Description
concerning this project, you will use the GSS2012.xlxs dataset. The project involves writing a research
paper addressing a research question that can be answered using a hypothesis test and/or
confidence interval. You can choose to work with one numerical or one categorical variable, or you
can choose to evaluate the relationship between a categorical and a numerical, two categorical
variables or two numerical variables.
Your project should be a write up of parts 1 - 5 below in the form of a research paper.
1. Part 1 - Introduction: State the null hypothesis and the alternative hypothesis which
you want to test
2. Part 2 - Data: Write about the GSS data. Address these points:
o Cases: What are the cases? (Remember: case = units of observation). How many cases are
in the data?
o Variables: What are the variables you are studying? State the type of each variable. E.g.,
nominal, ordinal, interval.
o Scope of inference - generalizability: Identify the population of interest, and whether the
findings from this analysis can be generalized to that population, or, if not, a subsection of
that population. Explain why or why not. Also discuss any potential sources of bias that
might prevent generalizability.
o Scope of inference - causality: Can these data be used to establish causal links between the
variables of interest? Explain why or why not.
3. Part 3 – Descriptive Statistics: Perform relevant descriptive statistics, including
summary statistics and visualization of the data.
4. Part 4 - Inference:
o Perform the relevant inferential statistics
o Report the results of inferential statistics
5. Part 5 - Conclusion: Write a brief summary of your findings without repeating your
statements from earlier. Also include a discussion of what you have learned about your research
question and the data you collected. You may also want to include ideas for possible future research
Explanation & Answer
Attached. Please let me know if you have any questions or need revisions.
Outline
i.
Part 1: Introduction
a. Research Questions
b. Hypotheses
ii.
Part 2: Data
a. Units of Observation
b. Variables
c. Scope of Inference
i. Generalizability
ii. Causality
iii.
Part 3: Descriptive Statistics
iv.
Part 4: Inference
v.
Part 5: Conclusion
a. Discussion
b. Future Study
Running head: RESEARCH PAPER
1
Research paper
Student’s Name
Institutional Affiliation
RESEARCH PAPER
2
Table of Contents
Part 1: Introduction ......................................................................................................................... 3
Research Questions ..................................................................................................................... 3
Hypotheses .................................................................................................................................. 3
Part 2: Data ..................................................................................................................................... 4
Units of Observation ................................................................................................................... 4
Variables...................................................................................................................................... 4
Scope of Inference ....................................................................................................................... 5
Generalizability ....................................................................................................................... 5
Causality .................................................................................................................................. 5
Part 3: Descriptive Statistics ........................................................................................................... 6
Part 4: Inference .............................................................................................................................. 9
Part 5: Conclusion ......................................................................................................................... 12
Discussion ................................................................................................................................. 12
Future Study .............................................................................................................................. 12
RESEARCH PAPER
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Research Paper
Part 1: Introduction
The research paper focuses on exploring selected attributes from the GSS dataset to
investigate hypothetical statements to make a discovery that would inform social change. Myers,
Well, and Lorch (2010) defined statistical analysis as a technique used to collect data, clean the
data, and performing various transformations to uncover different patterns and trends. On the
other hand, hypotheses are statements developed by researchers with minimal information and
open for further statistical examination intended to guide towards making a discovery that would
inform supported decisions and corresponding recommendations (Bell, Bryman, & Harley,
2018). In this case, the research intends to comprehend the association between the income, age,
number of hours worked, and the degrees of the respondent in the general social survey dataset.
Research Questions
Is there a statistically significant mean difference between the respondent’s income
i.
depending on the degree?
Is there a statistically significant association between a respondent’s income, age, number of
ii.
hours worked?
Hypotheses
Ho: There is no statistically significant mean difference between the respondent’s income
depending on the degree. (all means are equal)
H1: There is a statistically significant mean difference between the respondent’s income
depending on the degree. (Not all means are equal)
RESEARCH PAPER
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Ho: There is no statistically significant association between a respondent’s income, age, the
number of hours worked.
H1: There is a statistically significant association between a respondent’s income, age, the
number of hours worked.
Part 2: Data
Units of Observation
The general social survey data provides diverse information regarding the areas of
interest to the user of the data in making informed decisions and recommendations related to
areas of interest under statistical investigation. The GSS data comprises numerous cases defined
as the units of observation in statistical analysis. Little and Rubin (2019) described cases as the
actual items in which information is collected and necessary to identify the units of observation
in a logical survey, organized data gathering, and used in unbiased data analysis, for example,
household, school, or individual. The GSS dataset contains 71 units of observations that provide
different aspects of the participants in the survey. Some of the GSS data cases are age, race,
marital status, income, education, number of hours worked, and work status, among other
observation units in the dataset.
