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SOCI332 Assignment: Final Project – Guide to Completing the Research Study
Complete the following assignment by filling in all requested information. You will need to utilize
SPSS and the GSS dataset provided in class to complete it. Information should be based on the
variables you chose and analyzed in Weeks 1-6. Use a different, but legible, color font for your
responses. This assignment is intended to help you complete the writing of a quantitative
research article, with particular emphasis on the Findings and Conclusion sections.
The assignment is to be completed and submitted no later than the Sunday of Week 7 by
11:55pm ET. It is worth 100 points.
(A) Introduction: Write an introduction to your research study in 1-2 paragraphs. Be sure to include
a brief statement of current research on your topic (with an in-text citation of a source), a
description of your research topic, why you chose the topic, what you hoped to learn from the
topic, your research question, and your broad hypothesis.
(B) Literature Review: Write a 3-5 paragraph lit review (review of studies on your topic) using more
than three sources, at least two of them being research studies in peer-reviewed journal
articles.
(C) Methods/Dataset: In one paragraph, describe the GSS dataset.
(D) Methods/Variables: In one paragraph, discuss why you chose the specific independent and
dependent variables for your analyses. Include the names of the variables, the level of
measurement, the questions asked in the survey, and the response choices for each.
(E) Methods/Variable information: Copy and paste your frequency tables and descriptive statistics
for each variable. Include your charts as well (histogram, bar, or pie). Provide a
summary/explanation of what these tables/charts tell us about your variables:
(F) Findings: Copy & Paste corrected analyses from Weeks 3-6, i.e., Crosstabs, Measures of
Association, Tests of Significance. Include the five steps of hypothesis testing.
(G) Findings: Explain what the analyses displayed above tell us about the relationships between the
variables in the study.
(H) Conclusions: Explain how the findings contribute to the study of your topic. What did you learn?
What other variables need to be explored? What further research on your topic could be
pursued in the future?
References: Include full APA citations of all sources used. List them in alphabetical order by the author’s
last name. Use hanging indentation (Line spacing options – Indention – Special – Hanging). Don’t forget
to cite the GSS dataset (citation is listed in Week 1 Lessons).
(A) My Purpose (research question)
My research question is: Should all schools include sex education in an age-appropriate course or
leave it to parents?
I chose this topic because: There have been increasing cases of sex related issues in the society
an thus there is need to establish whether sex education should be part of the school curriculum
or whether it should be left as a parental duty.
APA citation of an academic resource that relates to your topic:
Marshall, S. A., Hudson, H. K., & Stigar, L. V. (2020). Perceptions of a School-Based Sexuality
Education Curriculum: Findings from Focus Groups with Parents and Teens in a Southern State.
Health Educator, 52(1), 37-51.
(B) All About the GSS
1. What population does the sample represent? The sample in this study represents adults from
American households and teachers across community schools in the region.
2. Who do they sample? Adult respondents from American households were selected randomly
from the selected population spread over a different geographical region.
3. Who conducts the research? __________________________ Who funds the research? The
National Science Foundation funds the GSS.
4. When is the data collected? All data was collected in 2018.
5. How is the data collected? In order to provide a representative sample from all parts of the
United States, the GSS uses area probabilities to pick respondents at random. The survey
itself takes up to 90 minutes to perform in person.
(C) Variables
My IV: Provide information for the IV using the format below.
IV Variable name in SPSS: FECHLD
IV Question (as asked to the respondent verbatim): Sex education should be taught in school as
part of the school curriculum as opposed to being left as a parental responsibility.
IV Answer categories: Strongly agree, Agree, Disagree, strongly disagree, don’t know, No
answer, Not
applicable
IV Level of Measurement (nominal, ordinal, interval/ratio): Nominal
My DV: Provide information for the DV using the format below.
DV variable name in SPSS: Sexual education
DV Question (as asked to the respondent verbatim)- Code respondents’ sexual education
DV Answer categories: 1. School course 2. Parental responsibility
DV Level of Measurement (nominal, ordinal, interval/ratio): Nominal
(D) Frequency Tables (20 pts)
Run frequencies for each variable listed above. Summarize your findings in a paragraph or two
below. What do the counts and valid percent columns tell you about each variable? Cite
numbers in the frequency tables to support your conclusion. Be sure to insert your tables (copy
and paste from SPSS) into this document.
