This document summarizes a research paper analyzing how religion, education level, and political ideology influence views on same-sex marriage. The paper uses survey data from 900 New Jersey adults. It finds that education level and political ideology significantly impact views, with more educated and liberal individuals more supportive of same-sex marriage, while religion does not have a significant influence. The paper supports the hypotheses that education exposure and liberal ideology correlate with greater acceptance, but finds only weak-to-moderate associations. It concludes that political and educational factors somewhat predict attitudes, though religion does not.
This study was a test of the hypothesis that demographic variables (e.g. gender, education) would predict who would be closed minded about the idea of asexuality as a sexual orientation. The participants received the link to the survey on the researcher’s Facebook page. The survey asked the participants’ awareness of asexuality, educational background, feelings towards the topic of sex, religious background, gender, race, age, sexual orientation, and where they were raised. The survey also asked three questions regarding the participants’ beliefs about asexuality as a sexual orientation. The results did show a significant affect on attitudes of gender, and previous education about asexuality. The study also found a strong but not significant relationship between attitudes and religiosity.
• Presented at the Third Annual Conference of the International Network for Sexual Ethics and Politics in Ghent, Belgium 2013
• Presented at the Tenth Annual Conference of The Society for the Scientific Study of Sexuality in San Diego, CA 2013
This study was a test of the hypothesis that demographic variables (e.g. gender, education) would predict who would be closed minded about the idea of asexuality as a sexual orientation. The participants received the link to the survey on the researcher’s Facebook page. The survey asked the participants’ awareness of asexuality, educational background, feelings towards the topic of sex, religious background, gender, race, age, sexual orientation, and where they were raised. The survey also asked three questions regarding the participants’ beliefs about asexuality as a sexual orientation. The results did show a significant affect on attitudes of gender, and previous education about asexuality. The study also found a strong but not significant relationship between attitudes and religiosity.
• Presented at the Third Annual Conference of the International Network for Sexual Ethics and Politics in Ghent, Belgium 2013
• Presented at the Tenth Annual Conference of The Society for the Scientific Study of Sexuality in San Diego, CA 2013
An Exploration of the Literature Concerning the Correlation
Between Child Abuse and the Subsequent Abuse of Alcohol
and Illicit Drugs by the Surviving Adult
Intimate Partner Violence and LGBT Relationshipsjayembee
This presentation describes how LGBT relationships are impacted by intimate partner abuse (IPV), and how these effects are similar or different to heterosexual relationships. A brief review of policy and law is included.
Gang Membership, Violence, and Psychiatric Morbidityjeremy coid
Gang members engage in many high-risk activities associated with psychiatric morbidity, particularly violence related ones. The authors investigated associations between gang membership, violent behavior, psychiatric morbidity, and
use of mental health services. The study concluded that gang members show inordinately high levels of psychiatric morbidity,
placing a heavy burden on mental health services. Traumatization and fear of further violence, exceptionally prevalent in gang members, are associated with service use. Gang membership should be routinely assessed in individuals presenting to health care services in areas with high levels of violence and gang activity. Health care professionals may have an important role in promoting desistence from gang activity.
An Exploration of the Literature Concerning the Correlation
Between Child Abuse and the Subsequent Abuse of Alcohol
and Illicit Drugs by the Surviving Adult
Intimate Partner Violence and LGBT Relationshipsjayembee
This presentation describes how LGBT relationships are impacted by intimate partner abuse (IPV), and how these effects are similar or different to heterosexual relationships. A brief review of policy and law is included.
Gang Membership, Violence, and Psychiatric Morbidityjeremy coid
Gang members engage in many high-risk activities associated with psychiatric morbidity, particularly violence related ones. The authors investigated associations between gang membership, violent behavior, psychiatric morbidity, and
use of mental health services. The study concluded that gang members show inordinately high levels of psychiatric morbidity,
placing a heavy burden on mental health services. Traumatization and fear of further violence, exceptionally prevalent in gang members, are associated with service use. Gang membership should be routinely assessed in individuals presenting to health care services in areas with high levels of violence and gang activity. Health care professionals may have an important role in promoting desistence from gang activity.
