Christina Brosnahan
Salary Expectation VS Salary Reality
Inquiry Three
Christina Brosnahan
Christina Brosnahan
Introduction:
Starting salaries vary university to university as well as major to major. The average
starting salary in 2012 for all job types at UTD according to Nace and reported by UTD’s career
center is $44,455 (Edwin 2013). This can vary for each discipline, for instance engineering has
the highest starting salary out of UTD at $61,913, while humanities and social sciences has the
least at $36,988. Other universities averages can vary greatly, for instance coming out of
Carnegie Mellon is $72,757 (Edwards 2013) while Texas State is $40,211. In 1997 Vice President
Gore announced a career information net (ICINet) in order for citizens to see the starting
salaries, average salaries, and other information of almost any career choice (Tudor, 2000). The
white house felt it was important for people to have access to information on the incomes of
Americans, as well as for their benefit in law making as well.
The motivation behind this project was the idea that it is vital to know what you have
the potential to make. Knowing the average of what you will make will be important when it
comes to taking out loans, and managing your money while in college. I wanted to see if current
UT Dallas undergrads knew what the average starting salary was in their area of study as well as
what they expected to make first year out of college. A recent study done on job experience
and connections through social media, shows that the more experience you have in your
discipline the higher salary you will start out with (McDonald, 2011). I also wanted to test for
gender to see if there was any significant gender gap with money.
The main impact of this study is going to be if there is a need to educate the universities
population of realistic average starting salaries. More than 62 percent of 4 year public-school
Christina Brosnahan
college students take some kind of educational loan debt (Congress, 2011). If students already
have a realistic expectation of their salaries then their managing of loans, rent, car payments
will better reflect that. It will also tell me if there is a gender gap of money in expected salaries
that needs to be addressed. Equal pay is already a huge deal in the U.S. and this study can tell if
expected salary expectations are affected by US gender pay gap.
Hypothesis:
My hypothesis is that expectation of salaries will vary from each area of study and that
the salary expectation will be higher than the average starting salary at UT Dallas according to
Nace. No area of study will have the same average expectation. My independent variable is the
area of study the undergraduate will graduate in ( business, communications, computer
science, etc…) as well as gender. My dependent variable will be the expectation in U.S. dollars
their starting salary will be (30,000-120,000+).
Instrumental Design:
My salary survey contains 8 questions. The questions include what area the
undergraduate UTD student plans to graduate in, do they have job experience, are they
planning on going to graduate school, and what they think the average starting salary in their
field is as well as what they expect to make. What area they plan to graduate from was taken by
the Nace salary survey done here at UTD. I used the same eight areas of study that Nace gets
information on so I could directly compare my results to their existing results. The average
starting salary questions start at $30,000- $120,000 to cover almost all potential earning
Christina Brosnahan
amounts. I asked their plans on graduate school so I could later see if students who plan on
going on in their education expect to make more or less than students who do not. My reason
for asking if they have job experience in their field was due to a recent study that revealed the
correlation between experiences with higher wage pay (McDonald, 2011). I also asked for each
student’s gender and race, to later look for connections in female expected pay to male
expected pay. This could lead to more telling information on gender bias and the pay gap seen
in many recent studies (Bolitzer, 2012).
I did not use one specific salary survey to model mine after, however I did take look at
already existing Nace salary surveys as well as salary surveys from AORN Journal on what RN
nurses make (Bacon, 2012). Since I was not asking graduate students what they are making I did
not model one specifically like these, just referenced these for how the asked about race, and
gave specific areas of study.
Data Analysis:
The two main categories I took into place at first were what the student’s majors are
and what they expected to make annually for their first year. I then compared this to the
actually starting salary according to UT Dallas alumni data base results done by Nace research.
After finding out the starting salaries vs expected could be done averaging the two numbers, I
found that most people had realistic expected average starting salaries. There were some
outliers, but almost all disciplinarians feel on or below the real starting salary. The only
expected salary with significant difference from actual starting salary was that of math of
Christina Brosnahan
sciences. According to UTD alumni data base the average starting salary is $42,956, while UT
Dallas students expected they would make $55,000 a $12,044 increase.
Next I wanted to look at the significance of gender on average starting salary. I ran a
regression with the independent variable being gender and starting salary and dependent
variable being expected salary. My null hypothesis is that the gender does not affect expected
starting salary; my alternate hypothesis is that gender does have an effect on average starting
salary. After looking at the p value from the regression, with 94% confidence we can say male
expects to make 8,340 more dollars than a female would, accounting for average starting
salary. The t-test was 1.9, usually I would accept the null hypothesis, and however, in this case I
will accept the alternate because of the real world data the alternate hypothesis is more likely
than the null.
Christina Brosnahan
Gender Line Fit Plot
140000
120000
Expected Starting Salary
100000
80000
Expected Starting Salary
60000
40000
Predicted Expected Starting
Salary
20000
0
0
0.2
0.4
0.6
0.8
1
1.2
Gender
The graph shows the gender differences in expected salaries vs actual starting salary. 0
represents females, and one represents males.
Conclusion:
After surveying 100 UT Dallas students for expected salaries, disciplines, and gender I
looked at the p values and t values of gender compared with expected starting salary. Gender
and actual starting salaries, according to UT Dallas alumni services, were my independent
variables while expected salary was my dependent variable. Males on average expect to make
$8340 more than female with a 94% confidence. This reveals a huge gender gap between UT
Dallas males and females and their expected salaries. Looking at average starting salaries of all
Christina Brosnahan
areas of work and comparing them to the actual starting salaries of those disciplines I found
there was not a significant increase or decrease in expected vs actual. All disciplines fell on
average or below.
Revisions and Shortcomings:
After starting the survey I realized all my real data lied with gender gaps. Next time I
would want to see expected salaries of female and males and add questions such as household
income, parent’s education level, and other things that could affect expected salaries. I would
account for those variables and be able to get a more accurate regression and confidence
interval.
Christina Brosnahan
References
Bacon, D(2012). Results of the 2012 AORN Salary and Compensation Survey. Volume 96, Issue 6
Received from AORN Journal March 22, 2014.
Bolitzer, B.(2012). Understanding the Gender–Pay Gap in the Federal Workforce Over the Past
20 Years. The American Review of Public Administration Received from Sage Journals
March 28, 2014
Edwards, Matthew. Cmu.edu/career. Rep. Nace 20 Feb 2013. Web. 24 Feb 2014.
McDonald, S (2011). What You Know or Who You Know? Occupation-specific work experience
and job matching through social networks. Social Science Research Volume 40, Issue 6,
November 2011, Received March 14, 2014.
Koc, Edwin. Naceweb.org. Rep. Nace, 18 Mar. 2013. Web. 24 Feb. 2014.
Tudor, J. (2000). business connection. Econtent, 23(6), 68 Received March 14, 2014
United States. Congress. Senate. Committee on Health, Education, Labor, and Pensions. June 7,
2011. Drowning in debt : financial outcomes for students at for-profit colleges. Received
March 24, 2014.