# Study Of Demographic Profile Of The Respondents Accounting Essay

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In this chapter, result is obtained through the data collection method from 300 questionnaires. The data obtained from questionnaires are analyzed and computed by using the Statistical Package social Science (SPSS) software. Tables and charts will be exhibited to present the results in an understandable manner. This chapter started off with the descriptive analysis of the respondent demographic profile and central tendencies measurement of constructs. Next, scale measurement is to provide the results of reliability test. This is followed by the inferential analysis with the Pearson correlation coefficient analysis and multiple linear regression analysis. Lastly is to conclude this chapter.

## 4.1 Descriptive Analysis

## 4.1.1 Demographic Profile of the Respondents

Table 4.1: Gender of Respondents

## Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Male

105

51.7

51.7

51.7

Female

98

48.3

48.3

100.0

Total

203

100.0

100.0

Source: Developed for the research

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In the Table 4.1 above, it showed the frequency and percentage of gender of 203 respondents. There were a total of 105 male respondents (51.7%) and a total of 98 female respondents (48.3%).

Table 4.2: The Age Group of Respondents

## Age

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Under 21 years

5

2.5

2.5

2.5

21-30 years

67

33.0

33.0

35.5

31-40 years

54

26.6

26.6

62.1

41-50 years

31

15.3

15.3

77.3

51 years & above

46

22.7

22.7

100.0

Total

203

100.0

100.0

Source: Developed for the research

Table 4.2 presented the frequency and percentage of the age group of respondents. 5 respondents (2.5%) were under 21 years old, 67 respondents (33.0%) were 21 to 30 years old, 54 respondents (26.6%) were 31 to 40 years old, 31 respondents (15.3%) were 41 to 50 years old and 46 respondents (22.7%) were 51 years old and above.

Table 4.3: The Marital Status of Respondents

## Marital Status

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Single

73

36.0

36.0

36.0

Married

130

64.0

64.0

100.0

Total

203

100.0

100.0

Source: Developed for the research

The Table 4.3 indicated the frequency and percentage of the marital status of respondents. The majority of the respondents were married which represented 64.0% that is about 130 people out of 203 respondents. Besides that, 73 respondents (36.0%) were still single.

Table 4.4: The Education Level of Respondents

## Education Level

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Primary education

8

3.9

3.9

3.9

Secondary education

77

37.9

37.9

41.9

Certificate / Diploma

80

39.4

39.4

81.3

Bachelor's degree

33

16.3

16.3

97.5

Master's degree

5

2.5

2.5

100.0

Total

203

100.0

100.0

Source: Developed for the research

From the Table 4.4, it showed the frequency and percentage of the education level of respondents. From the information above, majority of 80 respondents held a certificate or diploma (39.4%). The second and third largest groups were 77 respondents who had completed the secondary education (37.9%) and 33 respondents who held a bachelor degree (16.3%). Lastly, the respondents who had completed the primary education and which is a master degree holders were 8 people (3.9%) and 5 people (2.5%) respectively.

Table 4.5: The Working Period of Respondents

## Working Period

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Less than 1 year

23

11.3

11.3

11.3

1-5 years

63

31.0

31.0

42.4

6-10 years

48

23.6

23.6

66.0

11-15 years

25

12.3

12.3

78.3

16-20 years

19

9.4

9.4

87.7

Above 20 years

25

12.3

12.3

100.0

Total

203

100.0

100.0

Source: Developed for the research

The Table 4.5 represented the frequency and percentage of the working period of respondents. The respondents were divided into six categories. 23 respondents worked as an insurance agent for less than one year (11.3%), 63 respondents worked for 1 to 5 years (31.0%), 48 respondents worked for 6 to 10 years (23.6%), 25 respondents worked for 11 to 15 years (12.3%), 19 respondents worked for 16 to 20 years (9.4%) and 25 respondents worked as an insurance agent above 20 years (12.3%).

## 4.1.2 Central Tendencies Measurement of Constructs

## 4.1.2.1 Commission Pay

Table 4.6: Central Tendencies Measurement for Commission Pay

No.

Statement

Mean

Standard Deviation

CP1

I think the commission pay is a very important motivator for me.

4.3448

0.75078

CP2

I am motivated by commission pay to try my best efforts in work.

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4.2562

0.73350

CP3

My job performance determine the additional commission pay when achieve higher target.

4.2808

0.81141

CP4

I will retain the job because I am satisfied with the commission pay.