Sexual education should be part of school curriculum
Sex education should be left as a parental duty
Here we had a variable named FECHLD. Based on the outcome, the dependent variable showed
that 72.4% strongly agreed that sex education should be a parental duty, however, 26.5% and
45.9% agreed that it should be part of the school curriculum. The remaining 5.1% of the
population strongly disagreed, making the total number of those who disagreed with the
statement 27.6%. Of those surveyed, 357 strongly or somewhat disagreed with the statement,
while 935 agreed or strongly.
For example, consider the SEX factor. Only male and female respondents were included in this
poll. There were a total of 1974 persons included in the sample, and 1088 of them are women.
That means 886 of the people are men. With this, we were able to see how the population was
distributed. This enabled us to determine that 44.9% of the population is composed of males.
Equally interesting is the proportion of women, which is 55.1%.
(E) Charts
Run the appropriate charts (histogram, bar chart, or pie chart) for each of your variables listed
above. Summarize your findings briefly in a paragraph or two. Cite numbers in the charts to
support your conclusion. How does the visual representation help us understand the data?
Include a title on each of your charts and other pertinent labels.
Sex education should be part of school curriculum
A bar chart depicting FECHLD is shown here. This bar chart displays the total percentage of
respondents that chose each response category for this variable. What can be learned from the
frequency chart may also be learned from this one. In one survey, 26.5% of respondents said
they sex education should be left as a parental duty.
(F) Measures of Central Tendency and Dispersion
Run the measures of central tendency (mean, median, mode) and dispersion (variance, standard
deviation) for each of your variables. Summarize your findings briefly in a paragraph or two.
Which measures are appropriate for nominal, ordinal, or interval/ratio variables? What do these
measures tell us about each variable?
A frequency distribution for FECHLD may be shown below. Central tendency and dispersion
tests have been performed on this table. As you can see, it explores the variable in more detail.
Average, middle, and outlying values as well as standard deviation, variance, and range are
shown in the table above. Using this metric, we can get the average value.
(G) Included SPV file (SPSS output of all syntax, tables and charts) – (10 pts)
Sex education should be part of school curriculum
Epsilon values in the first row of the crosstabulations table reveal a significance of 15.2
percent. The value of 1.9% may be seen in the second column. In the third column, I get
11.7%, and in the fourth, I get 1.6%. It is clear that the majority of respondents either agree
or disagree with the 10% stipulation. Since this is the case, it follows that there is need to
include sex education as part of the school curriculum.
T-Tests (30 points)
Mock Study 1: t-Test for Independent Samples (15 points)
1. Six months after an industrial accident, a researcher has been asked to compare the job
satisfaction of employees who participated in counseling sessions with those who chose
not to participate. The job satisfaction scores for both groups are reported in the table
below.
Use the five steps of hypothesis testing to determine whether the job satisfaction scores
of the group that participated in counseling session are statistically different from the
scores of employees who chose not to participate in counseling sessions at .01 level of
significance. (Clearly list each step).
As part of Step 5, indicate whether the researcher should recommend counseling as a
method to improve job satisfaction following industrial accidents based on evaluation of
the null hypothesis.
Data to be entered in SPSS (instructions below)
PARTICIPATED IN
COUNSELING
36
39
41
36
37
35
37
39
42
DID NOT PARTICIPATE IN
COUNSELING
38
36
36
32
30
39
41
35
33
Step 1: Data managing
1. Open a blank SPSS data file: File→ New→ Data
2. In the blank SPSS data file, create your SPSS data set by entering the number of activities
of daily living performed by those who participated/did not participate in the counseling
sessions (reported on previous page). Please create two columns. Column one is the test
variable, where you enter ALL the 18 scores in the table. Column 2 is the grouping
variable, where you use “1” to indicate if a score is from someone who participated in the
counseling sessions; and “0” to indicate if a score is from someone who chose not to
participate in the counseling sessions. The data set will look like this in SPSS Data View
window:
36
1
1
39 1
……….
