1Running Head NURSING PROFESSIONALISM2NURSING PROFESSIONALI.docx
SDM Mini Project Group F
1. Statistics for Decision Making
Mini Project
“Analysis of difference in salaries for male and female
employees in IT sector”
Submitted By:
Group F
Sunil Malik
Anwesh Chakraborty
Chetan Barapatre
Harveen Johar
Samrendra Singh
Saikat Basu
2. Table of Contents
Motivation, Objective and description of a real problem .............3
Objective............................................................................................3
Hypothesis formulation and testing procedure.............................4
Confidence Level..............................................................................4
Design of Experiments ....................................................................5
Analysis/Inference/conclusion ........................................................5
Comments and Suggestions...........................................................6
Summary and Conclusions.............................................................6
References........................................................................................7
Reference Study...............................................................................7
3. Motivation, Objective and description of a real problem
The Information technology industry in India has gained a brand identity as a knowledge
economy due to its IT and ITES sector. The IT– ITES industry has two major components: IT
Services and business process outsourcing (BPO). The growth in the service sector in India
has been led by the IT–ITES sector, contributing substantially to increase in GDP,
employment, and exports. Bangalore is considered to be the Silicon Valley of India because
it is the leading IT exporter. Export dominate the IT–ITES industry, and constitute about 77%
of the total industry revenue.
This sector has also led to massive employment generation. The industry continues to be a
net employment generator, thus providing direct employment to about 2.8 million, and
indirectly employing 8.9 million people.
In today’s world, where women work alongside men, the issue of fair and equal treatment
arises often. In fact, gender inequality related to work is one of the issues that has been raised
and debated often.
Women constitute almost half the population of India (48%) and thus potentially, half its labor
force.
Though the government has introduced several laws to prohibit inequalities or discrimination
against women workers but unfortunately, there still exists a wide gender pay gap in India. In
majority of Indian work places, the so -called “glass ceiling” is not completely broken yet. There
are many facets of gender inequality and in the current scenario; it is professional inequality
that incessantly acts as a barrier for women’s advancement at the workplace. Professional
inequality refers to discrimination in terms of employment, remuneration, promotion at work.
The gender pay gap measures the earning differences between women and men in paid
employment in the labour market. It is one of the many indicators of gender inequality in a
country when examining the labour market participation in terms of gender .The gender pay
gap has become a universal issue. Various theories have been advanced to explain this gap
from an economic perspective. Most of them discuss either the human capital model (supply
-side factor) that focuses on gender differences in skills, particularly education and experience,
or labour market discrimination (demand -side factor) i.e. inequitable treatment of equally
qualified male and female workers.
The purpose of this survey was to study if there is any statistically significant difference
between the salaries of male employees and female employees who are engineering
graduates with 4 to 6 years of work experience in IT and software industry.
Objective
To study if there is any statistically significant difference between the salaries of male
employees and female employees who are engineering graduates with 4 to 6 years of work
experience in IT and software industry
4. Hypothesis formulation and testing procedure
Based upon the above objective, we have formulated below Null hypothesis and alternate
hypothesis”:
Assuming the following:
Mean value of average gross monthly salary of male employee = µMales
Mean value of average gross monthly salary of female employee = µFemales
Null Hypothesis,
H0: µFemales = µMales
Alternate Hypothesis,
H1: µFemales ≠ µMales
Confidence Level
Since most of the data could be gathered with the help of free online services, cost involved
in the research is minimal. Hence, we have taken a high confidence level of 95%. Also, this is
a well-accepted and standard confidence level.
Confidence level - 95% or 0.95
Hence, Alpha = 5% or 0.05
5. Design of Experiments
Based upon the confidence interval chosen above, we have decided to take two independent
samples. First, a sample of 35 male respondents falling under the pre-defined cluster. Second,
a sample of 35 of 35 female respondents within same cluster.
To fulfil our criteria for the cluster sample, we have designed below questionnaire with four
multiple choice questions and one text type question to collect the salary details
Question
Number Question Options
1 Gender Male, Female
2 Work Experience 4 - 6 Years, Others
3 Salary Monthly Salary ( INR )
4 Industry IT/Software, Others
5 Education Graduate, Others
6 Education Stream Engineering, Others
We then floated the questionnaire to employees of two selected IT/Software companies (with
total employee base of more than 10,000). The data was collected keeping random sampling
and anonymity of user information in mind.
After we received the responses, we put the filter to collect 70 samples (35 each for males
and females) which belonged to our criteria/cluster (4 – 6 years of work ex, engineer,
graduates). This clustering was done to avoid the variation in salaries of males and females
because of other factors such as education, job sector, age and work experience etc.
The data was thereafter fed in SPSS for further analysis
Analysis/Inference/conclusion
To capture the statistically significant difference between the salaries of male employees and
female employees, we decided to use an independent samples t-test.
The reason for selecting independent samples t-test was that we're testing different people's
scores (men vs. women) to see if there's a significant difference between their scores. Hence,
independent samples t-test is the idle option.
Our Group variable was gender (Male = 1, Female = 2)
Test variable = salary
First, we analyzed the group statistics,
We could see some difference in mean salary, standard deviation, and standard error of
means. But to determine whether these differences are statistically significant or not, we would
analyze the table of Independent Samples Test.
6. Levene’s Test
First, we looked at Levene's test for equality of variances between male sample and female
sample.
Levene’s test is a sub hypothesis within our broader hypothesis test. Levene’s test takes the
null hypothesis as following
Test variances are equal on grouping variable, i.e.
(Null hypothesis is variance for females and males on salaries are equal)
For, this we checked the Significance (p – value), which is 0.095 in this case. Since, our alpha
value is 0.05, less than p-value, hence we do not have sufficient evidence to reject the null
hypothesis. So, we can say that
Variances are equal on grouping variables.
Main Hypothesis Analysis
Now, we analyze our main Null hypothesis, we observed the following in our SPSS output:
t value = test statistics is 1.693 with 68 (70 sample size – 2 groups) degree of freedom
p value (sig. 2 tailed) = 0.095 for wider null hypothesis
With alpha value = 0.05, this p value is greater than alpha value, hence we can say that we
don’t have enough evidence to reject the null hypothesis.
Comments and Suggestions
Although we fail to reject the Null hypothesis in this case, but there could be several other
factors that might distort our results:
Criteria based data collection: There could be other companies, sectors, education
levels at which this hypothesis might be false.
The method of data collection is not absolutely random. There is inherent bias in the
approach.
The questionnaire could not capture all the causal factors that affect the average
monthly salary of the employees.
Summary and Conclusions
Using above Hypothesis testing, we conclude that we do not have enough evidence to reject
the null hypothesis and we are 95% confident about this conclusion.