This article co-written by Dr. Robert J. Winn which aims to quantify the number of lesbian, gay, bisexual, and transgender (LGBT) people in Philadelphia who report to be victims of domestic violence.
For this discussion, you will use a census website that posts infoShainaBoling829
For this discussion, you will use a census website that posts information on variables observed in the city where you live. Here is the website address. https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2017/ (Links to an external site.)
(Links to an external site.)After you open the website, you can enter the name of your state on the left at the bottom and the name of your city on the right of the landing page. You will see links to 4 sets of information on your area: social, education, housing, and demographic. You will be assessing the change in one variable you select for two different years. For example, data from the entire United States could be used to compare the percentage of women never married for the years 2010 and 2017.
Once you have selected your variable and obtained the information, answer the following questions:
· Was there a difference in the values of your variable?
· How would you write the null hypothesis if you wanted to test the differences statistically?
· Does the difference appear to be a significant one? How would you substantiate that?
· Is the difference important?
· What are the consequences of the change in your values for your community? For example, a significant increase in the number of women never married could affect the birth rate. It could also mean more women are attending college and becoming self-sufficient.
Please be sure to validate your opinions and ideas with citations and references in APA format.
Name of city : ATLANTA
Name of state : Georgia
Health Care as a Social Good
This page intentionally left blank
Health Care
as a
Social Good
Religious Values and American Democracy
D AV I D M . C R A I G
G EO RG ET OW N U NI VE RS IT Y P RE SS
Washington, DC
� 2014 Georgetown University Press. All rights reserved. No part of this book may be
reproduced or utilized in any form or by any means, electronic or mechanical, including
photocopying and recording, or by any information storage and retrieval system, without
permission in writing from the publisher.
Library of Congress Cataloging-in-Publication Data
Craig, David Melville, 1965– author.
Health care as a social good : religious values and American democracy / David M. Craig.
p. ; cm.
Includes bibliographical references and index.
ISBN 978-1-62616-138-2 (hardcover : alk. paper)
ISBN 978-1-62616-077-4 (pbk. : alk. paper)
I. Title.
[DNLM: 1. Health Care Reform—United States. 2. Public Policy—United States.
3. Religion—United States. 4. Social Justice—United States. 5. Social Values—United States.
WA 540 AA1]
RA418.3.U6
362.10973—dc23
2014005920
�� This book is printed on acid-free paper meeting the requirements of the American
National Standard for Permanence in Paper for Printed Library Materials.
15 14 9 8 7 6 5 4 3 2 First printing
Printed in the United States of America
To my parents,
Ann and Norman Craig
This page intentionally left blank
C o n t e n t s
Acknowledgments ix
...
Health Care as a Social Good
This page intentionally left blank
Health Care
as a
Social Good
Religious Values and American Democracy
D AV I D M . C R A I G
G EO RG ET OW N U NI VE RS IT Y P RE SS
Washington, DC
� 2014 Georgetown University Press. All rights reserved. No part of this book may be
reproduced or utilized in any form or by any means, electronic or mechanical, including
photocopying and recording, or by any information storage and retrieval system, without
permission in writing from the publisher.
Library of Congress Cataloging-in-Publication Data
Craig, David Melville, 1965– author.
Health care as a social good : religious values and American democracy / David M. Craig.
p. ; cm.
Includes bibliographical references and index.
ISBN 978-1-62616-138-2 (hardcover : alk. paper)
ISBN 978-1-62616-077-4 (pbk. : alk. paper)
I. Title.
[DNLM: 1. Health Care Reform—United States. 2. Public Policy—United States.
3. Religion—United States. 4. Social Justice—United States. 5. Social Values—United States.
WA 540 AA1]
RA418.3.U6
362.10973—dc23
2014005920
�� This book is printed on acid-free paper meeting the requirements of the American
National Standard for Permanence in Paper for Printed Library Materials.