38 0
36 0
……….
3. After data entry, go to Variable View window, change the name of the first variable (test
variable) to “ADL” and the second variable (grouping variable) as “group.” Set decimals
for both variables to zero.
Step 2: SPSS execution
a. Click: Analyze→ Compare Means→Independent-Samples T Test→ use arrow to
move ADL to “Test Variable” → use arrow to move “group” to “Grouping Variable”
→when two (? ?) appear, click Define Groups. On the next pop up window, enter “1” for
“Group 1” and “0” to “Group 2.”
b. Click OK.
1. Research Hypothesis (H1): Counseling improves job satisfaction following the industrial
accidents.
Null Hypothesis (H0): Counseling does not improve job satisfaction following the
industrial accidents.
2. Significance Level: 0.01
3. SPSS Analysis:
4. Valid Score: p = 0.103
5. Comparison and Results:
p > 0.01 = 0.103 > 0.01
*The null hypothesis must be accepted.*
Because the null hypothesis is accepted, it can be concluded that counseling does not
improve job satisfaction following industrial accidents. Therefore, I would not recommend
counseling as a method to improve job satisfaction following the industrial accident at this
company.
2
Mock Study 2: t- Test for Dependent Samples/Paired Samples (15 points)
1. Researchers are interested in whether depressed people undergoing group therapy will
perform a different number of activities of daily living before and after group therapy.
The researchers randomly selected 10 depressed clients in a 6-week group therapy
program.
Use the five steps of hypothesis testing to determine whether the observed differences in
the numbers of activities of daily living obtained before and after therapy are statistically
significant at .05 level of significance. (Clearly list each step).
As part of Step 5, indicate whether the researchers should recommend group therapy for
all depressed people based on evaluation of the null hypothesis.
Data to be entered in SPSS (instructions below)
CLIENT
A
B
C
D
E
F
G
H
I
J
BEFORE THERAPY
11
7
10
13
11
12
9
8
13
12
AFTER THERAPY
17
12
12
21
12
15
16
17
17
8
Step 1: Managing data
1. Open a blank SPSS data file: File→New→Data
2. In the blank SPSS data file, create your SPSS data set by entering the number of activities
of daily living performed by the depressed clients (see above) in the Data View window.
Enter the “before therapy” scores in the first column and the “after therapy” scores in the
second column.
3. In the Variable View window, change the variable name for the first variable to
“ADLPRE” and the second variable to “ADLPOST.” Set the decimals for both variables
to zero.
Step 2: SPSS execution
a. Click: Analyze → Compare Means →Paired-Samples t-Test → use the arrow to
move ADLPRE under “variable 1” inside Paired Variable(s) window→ and then
use the arrow to move ADLPOST under “variable 2” inside Paired Variable(s)
window.
3
b. Click OK.
1. Research Hypothesis (H1): Depressed people undergoing group therapy will perform a
different number of activities of daily living before and after group therapy.
Null Hypothesis (H0): There will be no difference in the number of activities of daily living
before and after group therapy in depressed people.
2. Significance Level: 0.05
3. SPSS Analysis:
4. Valid Score: 0.08
5. Comparison and Results:
p > 0.05 = 0.08 > 0.05
*The null hypothesis must be accepted.*
Because the null hypothesis is accepted, it can be concluded that there is no change in the
number of activities of daily living before and after group therapy in depressed individuals.
Therefore, group therapy should not be recommended for all depressed people.
4
ANOVA (15 points)
Mock Study 3: One-Way ANOVA
1. An advertising firm has been hired to assess whether different demographics have
different rates of TV watching to help determine their advertising strategy. Using the
GSS data, determine whether hours of tv watched differs by race.
Use the five steps of hypothesis testing to determine whether the observed differences in
the number of hours watching TV across three groups are statistically significant at .05
level of significance. (Clearly list each step).
