Posted: May 22nd, 2015
T-Test Paper
T-Test Paper
Introduction:
Employment is one of the main issues that are facing the society today. People are seeking for employment each and every day. Many jobs are being listed daily on the newspapers, televisions, internet, and magazines as well as on radio stations. Gender is an important determinate of employment. People are always showered with issues that are related to how and where to get employment and also if that employment will cater for our daily needs. In the survey, the main interest is to gauge what the people felt about their survival and employment. Therefore, sample of convenience were given which consisted of diverse questions to a pool of 260 respondents who were among our colleagues, friends as well as family. The survey consisted of variety of common questions including their name, gender, permanent residence, dominant hand, the number of siblings in their household and their height. Part of this survey was used in exploring the differences between gender and employment. With the help of the survey’s results that related to employment, gender was compared with the respective respondent. Therefore, gender is the independent variable while the dependent variables consist of the following:
1) Level of education
2) Level of job satisfaction
3) Level of payment
Hypothesis:
Figure 1: summary of null hypothesis and the alternative hypothesis
Null hypothesis: there exists a significance difference among women and men in regards to their level of education.
Alternative hypothesis: there exists no significance difference among women and men in regards to their level of education.
Null hypothesis: there exists a significance difference among women and men in regards to their level of job satisfaction.
Alternative hypothesis: there is no significance difference among women and men in regards to their level of job satisfaction
Null hypothesis: there exists a significance difference among women and man in regards to their level of payment
Alternative hypothesis: there exit no significance difference among women and men in regards to their level of payment.
Figure 1:
| Dependent
Variable |
Independent
Variable |
Null
Hypothesis(H0) |
Alternative
Hypothesis (Ha) |
| Level of education | Gender | H0: women = men | Ha: women men |
| Level of job Satisfaction | Gender | H0: women = men | Ha: women men |
| Level of payment
|
Gender | H0: women = men | Ha: women men |
Methods:
This was a convenience survey where respondents consisted of fellow colleagues, family and friends. From a sample of 260 respondents, 169 were women (65%) and 91 were men (35%). However, it is important to note that there existed error in several sources within sample of questions that included the respondent’s profile as well as the probability of a general population not being the representative of the survey’s results. Independent variable was obtained as a result of one question which asked the respondent to reveal their gender whether woman or man. On the other hand, dependent variables were obtained as a result of numerical measurement that ranged from 1-10 in providing the agreement level of statements that were reflected in every question. 1= no to 10= yes
Figure 2
Gender distribution in the class survey
Statistical Analysis:
Microsoft Excel was used in order to interpret and calculate independent t-test from the survey’s data. Inferential statistics were also used in determining the validity of null hypothesis where the level of error as well as significance was based on determination of significance level which was equal to 0.06. Therefore the null hypothesis can be rejected if it is less than 0.06.
Results:
Level of education
Education is considered to be a strong determinant when it comes to employment. In today’s society many jobs requires a person to be a degree holder. During this survey we discovered that when it comes to technology, mathematics and science, women are under-represented. It was realized that men are taking all the good jobs that are related to these fields for instance engineering and medicine. On the other hand, it was still discovered that men are also under-represented when it comes to fields such as arts, humanities as well as other areas of work having care-related dimension for instance child care, nursing as well as primary school teaching. For one to get a good job that is paying well, he has to be well educated. In the effort to gauge education level among survey participants, questions were asked on a 1-10 scale (1=no to 10=yes) in order to determine their level of education. The mean response of the women respondents was 4.445. The frequent response (mode) was 6. Figure 3 and 4 shows the mean and mode for the women respondents. The mean response of the men respondents was 7.340 while the frequent response was 9, on the basis of numerical scale which was 1-10. Figure 5 and 6 represents this.
Figure 3
Descriptive statistics for women response on the level of education
Level of education for women
Mean 4.445
Standard Error 0.190391
Median 5
Mode 6
Standard Deviation 3.367183
Sample Variance 7.723234
Skewness -0.0239
Range 8
Minimum 1
Maximum 10
Sum 876
Count 169
Figure 4
Histogram for women response on level of education
Women response to the level of education
30 30 30
20 10 20 20 5
10 3
0 1 2 3 4 5 6 7 8 9 10
Level of education on 1 to 10 scale 1 = no; 10= yes
Figure 5
Level of education for Men
Mean 7.340108
Standard Error 0.236559
Median 7
Mode 9
Standard Deviation 2.281294
Sample Variance 5.204301
Kurtosis -0.24191
Skewness -0.4914
Range 9
Minimum 1
Maximum 10
Sum 598
Count 9
Figure 6
Histogram for men response on level of education
Men response to the level of education
25
20 21
15 11 11 16
10 6 6 6 9
5 3 3
0 1 2 3 4 5 6 7 8 9 10
Level of education on 1 to 10 scale 1=no; 10= yes
Based on figure 7 which illustrates independent sample t-test, average men response was 7.340 while the average mean for female was 4.445. This indicates that the male respondents on this survey have a higher level of education compared to the women respondents. The figure also shows that, P (t<=t) two tails which is 0.00429 is comparatively less compared to designated significance level which had an original value of 0.05. This means that it is most likely for the alternative hypothesis to be rejected. It concludes that null hypothesis is acceptable since it states that there exists a significant difference among women and men in regards to their level of education.