Variables
This study’s selected variables include the respondent’s age, the number of hours worked,
income and the highest attained degree. The table below illustrates the type of variables chosen
based on their level of measurement.
RESEARCH PAPER
5
Table 1
Variable Description
Variable
Description
Level of measurement
Type of variable
Age
The variable illustrates the age of the
Interval
Continuous
participant in the survey
HRS1
The number of hours worked in a week.
Ratio
Continuous
Income
The amount of money earned per month
Ratio
Continuous
Degree
Highest degree attained
Nominal
Categorical
Scope of Inference
Generalizability
Job seekers form the population of interest for this analysis because the finding will
inform the expected level of income based on varying attributes such as their age and the
corresponding number of hours worked in a week. The results could be generalized across the
population of interest because the analysis took keynote on different aspects that affect a
person’s earning, such as education. In this case, it is expected that a job seeker with a higher
degree would earn a higher income than the job seeker with a college or a high degree.
Nonetheless, the target population of interest’s specificity forms the primary bias that could
prevent the research finding’s generalizability. Ali and Yusof (2011) noted that generalizability
entails how the research findings could be used by a broader population, with a considerably
narrow population of interest in specific research depicting potential bias to the research
findings’ generalization.
Causality
Nonetheless, researchers may not use the data to establish causal links between the
variables of interest. Notably, causality is significantly different from correlation and requires
establishing a controlled study to determine the presence of a causal relationship between an
RESEARCH PAPER
6
aspect under investigation (Li, Liu, & Le, 2015). For instance, in this case, we can evaluate a
significant correlation between income and the person’s age. Still, we cannot tell whether there is
a causal association between an individual’s age and their corresponding earned amount as their
monthly income. In this case, the research could require a controlled study to establish whether
the age of a respondent has a causal relationship with the consequently earned income.
Part 3: Descriptive Statistics
According to Easterby-Smith, Thorpe, Jackson, and Jaspersen (2018), descriptive
statistics in research help the researcher describe their data based on different features such as the
frequency distribution for categorical variables measuring central tendency and variation for the
continuous attributes. The table below illustrates the varying descriptive statistic depicted by
different selected variables from the GSS dataset.
Participant’s Age
Table 2 above depicts the case processing summary, indicating that the number of valid
cases for the variable age included in the analysis is N= 1323. Notably, to avoid bias and undue
influence on the results, the study excluded the missing data.
RESEARCH PAPER
Table 3 above illustrates diverse descriptive statistics for the selected variable age of the
respondent. The participants’ mean age was 42.87 years, with a standard deviation of 13.996
years from the table. Subsequently, the youngest participant was 18 years, while the oldest
respondents in the survey were 86 years of age.
Number of Hours Worked
Based on table 4 above, the average number of hours worked in a week was 35.46 hours, with
an SD=19.6 hrs. Besides, the minimum hours worked in a week was 0, implying that the
7
RESEARCH PAPER
8
respondent was not working at the moment of the taking the survey, or maybe the participant
was a student at the time of data collection. On the other hand, the maximum number of hours
worked in a week was 89 hrs, implying that the participant worked approximately 12.7 hrs a day
for seven days in a week.
Participant’s Income
Table 5 above illustrates the respondent income per month, depicting a mean M= US$34,764.17
and SD=US$ 37,318.243. Moreover, the minimum amount of income earned by the respondents
was US$ 0, illustrating that the respondent was not earning a payment at the time of the study.
On the other hand, the maximum amount earned US$ 175,000 per month.
Respondents Degree
RESEARCH PAPER
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Figure 1: A pie chart for the respondent degree
Inference
From figure 1 above, most participants had attained a high school degree (49%), followed
by graduates with a bachelor’s degree constituting (21 %) of the total sampled population.
Graduates in the study were 11 percent of the sampled population. In comparison, 8 percent of
the participant had attained a junior college degree. Subsequently, 11 percent of the respondents
depicted having left after high school education.