15 14 9 8 7 6 5 4 3 2 First printing
Printed in the United States of America
To my parents,
Ann and Norman Craig
This page intentionally left blank
C o n t e n t s
Acknowledgments ix
Introduction: Hearing Health Care Values 1
PART ONE
The Moral Languages of US Health Care
Chapter 1: Health Care as a Private Benefit or Private Choice 27
Chapter 2: Health Care as a Public Right 54
Chapter 3: Health Care as a Social Good 85
PART TWO
Religious Values in Health Policy, Markets, and Politics
Chapter 4: Modeling Community Benefits: Social Contract, Common
Good, Covenant 123
Chapter 5: Assessing Market-Driven Reforms: Economy without
Solidarity 153
Chapter 6: Building Solidarity: Religious Activism and Social Justice 183
Conclusion: Religious Values and Community Care 214
Bibliography 239
Index 257
vii
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A c k n o w l e d g m e n t s
This project began years ago in a conversation with my cousinCurt Williams about Catholic hospitals and economic justice.
Although my research expanded into many other conversations, I hope that
Curt’s passion for justice shines throughout the book. My interest in ethics and
public policy dates back even further to family dinner conversations in my
youth. My parents’ engagement with public affairs and their dedication to the
common good informed my desire to join the health care reform debate. Ann
and Norman Craig’s reflective commitment to living out their religious values
is a model for the dialogue and work I foresee in the years ahead. I dedicate this
book to them. My wife Jocelyn Sisson gave her constant support—intellectual,
editorial, and soulful—throughout the project. Our daughters Claudia and Eliza
Craig kept me going with thei ...
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
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Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
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By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
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This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. Bhumi Patel
Mona Patel
December 15, 2014
Research Methods: Section 03
Final paper: Data Analysis
Introduction
Between 1998 and 2006, twenty-seven states had amended the constitution
of their state to completely prohibit same-sex marriages. Then again in 2008, three
states had ballots that targeted banning same-sex marriages. Same-sex marriages
had been a taboo for so long that when there was an increase in awareness, many
people had different ways of dealing with it. Some blamed the gays for choosing
their lifestyle, some blamed biology, some blamed God, and some blamed the
parents of the homosexuals. It’s these attributions and reasoning that can predict
whether a person supports or opposes gay marriages.
In Sarah William’s “Left-Right Ideological Differences in Blaming Victims”,
Williams reported that liberals are more likely to attribute homelessness to market
forces and depend on the government to help solve that problem. On the contrary,
conservatives are more likely to blame homelessness on personal dispositions and
therefore don’t rely on the government to help resolve the issue of homelessness.
These two sceneries show how the differences in ideological beliefs attribute to how
a person thinks as a liberal and as a conservative. We can use this model to project
how these perspectives can affect the views on same-sex marriages. The liberals will
take away the blame from an individual whereas conservatives will attribute being
gay as a personal responsibility. Not only will views on homosexuality be
determined by political ideology, but also education. Early philosophers such as
Aristotle and Plato pointed out that education was central to the moral fulfillment of
individuals and the well being of the society in which they live. Data show that
adults who have attained higher levels of education are generally more likely than
those with lower levels of educational attainment to report stronger civic
engagement, in terms of voting, volunteering, political interest, and interpersonal
trust (What are the Social Benefits of Education). From this we see how educated
individuals will be more involved in society by using the skills inherited through
education to benefit others. Lastly, we see how certain religions influence people’s
lifestyles and viewpoints. Support for same-sex marriages and views of
homosexuality come from certain religious denominations that are reformed or
liberal. Opposition of civil union between homosexuals come from religious
denominations that are orthodox or conservative in their views.
The values and norms of society have been consistently changing
significantly as generations have gone by. One of the most controversial issues we
face today is the morality and legality of same-sex marriages. The reason why this
topic is so important and why we chose to talk about it is because it is so common in
today’s day and given the least attention. This report studies how the level of
2. education completed, what religion a person practices and the level of ideology
influences a person’s view on same-sex marriages.
Hypothesis
We looked at how religion, education and ideology play a role in a person’s
stand on same-sex marriage.