4.0985

0.85025

CP5

I work hard is to get a better commission pay in this job.

4.3005

0.78561

Source: Developed for the research

Table 4.6 illustrated the central tendencies measurements of commission pay. Referring to the table above, most respondents agreed to the CP1 with mean value of 4.3448. The second highest was CP5 with mean value of 4.3005. The third and fourth highest were CP3 and CP2 which had the mean value of 4.2808 and 4.2562 respectively. The lowest mean value was CP4 which was 4.0985.

From the table above, CP4 had highest standard deviation value which was 0.85025. The second and third highest standard deviation value was CP3 and CP5 which had 0.81141 and 0.78561 respectively. The fourth highest standard deviation was CP1 with the value of 0.75078. The lowest standard deviation value was 0.73350 by CP2.

## 4.1.2.2 Job Security

Table 4.7: Central Tendencies Measurement for Job Security

No.

Statement

Mean

Standard Deviation

JSM1

I think job security is another important motivator for me.

4.1232

0.68188

JSM2

I am motivated by the good job security in current job.

3.9852

0.74779

JSM3

I feel my current job is secure, reliable and permanent.

3.9951

0.82951

JSM4

My job performance is influenced by the job security.

4.0000

0.79603

JSM5

I work hard is because I'm afraid to loss this stable job.

3.7143

1.00845

Source: Developed for the research

The Table 4.7 illustrated the central tendencies measurements of job security. The result from the table above, JSM1 had the highest mean value of 4.1232. The second highest mean value was JSM4 which was 4.000. The third highest was JSM3 with the mean value of 3.9951 which was followed by JSM2 which had a mean value of 3.9852. The JSM5 had the lowest mean value of 3.7143.

According to the Table 4.7, the JSM5 had the highest standard deviation value of 1.00845. The second highest standard deviation value was JSM3 which was 0.82951. The third highest was JSM4 with the standard deviation value of 0.79603 which was followed by JSM2 which had a standard deviation value of 0.74779. The lowest standard deviation value was JSM1 which was only 0.68188.

## 4.1.2.3 Opportunities for Advancement and Development

Table 4.8: Central Tendencies Measurement for Opportunities for Advancement and Development

No.

Statement

Mean

Standard Deviation

AD1

I think the opportunities for advancement and development is another important motivator for me.

4.1034

0.79238

AD2

I am motivated by opportunities for advancement and development in current job.

4.0443

0.75321

AD3

I have more opportunities for advancement and development in current job if I retain the job for longer period of time.

4.0936

0.78734

AD4

I have more opportunities to send for training, learn skill and developing myself in current job.

4.1084

0.81330

AD5

I work hard is to get more opportunities for advancement and development in this job.

4.1675

0.74563

Source: Developed for the research

Table 4.8 illustrated the central tendencies measurements of opportunities for advancement and development. In the term of mean value, AD5 had the highest mean of 4.1675. Secondly, it is followed by AD4 of 4.1084, AD1 of 4.1034 and AD3 of 4.0936. The AD2 had the lowest mean value of 4.0443.

From the Table 4.8, the AD4 had the highest standard deviation value of 0.81330. Secondly, it is followed by AD1 with the standard deviation value of 0.79238, AD3 with the standard deviation value of 0.78734 and AD2 with the standard deviation value of 0.75321. The lowest standard deviation value was 0.74563 by AD5.

## 4.1.2.4 Work Itself

Table 4.9: Central Tendencies Measurement for Work Itself

No.

Statement

Mean

Standard Deviation

W1

I have more independent when I'm working.

4.1724

0.78654

W2

I have more confidence to achieve the target of the job.

4.2217

0.75461

W3

I am willing to work hard with current job.

4.2069

0.72886

W4

I am not willing to change another job even if I get other job in elsewhere.

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0.98909

W5

I think my ability can perform well in current job.

4.1527

0.75211

W6

I am proud to work with current job.

4.2020

0.77944

Source: Developed for the research

The Table 4.9 above, it illustrated the central tendencies measurements of work itself. Based on the table, W2 had the highest mean value of 4.2217. The second highest was W3 that had 4.2069 of mean value. The third highest was the W6 that had 4.2020 of mean value which was followed by, the W1 and W5 which had the mean value of 4.1724 and 4.1527 respectively. The W4 had the lowest mean value was 3.8227.

In the table above, the highest standard deviation value was 0.98909 by the W4. The second highest standard deviation value was W1 which was 0.78654. The third highest standard deviation value was the W6 that had 0.77944 which was followed by, the W2 and W5 which had the standard deviation value of 0.75461 and 0.75211 respectively. The lowest standard deviation value was W3 which was 0.72886.

