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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

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.

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