We hypothesized that same sex marriages are influenced by religious beliefs
because orthodox religions are more likely to be conservative and traditional about
their views and reformed religions are more likely to be open to change. This leads
us to believe that orthodox religions will be against same-sex marriages and
reformed religions will support same-sex marriages.
The second hypothesis is that education influences an individual’s view on
same-sex marriages because the more educated a person is, the more likely they will
be aware of the scientific reasons behind why someone is a homosexual.
Lastly, we hypothesized that ideology influences a person’s view on same-sex
marriages because depending on if they identify themselves as conservative,
moderate, or liberal, their understanding of same-sex marriages will be affected.
Data and Methods:
The dataset used for this study was from the New Jersey Omnibus Survey.
The data collected was from the Rutgers University Eagleton Institute of Politics.
The purpose of this poll was to study issues relating to Superstorm Sandy and other
concerns in New Jersey. The information was collected from March 8th to March
17th, 2013. The sample size of this study was 900 NJ adults. To be considered an
adult in New Jersey, you have to be 18 years or older. The survey was complied
through the use of random sampling digital dialing (RDD) by landline and a cell
phone. To be consistent in this survey, the 900 adults used in this survey were
representative of the whole population of New Jersey. The 900 adults included both
males and females. The margin of error between sample size and sampling error is
3.3.
The dataset asked questions ranging from the effects of Superstorm Sandy,
views on same-sex marriages, political affiliation and political ideology, religion,
income, and marital status. The four variables we used for this report were religion,
education, ideology and gay views. To better understand the results of the survey
questions regarding Superstorm Sandy and views on same-sex marriage, a
demographics section was used, which included questions on religion, education,
and political ideology. The survey asked if the interviewee was Catholic, Protestant,
Jewish, Muslim, some other religion, Atheist or agnostic. They could either answer
by saying one of the choices from the question, refuse to answer or say they don’t
know. To get a better understanding and reading of the sample population, the nine
3. options were recoded to four: Catholic, Protestant, Jewish, and Other religion. The
surveyor was also asked what the last grade in school they completed was. They
could choose: 8th Grade Or Less, High School Incomplete (Grades 9, 10 and 11),
High School Complete (Grade 12), Vocational/Technical School, Some College, Junior
College Graduate (2 Year, Associates Degree), 4 Year College Graduate (Bachelor’s
Degree), Graduate Work (Masters, Law/Medical School, Etc.), or Refused. It was
then recoded to: HS or less, Some college, College graduate, and Graduate work. The
surveyor was also asked if they consider themselves to be liberal, conservative, or
somewhere in between. They could answer with Liberal, Conservative, Somewhere
in between, Other, Don’t know, or Refused. It was later recoded to: Liberal,
Moderate, and Conservative. Lastly, the survey asked the individual what their
position on same-sex marriage was and they could respond with: Oppose same-sex
marriage, Support civil unions and oppose same-sex marriage, Don’t Know, or
Refused. It was then recoded to: Oppose, Support, or Support CU but Oppose
Marriage. Before these questions were asked, the surveyor was told that there is no
right or wrong answer and they were only interested in their opinion. They are as
important as anyone else’s. By saying this, the surveyor would be more inclined to
answer truthfully no matter what they believe
Table 1. Religion Frequency
Note: % is weighted, n is unweighted
Of the total number of people who were surveyed, 45% identified themselves
as Catholics, roughly 17% identified themselves as Protestant, and 6.2% were
Jewish and more than a quarter identified themselves as Other.
Table 2. Education Frequency
Percent n
Catholic 45.0% 413
Protestant 17.2% 158
Jewish 6.2% 57
Other 26.1% 240
Total 94.6% 868
Percent n
HS or Less 24.1% 221
Some College 22.4% 206
College Grad 30.7% 282
Grad Work 21.8% 200
Totals 99.0% 909
4. Note: % is weighted, n is unweighted.
Out of the total number of people who participated in the survey, almost a
quarter attended High School or Less. Roughly the same amount of people attended
Some College and Graduate Work, which was around 22%. Lastly, close to 1/3 of the
total number of participants were college graduates.