As part of Step 5, indicate whether the advertising firm should target each racial group
differently (if their habits differ) based on evaluation of the null hypothesis.
Variables from GSS dataset to be used (instructions below):
RACE – race of respondent
1 = WHITE
2 = BLACK
3 = OTHER
TVHOURS – hours per day watching TV
Step 1: Data managing
1. Open a blank SPSS data file: File→ Open Data→ GSS2018.sav (from wherever you
have it saved)
Step 2: SPSS execution
a. Click: Analyze → Compare Means → One-Way ANOVA → use arrow to move
TVHOURS to “Dependent Variable list” → use arrow to move RACE to “Factor,” which
instructs SPSS to conduct the analysis of variance on the number of activities performed
by therapy type.
b. Click: Options → Descriptive (to obtain descriptive statistics).
c. Click: Continue
d. Click: OK.
1. Research Hypothesis (H1): Different demographics have different rates of TV watching.
Null Hypothesis (H0): Different demographics do not have different rates of TV watching.
2. Significance Level: 0.05
3. SPSS Analysis:
5
4. Valid Score: 0.001
5. Comparison and Results:
p < 0.05 = 0.001 < 0.05 *The null hypothesis must be rejected.* Because the null hypothesis is rejected, it can be concluded that different demographics have different rates of TV watching. Therefore, the advertising firm should target each racial group differently. Additional question based on Mock Study 3 2. Describe the circumstances under which you should use ANOVA instead of t-Tests. Explain why t-Tests are inappropriate in these circumstances. An ANOVA test should be utilized instead of t-Tests when comparing three or more populations. A t-Test can only compare and determine the statistical difference between two populations, whereas the ANOVA test can compare and determine the statistical differences between three or more populations. In these circumstances, the t-Tests are inappropriate because more than two populations (the races of the respondents) are being compared. Thus, ANOVA is the suitable option under these circumstances to ensure all of the populations can be compared. 6 Chi-Square (15 points) Mock Study 4: Chi-Square Test for Independence 1. Researchers are interested in whether US adults have different levels of confidence in Congress (legislative branch of the federal government) and how strongly each person identifies with a specific political party. These data are presented below. Following the five steps of hypothesis testing, conduct chi-square test for independence at the .05 level of significance. (Clearly list each step). As part of Step 5, indicate whether the observed frequency is significantly different from the expected frequency, and what that means in regard to this mock study. In other words, does political party affiliation effect one’s confidence in Congress? Variables from the GSS dataset to be used (instructions below): CONLEGIS – confidence in congress (legislative branch of government) 1 = A GREAT DEAL 2 = ONLY SOME 3 = HARDLY ANY PARTYID – political party affiliation 0 = STRONG DEMOCRAT 1 = NOT STR DEMOCRAT 2 = IND NEAR DEMOCRAT 3 = INDEPENDENT 4 = IND NEAR REPUBLICAN 5 = NOT STR REPUBLICAN 6 = STRONG REPUBLICAN 7 = OTHER PARTY Step 1: Data managing 1. Open a blank SPSS data file: File→ Open Data→ GSS2018.sav (from wherever you have it saved) Step 2: SPSS execution a. Click: Analyze → Descriptive Statistics → Crosstabs → use arrow to move “PARTYID” to “Column(s)”→ use arrow to move “CONLEGIS” to “Row(s).” (Recall in crosstab, DV is always in the row and IV is always in the column.) b. Click: Statistics → check “Chi-Square.” c. Click: Continue. d. Click: Cells→ check “Expected.” e. Click: Continue. f. Click: OK. 7 1. Research Hypothesis (H1): US adults have different levels of confidence in Congress that are dependent on political party affiliation. Null Hypothesis (H0): US adults do not have different levels of confidence in Congress and are not dependent on political party affiliation. 2. Significance Level: 0.05 3. SPSS Analysis: 4. Valid Score: 0.016 5. Comparison and Results: p < 0.05 = 0.016 < 0.05 *The null hypothesis must be rejected.* Because the null hypothesis is rejected, it can be concluded that US adults have different levels of confidence in Congress that are dependent on political party affiliation. Therefore, political party affiliation does affect one’s confidence in Congress. 