## 4.1.2.5 Job Satisfaction

Table 4.10: Central Tendencies Measurement for Job Satisfaction

No.

Statement

Mean

Standard Deviation

JS1

In general I am satisfied with my job.

4.1330

0.74265

JS2

I am satisfied with the flexibility of the working hours in current job.

4.3054

0.68601

JS3

I am satisfied with the commission pay scheme in current job.

4.0394

0.78250

JS4

I will retain the job because I am satisfied with the job security.

4.0148

0.74779

JS5

I am satisfied with the achievement in current job.

4.0591

0.78125

JS6

I am happy with the way my colleagues and superiors treat me.

4.0936

0.79982

JS7

I am really enjoyed with my job.

4.2266

0.81925

Source: Developed for the research

According to Table 4.10, it illustrated the central tendencies measurements of job satisfaction. Based on the table, most respondents agreed to the JS2 with mean value of 4.3054. The second highest was JS7 that had 4.2266 of mean value. The third and fourth highest mean value was JS1 and JS6 which is 4.1330 and 4.0936 respectively. The fifth highest was JS5 with the mean value of 4.0591 which was followed by JS3 which had a mean value of 4.0394. The lowest mean value was the JS4 which was only 4.0148.

In the term of standard deviation value, JS7 had the highest standard deviation of 0.81925. Next, it is followed by JS6 of 0.79982, JS3 of 0.78250, JS5 of 0.78125 and JS4 of 0.74779. The two lowest standard deviation values were JS1 of 0.74265 and JS2 of 0.68601.

## 4.2 Scale Measurement

## 4.2.1 Internal Reliability Test

Table 4.11: Reliability Statistic

Variable

Cronbach's Alpha

N of Items

CP

0.881

5

JSM

0.805

5

AD

0.911

5

W

0.899

6

JS

0.901

7

Source: Developed for the research

The reliability test is test the consistency and accuracy of the IVs and DV which the acceptance level of cronbach's alpha more than 0.70.

Based on the Table 4.11, the highest cronbach's alpha is opportunities for advancement and development standing at 0.911. The second highest is job satisfaction which the cronbach's alpha is 0.901 followed by commission pay and work itself which the cronbach's alpha are 0.899 and 0.881. The lowest cronbach's alpha is job security standing at 0.805.

From the result, overall the cronbach's alpha for all variable are more than 0.70. Therefore, the questionnaire in this research is reliability and consistency.

## 4.2.2 Normality Test

The test of normality, histogram with normal curve, P-P plot and scatter plot diagram are being used to show the normal distribution of data.

Table 4.12: Tests of Normality

Kolmogorov-Smirnova

Shapiro-Wilk

Statistic

df

Sig.

Statistic

df

Sig.

Standardized Residual

.055

203

.200*

.988

203

.091

a. Lilliefors Significance Correction

*. This is a lower bound of the true significance.

Source: Developed for the research

This research uses 203 survey questionnaires which the sample size is more than 50. Therefore, Kolmogorov-Smirnov's test is use for testing the normality as it is used to examine larger sample size (n>50). Based on the Table 4.12, the significant value of Kolmogorov-Smirnov's test is equal to 0.200, which is more than 0.05. Thence, as the normality assumption is achieved, the data can be assumed to be normally distributed.

Besides, based on the Appendix G, the histogram with normal distribution curve of brand loyalty showing a reasonable bell-shaped and thus the data can be assumed to be normally distributed.

In addition, from the Appendix F, the normal probability plot of brand lies close to the imaginary straight line which is rising from the lower-left corner to the upper right corner and show an upward slope of the graph. This can implied that the data can be assumed to be normally distributed.

## 4.2.3 Multicollinearity Test

Based on the Table 4.14, the correlation coefficient between IVs were 0.581 for CP and JSM, 0.496 for CP and AD, 0.528 for CP and W, 0.604 for JSM and AD, 0.612 for JSM and W, and 0.657 for AD and W. Therefore, these correlation coefficients are high between IVs but there is no multicollinearity problem since they are all less than 0.8 in this research (Field, 2005).

Moreover, according to Table 4.13 the tolerance and VIF value for CP (0.606 1.650), JSM (0.493 2.029), AD (.497 2.013) and W (.478 2.094) are more than 0.1 and less than 10 respectively in the multicollinearity statistics. Hence, multicollinearity problem do not present in this research (Hair et al., 1992).