Table 3. Ideology Frequency
Note: % is weighted, n is unweighted.
When asked how the surveyors would rate their political affiliation, 24% of
the population said they have a liberal political ideology, whereas more than half
said they have a moderate political ideology. Only 21% of responders stated they
have a conservative political ideology.
Table 4. Combined Gay Views Frequency
Note: % is weighted, n is unweighted.
When the surveyors were asked what their views on homosexuals were,
more than 50% of them stated they supported homosexuality in New Jersey. On the
contrary, a little less than a quarter opposed gay views and 6% stated they
supported civil union, but opposed marriage.
Percent n
Liberal 24% 211
Moderate 55% 491
Conservative 21% 190
Total 100% 892
Percent n
Support 57.7% 530
Oppose 24.7% 227
Support CU but
Oppose Marriage
6.4% 59
Total 11.1% 816
5. Findings
Table 1a. Crosstabulation of Religion and Gay views
Note: % is weighted, n is unweighted; p-value is 0.316 and Cramer’s V is 0.067
The crosstab above examines two variables that were tested, Religion and
Gay Views. By picking these two variables, we wanted to analyze how religion
influences an individual’s support or opposition of gay views. Our hypothesis states
that orthodox religions will oppose gay views because of their tradition and values.
On the contrary, reformed religions would be more likely support gay views. Here
we see that more than half the responders in each religion stated that they support
gay views. The univariate frequency shows that 58% of people that answered
supported gay views. When we tested our variables against each other, 62% and
64% of Catholics and Protestants, respectively, supported gay views. This shows us
that there wasn’t a big difference between the groups and their support for gay
views. Jewish believers had higher rates of support, 78%, which was 20% higher
than the univariate frequency table. Although the category “Other” has about 70%
support rate, four religious views were combined together to make up this
percentage. This is not a reliable or valid number to use for our hypothesis. The p-
value, 0.316, tells us that we failed to reject the null hypothesis and that it is not
statistically significant. This tells us that religion does not affect a person’s
viewpoint on homosexuality. The Cramer’s V value, 0.067, tells us that there is a
weak association between the two variables.
Table 2a. Crosstabulation of Education and Gay views
HS or Less Some Coll Coll Grad Grad Work Total
Support 62.2% 67.7% 66.5% 71.0% 66.7%
Oppose 33.7% 23.1% 24.8% 25.9% 26.8%
Support CU But
Oppose Marriage
4.1% 9.1% 8.6% 3.1% 6.6%
Catholic Protestant Jewish Other Total
Support 64.0% 61.7% 77.8% 69.8% 66.3%
Oppose 28.3% 30.8% 17.8% 25.4% 27.1%
Support CU But
Oppose Marriage
7.8% 7.5% 4.4% 4.8% 6.6%
Total 100.0% 100.0% 100.0% 100.0% 100.0%
n 361 120 45 252 778
6. Total 100.0% 100.0% 100.0% 100.0% 100.0%
n 193 186 266 162 807
Note: % is weighted, n is unweighted; p-value is 0.027 and Cramer’s V is 0.094
Represented in the crosstab above is the relationship between Education and
Gay Views. The hypothesis for this association was that the more educated an
individual is, the more likely they are to rely on scientific reasoning and in turn
support gay views. When comparing the univariate analysis to the crosstab, we see
that there isn’t a strong correlation between HS or Less (62%), Some College (68%)
and College Graduate (67%) with those who supported gay views in the univariate
analysis, which was 58%, because they are within 10% of each other. 71% of
Graduate Workers supported gay views, which had a 13% difference. This backs our
hypothesis because people who completed the highest level of education, had a
higher support rate than the other categories. The p-value, 0.027, tells us that we
can reject the null hypothesis and the association is statistically significant. We can
gather that education does factor in on gay views. The Cramer’s V value, 0.094, tells
us that the association is weak.