Part 4: Inference
The section provides the inferential statistic findings focused on exploring the
hypothetical statements to reject the null supposition. The inferential analysis included one-way
ANOVA, to help determine whether there was a significant income means the difference
between the degree attained by the participant in the study. The inferential analysis included a
regression analysis in determining whether there was a significant association between the
income earned by an individual based on their age and the corresponding number of hours
RESEARCH PAPER
10
worked per week. In performing the inferential analysis, the evaluation assumed that the
variables met the following assumptions
1. There were no significant outliers to avoid undue influence in the obtained results
2. The variable assumed a normal distribution
3. The independent and dependent attributes depict a linear association.
4. The variable depicts no multicollinearity.
Besides, the study decision rule is to reject the null statement if the p-value < than statistical
power α=0.05.
The table below illustrates the dataset’s obtained inferential statistics to answer the research
question while proving the hypothesis.
Hypothesis 1
There is no statistically significant mean difference between the respondent’s income depending
on the degree. (all means are equal)
Inference
From table 7 above, the F = 77.062 and p (sig.) =0.000, with a df=4. The finding indicates that
the p-value value is less than the statistical power α=0.05, thus rejecting the null statement.
Hypothesis 2
There is no statistically significant association between a respondent’s income, age, the number
of hours worked.
RESEARCH PAPER
11
Inference
From table 8 above, R=0.371 and R-square = 0.138, depicting a weak positive correlation
between the dependent and independent attributes. The R-square value implies that the
dependent variable explains 13.8 percent of the predictor attribute in the analysis.
Inference
From table 9 above, F= 105.475, P= 0.000, with 2 df. The finding depicts that the p-value value
is less than the statistical power α=0.05, thus rejecting the null statement.
Inference
From table 10 above, the beta value for the variables age = 474.718 and Hrs1=611.046. The tvalue for the attributes is 6.964 and 12.533 with a p= 0.000 for the variable period and hrs.
worked, respectively. In this case, the predicting model for the anticipated income is
RESEARCH PAPER
12
𝑦 = 474.718 ∗ 𝑋1 + 6 ⋅ 11.046 ∗ 𝑋2 − 7252.942
Where
x1: age and x2: Hours worked per week
Part 5: Conclusion
From the inferential analysis, the finding rejected the null statement at a 95 percent
confidence level, which implies that the mean difference for the income varies depending on the
degree. Similarly, the statistical analysis results rejected the send null supposition, indicating that
we are 95 % confident that there is a statistically significant association between a respondent’s
income, age, and the number of hours worked per week. In support, the coefficient table 10
above depicted that the variable age and hours worked per week by a participant were
significantly illustrated by p-values less than the statistical power described in the analysis
decision rule.
Discussion
From the research, it is evident that a parson level of education and degree attained
contribute significantly to the corresponding amount of income that a person earns per month.
Another significant finding from the research work is that the person’s age and the number of
hours worked per week also contributed significantly to their income. The conclusion is highly
informative for the job seeker because based on the results and the model above, one would
determine an expected amount of the money that would earn based on the predictor variables age
and number of hours worked.
Future Study
However, future studies should seek to evaluate the relationship between the age and
hours worked relative to the income earned to establish a causal relationship between the
RESEARCH PAPER
13
variables. The finding would help to determine the effect size of the relationship between the age
and hours to the corresponding amount earned by an individual as the monthly income.
RESEARCH PAPER
14
References
Ali, A. M., & Yusof, H. (2011). Quality in qualitative studies: The case of validity, reliability
and generalizability. Issues in Social and Environmental Accounting, 5(1/2), 25-64.
Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press.
Easterby-Smith, M., Thorpe, R., Jackson, P. R., & Jaspersen, L. J. (2018). Management and
business research. Sage.
Li, J., Liu, L., & Le, T. D. (2015). Practical approaches to causal relationship exploration.
Springer.
Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley
& Sons.
Myers, J. L., Well, A., & Lorch, R. F. (2010). Research design and statistical analysis.
Routledge.