8 Regression (15 points) Mock Study 5: Linear Regression 1. Researchers in the field of gerontology are researching the effects of age on mental health. They are using GSS data to gather some preliminary findings. Following the five steps of hypothesis testing, conduct a linear regression analysis to determine whether age affects number of poor mental health days at the .05 level of significance. (Clearly list each step). As part of Step 5, indicate whether there is a significant relationship between age and mental health at the .05 level and what does this mean in regard to this mock study. Should the researchers continue their study? Variables from the GSS dataset to be used (instructions below): AGE – age of respondent MNTLHLTH – Days of poor mental health past 30 days Step 1: Data managing 2. Open a blank SPSS data file: File→ Open Data→ GSS2018.sav (from wherever you have it saved) Step 2: SPSS execution e. Click: Analyze → Regression → Linear → use arrow to move MNTLHLTH to “Dependent list” → use arrow to move AGE to “Independent,” which instructs SPSS to conduct the linear regression on the relationship of age to poor mental health. f. Click: OK. 1. Research Hypothesis (H1): There is a significant relationship between age and mental health. Null Hypothesis (H0): There is not a significant relationship between age and mental health. 2. Significance Level: 0.05 3. SPSS Analysis: 9 4. Valid Score: 0.001 5. Comparison and Results: p < 0.05 = 0.001 < 0.05 *The null hypothesis must be rejected.* Because the null hypothesis is rejected, it can be concluded that there is a significant relationship between age and mental health at the 0.05 level. Therefore, the researchers should continue their study as there is statistical evidence to suggest that there are significant effects of age on mental health. 10 Part 2 Now it is time for you to choose and perform the correct test of significance for your project variables (from the GSS, chosen in Week 1). As we discussed previously, the levels of measurement of our variables determine which test of significance works for the research project. Here are the guidelines: 1. IV is categorical (nominal or ordinal) with two categories/values and DV is interval/ratio [DV values are independent]: Independent Samples T-test 2. IV is categorical (nominal or ordinal) with two categories/values and DV is interval/ratio [DV values are dependent]: Dependent Samples T-test 3. IV is categorical (nominal or ordinal) with more than two categories/values and DV is interval/ratio: ANOVA 4. IV and DV are BOTH categorical variables (nominal or ordinal): Chi-square • Special note for Chi-square: you should have less than 20% of the cells with an expected count of 5 or less. This information is reported automatically, right below the chi-square output table. If your chi-square test fails to meet this requirement, it is necessary to use "recoding" to combine some of the answer categories on the IV or DV to increase the expected counts. 5. IV and DV are BOTH interval/ratio variables: Regression Follow the five steps of hypothesis testing for your project variables: 1. State your research hypothesis (H1) and null hypothesis (H0). 2. Identify your significance level (alpha) at .05 or .01. (It is typical to use .05.) 3. Conduct your analysis using SPSS. 4. Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’ or ‘Asymptotic Sig.’ We will call this “p.” 5. Compare the two and apply the following rule: a. If “p” is < or = alpha, then you reject the null. b. Please explain what this decision means in regards to your project variables. (i.e., Does your IV influence your DV?) IV: FECHLD (Mother working doesn’t hurt children) DV: SEXEDUC (Sex education in public schools) 1. Research Hypothesis (H1): Individuals that believe that mothers working doesn’t hurt children also believe that sex education should be available in public schools. Null Hypothesis (H0): Individuals that believe that mothers working do hurt children also believe that sex education should not be available in public schools. 2. Significance Level: 0.05 3. SPSS Analysis: 11 4. Valid Score: 0.001 5. Comparison and Results: p < 0.05 = 0.001 < 0.05 *The null hypothesis must be rejected.* Because the null hypothesis is rejected, it can be concluded that individuals that believe that a mother worker doesn’t harm children also believe that sex education should be in public schools. Therefore, the IV does significantly influence the DV. Test of Significance on Project Variables (25 points) 12 Purchase answer to see full attachment

  
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