Table 4.13: Multicollinearity

Model

Collinearity Statistics

Tolerance

VIF

CP_Average

.606

1.650

JSM_Average

.493

2.029

AD_Average

.497

2.013

W_Average

.478

2.094

Source: Developed for the research

## 4.3 Inferential Analysis

## 4.3.1 Pearson's Correlation Analysis

Table 4.14: Pearson's Correlation Analysis

CP

JSM

AD

W

JS

Commsion Pay (CP)

1

Job Security (JSM)

.581**

1

Opportunities for Advancement and Development (AD)

.496**

.604**

1

Work Itself (W)

.528**

.612**

.657**

1

Job Satisfaction (JS)

.557**

.659**

.674**

.836**

1

**. Correlation is significant at the 0.01 level (2-tailed).

Source: Developed for the research

By implementing Pearson Correlation Coefficient, Table 4.14 illustrates the results of the correlation analysis of the four independent variables (CP, JSM, AD and W) and one dependent variable (JS).

The result from Table 4.14 shows that p-value of commission pay is 0.000 which is less than the significance value 0.01 level (2-tailed) and the r value is 0.557. Therefore, the analysis result concludes that commission pay is moderate positive correlated with job satisfaction.

For job security variable, the p-value is 0.000 which is lower than the significance value 0.01 level (2-tailed) and the r value is 0.659 which indicates there is moderate positive relationship between job security and job satisfaction.

In the Table 4.14, the p-value of opportunities for advancement and development is 0.000 (p<0.01) and the r value is 0.674. Hence, the opportunity for advancement and development is moderate positive correlated with job satisfaction.

As shown in the Table 4.14, the p-value of work itself is 0.00 which is lower than the significance value 0.01 level (2-tailed) and the r value is 0.836. As a result, the work itself has high positive relationship with job satisfaction.

## 4.3.2 Multiple Regression Analysis

Table 4.15 : Multiple Regression Analysis

Independent Variables

Unstandardized

Coefficients

Standardized

Coefficients

t-value

Sig.

B

Beta

Constant

.350

2.069

.040

Commission Pay

.069

.074

1.610

.109

Job Security

.158

.160

3.154

.002

Opportunities for Advancement and Development

.130

.143

2.834

.005

Work Itself

.562

.605

11.724

.000

R2

.748

Adjusted R2

.743

F

147.264**

Source: Developed for the research

From table 4.15 above, shows that the correlation coefficient, R= 0.864, means that there is a positive correlation between the four independent variables and dependent variable. The value of R Square is 0.748 which indicates that 74.8% of the variance in the dependent variable (job satisfaction) is explained by the 4 independent variables (commission pay, job security, opportunities for advancement and development, and work itself). However, it is still leaves 25.2% of job satisfaction is explained by other factors in this study.

Furthermore, according to table above, p-value (Sig. 0.000) is less than alpha value 0.05, thus, the F- statistic which equals to 147.204 is significant. That mean this model is a good descriptor for the relation between the residual and predictors. Therefore, the independent variables (commission pay, job security, opportunities for advancement and development, and work itself) are significantly explaining the variance in the job satisfaction among insurance agents. Since the p-value is less than 0.05 and is in the reject region which H0 is rejected.

According to Table 4.15, job security (p=0.002), opportunities for advancement and development (p= 0.005), work itself (p<0.001) are significant to predict the dependent variable (job satisfaction) in this study because their p-values (Sig.) are less than alpha value 0.05. From the multiple regression analysis, job security, opportunities for advancement and development and work itself are the important motivation factors that affect the job satisfaction among insurance agents in Malaysia. Among these IVs, work itself is the strongest determinant. However, the independent variable (commission pay) is not significantly predicting the dependent variable (job satisfaction). This is because commission pay (p=0.109) is more than the alpha value 0.05.

Therefore, a multiple linear regression is formed by using the data from the column headed "B" shown in the table 4.15 above. The regression equation is as below:

Job satisfaction = 0.350 + 0.069 commission pay + 0.158 job security + 0.130 opportunities for advancement and development + 0.562 work itself

## 4.4 Conclusion

Three independent variables (job security, opportunities for advancement and development and work itself) for this research are found to have significant relationship with the independent variable (job satisfaction). However, the independent variable (commission pay) is not significant relationship with the dependent variable (job satisfaction). Results of the analysis and supporting reasons for the results are being discussed in the following chapter.