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SEX
1
1
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1
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RACE
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3
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RACECAT1 RACECAT2 MARITAL
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0
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BORN
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2
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1
EARNRS
3
3
1
2
0
1
4
0
3
1
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4
1
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1
1
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1
3
1
1
1
2
1
1
1
1
1
2
5
3
2
3
3
1
1
3
3
5
4
1
5
1
1
5
5
3
1
5
1
5
1
5
4
4
2
4
1
5
3
5
5
2
3
5
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
2
2
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
1
2
0
2
1
1
2
2
2
3
0
1
0
1
0
1
3
2
0
0
1
2
1
1
2
2
2
2
1
4
1
0
1
2
0
1
0
0
1
1
2
0
0
1
0
1
2
1
3
3
0
57012
57013
57014
57015
57016
57017
57018
57019
57020
57021
57022
57023
57024
57025
57026
57027
57028
57029
57030
57031
57032
57033
57034
57035
57036
57037
57038
57039
57040
57041
57042
57043
57044
57045
57046
57047
57048
57049
57050
57051
57052
57053
57054
57055
57056
57057
57058
57059
57060
57061
56
34
56
47
44
30
73
81
70
33
21
39
65
34
73
32
44
66
30
31
21
50
49
71
53
58
40
45
62
28
74
60
42
36
50
63
20
71
50
23
65
60
69
78
25
61
53
48
37
22
2
2
1
1
2
1
1
2
2
1
2
1
1
1
2
2
2
1
2
2
1
1
2
1
1
2
1
2
2
2
1
1
1
2
2
1
2
1
1
2
2
1
2
1
2
2
2
1
2
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
3
3
3
1
3
3
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
5
3
1
2
5
1
2
2
5
5
1
3
1
1
1
3
3
5
3
5
3
3
1
1
1
5
1
3
5
2
1
3
1
1
1
5
3
2
5
1
2
1
3
5
5
5
1
5
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
2
0
1
2
1
1
0
0
0
2
2
2
0
2
0
2
1
1
1
1
1
3
1
1
2
2
1
2
1
2
1
2
1
2
3
0
3
1
1
1
2
0
0
0
0
2
0
4
2
1
INCOMECAT
23
16
6
11
19
19
18
20
INCOME TFINCOMECAT TFINCOME
25
175000
25
175000
120000
23
120000
24
140000
19
55000
15
27500
16
32500
5
5500
1
500
32500
16
32500
1
500
6500
19
55000
8
9000
14
23750
16250
55000
19
55000
55000
19
55000
45000
18
45000
67500
20
67500
14
23750
20
22
67500
100000
18
22
24
23
45000
100000
140000
120000
12
18750
25
175000
8
9000
19
55000
20
18
20
20
18
16
18
22
67500
45000
67500
67500
45000
32500
45000
100000
25
25
25
175000
175000
175000
25
13
175000
21750
20
22
67500
100000
18
8
21
19
25
20
20
20
22
18
16
18
22
23
25
25
25
25
25
25
20
25
20
25
25
24
45000
9000
82500
55000
175000
67500
67500
67500
100000
45000
32500
45000
100000
120000
175000
175000
175000
175000
175000
175000
67500
175000
67500
175000
175000
140000
EDUC
16
12
12
13
16
19
15
11
9
17
10
16
12
12
4
13
12
13
12
12
0
10
14
16
12
17
15
10
16
13
16
14
19
16
14
18
11
12
17
14
18
14
18
16
16
16
16
16
16
19
16
16
20
14
SPEDUC PAEDUC MAEDUC DEGREE
16
16
3
12
1
16
15
1
16
18
16
1
12
3
14
12
3
12
14
12
2
11
0
12
0
14
12
3
0
13
3
1
8
1
3
3
0
14
12
1
9
9
9
1
16
1
12
6
1
1
5
0
4
0
14
12
2
16
12
2
13
8
8
1
20
17
17
3
20
14
1
12
10
0
5
3
11
13
1
12
12
3
14
12
2
19
6
4
17
19
19
3
12
12
1
14
12
4
0
1
13
1
16
3
12
1
20
18
18
4
11
10
1
14
4
9
14
3
16
16
16
3
20
20
12
3
16
18
12
3
16
16
12
4
17
16
3
9
12
4
12
5
3
16
16
12
3
20
8
4
12
2
25
175000
11
16250
25
22
17
15
8
20
15
18
45000
15
20
21
14
20
27500
67500
82500
23750
67500
22
100000
22
100000
14
23750
4
4500
18
20
24
18
18
175000
100000
37500
27500
9000
67500
27500
45000
16
20
21
16
23
23
22
32500
67500
82500
32500
120000
120000
100000
25
25
21
21
15
175000
175000
82500
82500
27500
45000
67500
140000
45000
15
25
24
24
18
27500
175000
140000
140000
45000
14
18
23750
45000
17
21
37500
82500
11
23
19
7
16250
120000
55000
7500
4
4500
11
23
19
14
13
15
14
25
16250
120000
55000
23750
21750
27500
23750
175000
21
11
18
82500...