# Gender College Study | Education Dissertations

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Date added: 17-06-26

**Category:** Statistics

Tags: Gender Essay

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.

### 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 td> |

### 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

H_{01}:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and rank.

H_{a1}: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

H_{02}:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status.

H_{a2}: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

H_{03}:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status.

H_{a3}: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.