Figure 7:
Education
Male female
| Mean | 7.34072 | 4.445607 |
| Variance | 5.20363 | 5.72279102 |
| Observations | 91 | 169 |
| Pooled Variance Hypothesized Mean Difference | 5.583173
0 |
|
| Df | 249 | |
| t Stat | 2.042507 | |
| P(T<=t) one-tail | 0.020578 | |
| t Critical one-tail | 1.650997 | |
| P(T<=t) two-tail | 0.00398 |
T-Test results for level of education
Level of job satisfaction
Job satisfaction is considered to be an important factor quality of life in general. This is because it is closely connected with working life. Quality of life much affected by job satisfaction because it is connected with everyday life. Lack of job satisfaction will lead to under-performance in the employee’s work. It will also lead to stress which might cause physical as well as emotional health. On the other hand, job satisfaction will lead to happier employee; good work performances as well as improved productivity hence profit the company. In carrying out this survey, it was found out that women are not happy with their jobs unlike men. Some respondents gave us the reasons which contributed to their lack of job satisfaction. They included poor payment and job discrimination. The participants were requested to rate their satisfaction level of their present jobs on numerical scale ranging from 1 to 10. 1=no to 10 yes. The mean response from the men respondents was 5.689 while the mean response from women respondents was 4.887. For men respondents, the frequent was 4 which indicated a neutral response for the satisfaction of their jobs and for the women respondents, the frequent response was 1. This indicated that women respondent were not satisfied with their present jobs. Figure 8 and 9 summarizes this data for men and women respondents.
Figure 8:
Descriptive statistics for men respondent on their job satisfaction
Male Response- present job Satisfaction
Mean 5.688972
Standard Error 0.304546
Median 5
Mode 4
Standard Deviation 2.972123
Sample Variance 8.738663
Kurtosis -1.17116
Skewness 0.074784
Range 9
Minimum 1
Maximum 10
Sum 529
Count 91
Figure 9:
Descriptive statistics for men respondents on job satisfaction
Female Response- present job satisfaction
Mean 4.887076
Standard Error 0.239422
Median 5
Mode 1
Standard
Deviation 3.002475
Sample Variance 9.06002
Kurtosis -1.23
Skewness 0.1742371
Range 9
Minimum 1
Maximum 10
Sum 772
Count 169
Figure 10:
Histogram for men response on their job satisfaction
Histogram for men response- job satisfaction
20 18
15 13 15
10 10 7 10
5 4.5 9 4 3.5
0
1 2 3 4 5 6 7 8 9 10
Men response on scale 1 to 10; 1=no; 10=yes
Figure 11:
Histogram for women response- job satisfaction
40 34
35
25 24
20 17
15 13 12 13 12 10 11 13
5
0
1 2 3 4 5 6 7 8 9 10
Response of women respondent on scale of 1 to 10, 1-o: 10= yes
Independent samples t-test that is shown in figure 12 indicates that 5. 689 was the average response for male respondents while the average response for female respondents was 4. 887. Therefore this results show that men were satisfied with the present jobs compared to their women counterparts. The result of P (t<=t) is 0.0421which is less than designated significance level which was at original value of 0.05. Therefore, the alternative hypothesis is likely to be rejected while the null hypothesis is proven right since it states that there exists a significance difference among women and men in regards to their job satisfaction.
