square = 0.006 The r-square value indicated that approximately 1% of the variation in instructional support was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .392 (gender) and -.773 (rank), df = 363, and p .05 for both gender (.695) and rank (.440). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 11. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression .570 2 .285 .370 .691 Residual 279.804 363 .771 Corrected Total 280.374 365 R-Square = 0.002 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = 13.505, p = .000. The total model produced an r-square value of 0.069 (See Table 12). The r-square value indicated that approximately 1% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = -5.169 (gender) and -.436 (rank), df = 363, and p .05 for gender (.000) and p .05 for rank (.663). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 12. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression 19.269 2 9.634 13.505 .000 Residual 258.950 363 .713 Corrected Total 278.219 365 R-Square = 0.069 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Salary), F (2, 363), = .050, p = .951. The total model produced an r-square value of 0.000 (See Table 13). The r-square value indicated that approximately 0% of the variation in salary was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .220 (gender) and -.230 (rank), df = 363, and p .05 for gender (.826) and for rank (.818). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = .050, p = .819. Table 13. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .091 2 .045 .050 .951 Residual 331.857 363 .914 Corrected Total 331.948 365 R-Square = 0.000 The total model produced an r-square value of .001 (See Table 14). The r-square value indicated that approximately 0% of the variation in technology based activities was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .081 (gender) and -.628 (rank), df = 363, and p .05 for both gender (.936) and rank (.531). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 14. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression .245 2 .123 .199 .819 Residual 223.219 363 .615 Corrected Total 223.464 365 R-Square = 0.001 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Workload), F (2, 363), = .557, p = .573. The total model produced an r-square value of 0.003 (See Table 15). The r-square value indicated that approximately 0% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .312 (gender) and -1.015 (rank), df = 363, and p .05 for both gender (.756) and rank (.311). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 15. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 1.218 2 .609 .557 .573 Residual 396.607 363 1.093 Corrected Total 397.825 365 R-Square = 0.003 Gender, Employment Status, and Job Satisfaction H02:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status. Ha2:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = .070, p = .932 (See Table 16). A non-significant relationship was found between gender, employment status, and component one. The coefficients were: t = -.355 (gender) and .120 (employment status), df = 363, and p .05 for both gender (.723) and employment status (.904). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 16. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .040 2 .020 .070 .932 Residual 104.091 363 .287 Corrected Total 104.131 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Benefits), F (2, 363), = 26.952, p = .000. The total model produced an r-square value of 0.129 (See Table 17). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = -.140 (gender) and 7.340 (employment status), df = 363, and p .05 for gender (.889) and p.05 for employment status (.000). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 2.754, p = .065 (See Table 18). Table 17. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F P Regression 51.741 2 25.870 26.952 .000 Residual 348.437 363 .960 Corrected Total 400.178 365 R-Square = 0.129 The coefficients were: t = -.016 (gender) and -2.347 (employment status), df = 363, and p .05 for gender (.987) and p .05 for employment status (.019). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 18. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 3.331 2 1.665 2.754 .065 Residual 219.489 363 .605 Corrected Total 222.820 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Instructional support), F (2, 363), = 1.844, p = .160 (See Table 19). The coefficients were: t = -.308 (gender) and -1.897 (employment status), df = 363, and p .05 for gender (.758) and p .05 for employment status (.059). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 19. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression 2.651 2 1.326 1.844 .160 Residual 260.977 363 .719 Corrected Total 263.628 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = .637, p = .529. The total model produced an r-square value of 0.003 (See Table 20). The r-square value indicated that approximately 0% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = -.652 (gender) and -.924 (employment status), df = 363, and p .05 for both gender (.515) and employment status (.356). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Salary), F (2, 363), = .058, p = .944 (See Table 21). The coefficients were: t = .260 (gender) and -.216 (employment status), df = 363, and p .05 for gender (.795) and for employment status (.829). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 20. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression .516 2 .258 .637 .529 Residual 146.916 363 .405 Corrected Total 147.432 365 R-Square = 0.003 Table 21. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .100 2 .050 .058 .944 Residual 315.441 363 .869 Corrected Total 315.541 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = .529, p = .589 (See Table 22). The coefficients were: t = -.334 (gender) and -.975 (employment status), df = 363, and p .05 for both gender (.739) and employment status (.330). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Workload), F (2, 363), = 13.418, p = .000. Table 22. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression .523 2 .261 .529 .589 Residual 179.130 363 .493 Corrected Total 179.653 365 The total model produced an r-square value of 0.069 (See Table 23). The r-square value indicated that approximately 1% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = 1.351 (gender) and -4.995 (employment status), df = 363, and p .05 for gender (.178) and p .05 for employment status (.000). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 23. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 17.895 2 8.947 13.418 .000 Residual 242.062 363 .667 Corrected Total 259.956 365 R-Square = 0.069 Gender, Tenure Status, and Job Satisfaction H03:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status. Ha3:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = 0.120, p = .887 (See Table 24). A non-significant relationship was found between gender, tenure status, and component one. The coefficients were: t = -.442 (gender) and .222 (tenure status), df = 363, and p .05 for both gender (.659) and tenure status (.825). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 24. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .073 2 .037 .120 .887 Residual 110.465 363 .304 Corrected Total 110.538 365 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Benefits), F (2, 363), = 9.706, p = .000. The total model produced an r-square value of 0.051 (See Table 25). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = .015 (gender) and 4.405 (tenure status), df = 363, and p .05 for gender (.988) and p.05 for tenure status (.000). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 25. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F p Regression 20.959 2 10.479 9.706 .000 Residual 391.916 363 1.080 Corrected Total 412.874 365 R-Square = 0.051 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 3.790, p = .024. The total model produced an r-square value of 0.020 (See Table 26). The r-square value indicated that approximately 1% of the variation in equipment/facilities for instructional use was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = .247 (gender) and -2.746 (tenure status), df = 363, and p .05 for gender (.805) and p .05 tenure status (.006). Therefore, the null hypothesis was rejected because p .05 p.05 with alpha= .05. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Instructional support), F (2, 363), = 2.705, p = .068. Table 26. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 4.463 2 2.232 3.790 .024 Residual 213.758 363 .589 Corrected Total 218.221 365 R-Square = 0.020 The total model produced an r-square value of 0.015 (See Table 27). The r-square value indicated that approximately 1% of the variation in instructional support was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.201 (gender) and -2.313 (tenure status), df = 363, and p .05 for both gender (.841) and p .05 tenure status (.021). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 27. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression 3.868 2 1.934 2.705 .068 Residual 259.599 363 .715 Corrected Total 263.467 365 R-Square = 0.015 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = .511, p = .600. The total model produced an r-square value of 0.003 (See Table 28). The r-square value indicated that approximately 0% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.484 (gender) and -.878 (tenure status), df = 363, and p .05 for both gender (.629) and for tenure status (.381). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 28. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression .391 2 .196 .511 .600 Residual 139.084 363 .383 Corrected Total 139.475 365 R-Square = 0.003 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Salary), F (2, 363), = .164, p = .849. The total model produced an r-square value of 0.001 (See Table 29). The r-square value indicated that approximately 0% of the variation in salary was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.485 (gender) and -.296 (tenure status), df = 363, and p .05 for gender (.628) and for tenure status (.767). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 29. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .269 2 .135 .164 .849 Residual 297.286 363 .819 Corrected Total 297.555 365 R-Square = 0.001 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = 13.722, p = .000. The total model produced an r-square value of .070 (See Table 30). The r-square value indicated that approximately 1% of the variation in technology based activities was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = 2.061 (gender) and -4.855 (tenure status), df = 363, and p .05 for both gender (.040) and tenure status (.000). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Workload), F (2, 363), = 6.544, p = .002. The total model produced an r-square value of 0.035 (See Table 31). The r-square value indicated that approximately 1% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = 1.140 (gender) and -3.455 (tenure status), df = 363, and p .05 for gender (.255) and p .05 for tenure status (.001). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 30. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression 16.535 2 8.267 13.722 .000 Residual 218.700 363 .602 Corrected Total 235.235 365 R-Square = 0.070 Table 31. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 8.363 2 4.182 6.544 .002 Residual 231.946 363 .639 Corrected Total 240.309 365 R-Square = 0.035 Summary The finding of this study showed that the gender of community college instructional faculty was almost equally distributed. In that, 51% were male and 49% were female. Apparently, community colleges are providing instructional opportunities not only for men, but also for women. The findings also showed that the majority of community college instructional faculty were below the age of thirty-four making a combined percentage of 32% for the age ranges of 34-30 and 30 and under, although 21% of community college instructional faculty are fifty years of age or over. Assuming a retirement age of 65, these data indicate the approximately 130 out 371 community college instructional faculty will have to be replaced in the next 15 years. This study also found that the community college instructional faculty ethnic make-up is White, Non-Hispanic at 83%. This indicates that the race of community college instructional faculty has a limited minority presence. Other findings from this study, such as employment status, showed that 66% of community college instructional faculty were employed in part-time status. This is consistent with findings in the literature regarding employment status. The findings also showed that 75% of community college instructional faculty were identified as instructors or had other titles. Since this study was examining the job satisfaction of community college instructional faculty regarding their role as teachers, the finding are not surprising that faculty viewed themselves as instructors. Finally, the finding for research question one, as it relates to tenure status showed that 76% of community college instructional faculty were not on a tenure track. The finding for research question three yielded that community college instructional faculty were either somewhat or very satisfied with all eight components (Authority to make decisions; Benefits; Equipment/facilities; Instructional support; Overall; Salary; Technology-based activities; and Workload) of job satisfaction ranging from 61% to 95%, with Benefits fairing the least at 61%. The results of the regression analysis conducted in this study showed that no significant relationship existed between gender and nature of employment (rank, employment status, and tenure status), and job satisfaction. All three hypotheses were tested at the .05 level of significance. The findings of this study revealed that none of the independent variables are predictive of job satisfaction of community college instructional faculty. The next chapter will present discussion, conclusions, implications, and recommendations of this study.