Figure 12:
T-test results for present job satisfaction with
Male Female
| Mean | 5.689172 | 4.887076 |
| Variance | 8.738663 | 9.057002 |
| Observations | 91 | 169 |
| Pooled Variance Hypothesized Mean Difference | 8.789383
1 |
|
| Df | 249 | |
| t Stat | 2.052607 | |
| P(T<=t) one-tail | 0.020478 | |
| t Critical one-tail | 1.66787 | |
| P(T<=t) two-tail | 0.040156 |
T Critical two-tail 1.869528
Level of payment
Payment is another aspect of employment. It has been made clear that men get better payment compared to women. According to SoHo statistics, 80 percent of the working women are poorly paid. The female respondents were complaining that even when they are given the same position, their payment are not the same. The men’s pay is high than the women’s. The amount of payment employees contributes highly to their performance. An employee will work poorly if he feels that he is his payment is not worth his work. On the other hand, an employee who is well paid will work efficiently since he feels that the payment is worth his effort. Poor payment will also lead to low turnout of labor hence affecting the performance of a business. During the survey, it was realized that women are moving from one job to another in search of a better pay job. It was also noted that women are forced to take from one to three jobs so as to be able to cater for their needs as well as their families. The average mean response for men respondents was 6.340 while the average response for women respondents was 5.50. The frequent response for men and women respondents was 6. Figure 13 and 14 summarizes the data for men while figure 14 and 15 summarizes data for women.
Figure 13
Descriptive statistics for men respondents on their payment
Male Response to
Payment
| Mean | 6.340108 |
| Median | 7 |
| 6Mode Standard Deviation | 7
2.271284 |
| Sample Variance | 5.203341 |
| Range | 9 |
| Minimum | 1 |
| Maximum | 10 |
| Sum | 598 |
Count 91
Figure 14
Descriptive statistics for women respondents on their payment
Female Response to
Payment
| Mean | 5.5 |
| Median | 6 |
| Mode Standard Deviation | 7
2.42054543 |
| Sample Variance | 5.9067343 |
| Range | 9 |
| Minimum | 1 |
| Maximum | 10 |
| Sum | 869 |
Count 169
Figure 15:
Histogram of men respondents on their payment
Male response to their payment
25 22
20 15.5
15 10.5
10 5.5 5.5 10.5 5.5 9m
5 3 3
0
1 2 3 4 5 6 7 8 9 10
Men responses on scale of 1 to 10, 1= no: 10=yes
Figure 16:
Histogram for women response on the level of payment
Women response to the level of payment
30
25 24
20 14 24 21 24
10 9 14
5 12 13 9
0 1 2 3 4 5 6 7 8 9 10
Female responses on the scale of 1-10, 1=no: 10=yes
Figure 17:
T-test results for level of payment
| Men | Women | |
| Mean | 6.3401085 | 5.5 |
| Variance | 5.2043011 | 5.507553212 |
| Observations | 91 | 169 |
| Pooled Variance Hypothesized Mean Difference | 5.6387639
3 |
|
| Df | 249 | |
| t Stat | 2.9846241 | |
| P(T<=t) one-tail | 0.0014159 | |
| t Critical one-tail | 1.6408992 | |
| P(T<=t) two-tail | 0.0030258 |
T Critical two-tail 1.9695368
Discussion
The main purpose of this study was to explore whether women and men differ on issues related to employment. It assumed that women are not more at ease with employment issues since they fell that there is no gender equality when it comes to employment. For instance they feel that men get better jobs as well as better payment than women. Therefore, prior to this study our survey was a sample of the convenience which was among the people we were usually acquainted with. The survey was not representative of population in general. Some points should be mentioned in order to understand the anomalies as well as the delineation of the given data. For instance women were not fully cooperative compared to their men counterparts and they failed to clearly specify some questions that were asked. However when it comes to various groups, we are given some insight on people making it easier to gain perspective on variety of topics apart from independent t-test studies. In most cases, seeing why differences exist on different variables has been beneficial. For example, why men are satisfied with their jobs? This could be as a result their good payment as well as good positions within a company. Such issues also brings about questions like how more are women and men willing to sacrifice in order to get good employment, for instance are they willing to give bribery in order to get the job. The data analysis could be useful to both the individuals and to the businesses. The individuals especially women could use this data in order to determine why men are getting better jobs, good payment and job satisfaction. This will influence them to invest in more promising careers which are commonly known to be the jobs for men, hence better payment for them which will lead to job satisfaction. Employers can use this data to determining why their employees are performing poorly in their work. The data will help them to come up with strategies designed to motivate their performance hence profits to the business. The data analysis will also help the employers to practice gender equality within the workplace so as to increase the performance of women employees. With this data analysis we have realized men are more favored by the employment environment compared to women. Figure 18 summarizes comparative analysis which exists between variables and gender.
Figure 18
T-test results summary
| Dependent Variable | Independent
Variable |
Hypothesis Accepted |
| Level of education | Gender | Null Hypothesis
Accepted– there exist a significant difference among women and men in regards to their level of education |
| Job satisfaction | Gender | Null Hypothesis– there
Exist a significant difference among women and men in regards to their level of job satisfaction |
| Level of payment | Gender | Null Hypothesis– there
exist a significant difference amongst women and men in regards to their paymentmenreregards to their payment |
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