Table 3a. Crosstabulation of Political Ideology and Gay views
Liberal Moderate Conservative Total
Support 82.2% 71.0% 32.2% 66.5%
Oppose 11.94% 22.3% 59.1% 26.6%
Support CU But Oppose
Marriage
5.9% 6.7% 8.7% 6.9%
Total 100.0% 100.0% 100.0% 100.0%
n 202 435 149 786
Note: % is weighted, n is unweighted; p-value is 0.000 and Cramer’s V is 0.270
Lastly, as demonstrated in the crosstab, we observed the relationship
between Political Ideology and Gay Views. Firstly, looking at the date above, we see
a pattern emerging. Support of gay views declines as it goes from Liberal, to
Moderate and to Conservative. On the contrary, we see an increase for opposition as
it goes from Liberal, to Moderate, and to Conservative. Without looking at the
statistics, we see a correlation between the data and our hypothesis. In our
hypothesis, we stated that a person’s ideology influences an individuals view on
homosexuality. Liberals would most likely support gay views because their thinking
is inclusive whereas, conservatives would oppose homosexuality because they are
less likely to conform to modern societal norms. Secondly, the statistics also support
our hypothesis. The p-value, 0.000, tells us that we can reject the null hypothesis
and the association between the variables is statistically significant. Political
ideology does affect a person’s view on homosexuality. The Cramer’s V value, 0.270,
7. tells us that there is a moderate association.
Conclusion/Discussion:
In today’s society, many still face challenges in adapting, adjusting and
adhering to different cultures. It is the 21st century and we would think people
would be more accepting to each other’s religion, lifestyle choices, race and much
more, yet we see the complete opposite. There are protests to ban same-sex
marriages and laws in some states that say marriage between two men and two
women is illegal and unworthy in God’s eyes. They blame homosexuals for choosing
their lifestyle when in reality it is not their choice. In the scholarly journal
“Neurohormonal functioning and sexual orientation: A theory of homosexuality-
heterosexuality”, it states that sexual orientation is categorized as genetic-hormonal,
pharmacological, maternal stress, immunological, and social experimental. We also
see the opposite side of the spectrum in which people and societies are extremely
welcoming of the gay community. Homosexuality wasn’t recently discovered or
invented. There has always been some form of homosexual demographic groups in
society, but never talked about. Now, people are coming out and want to be seen as
equals to heterosexuals. It is only fair that we, as a community and nation, listen
without interrupting. Often times, religious beliefs, conservative views and lack of
education play a role in hindering the assimilation of homosexuals in a society.
This research paper aims to look at how religious beliefs, political ideology
and level of education can determine if an individual will support or oppose views
on sexual orientation. We have learned that two out of the three independent
variables, ideology and education, do in fact affect an individual’s view on
homosexuality. Religion is the only variable that does not affect a person’s view of
homosexuality. Our first hypothesis could not be supported with our findings when
we ran the bivariate analysis. Our hypotheses for ideology and education were
supported with our findings. Our second hypothesis stated that the level of
education affects a person’s view on homosexuality. Our findings also supported this
prediction, but with a weak association. We then hypothesized that political
ideology, identifying oneself as liberal, moderate, or conservative, plays a role in
influencing an individual’s stance on gay views. Our findings supported this
prediction, but with a moderate association. The reason we could support our
hypotheses is because our p-values were always less than 0.05, which means we
could reject the null hypothesis making the predictions statistically significant.
However, even though there was a statistical significance, the Cramer’s V correlation
was either weak or moderate in degree of association.
The data was only collected from 900 adults in New Jersey, but can be
representative to the greater population of New Jersey, which was 8,899,339 in
2013 (United States Census Bureau). Overall, there is a higher support rate for
homosexuality than opposition in this state. For this reason, on October 21, 2013,
same-sex marriages were legalized in New Jersey after Governor Chris Christie
conceded to defeat (NJ.com). At the same time, there are conservative residents in
8. New Jersey who have a hard time adjusting to the changes, but majority have
adapted to these modified views. Some limitations that we faced in this study were
that the religion variable was too generalized. The popular religions practiced in
New Jersey should have been used in the survey and this way, we could’ve gotten a
clearer understanding as to how much of a role religion plays on a person’s stance
on gay views. Even though we concluded that having a higher level of education
completed leads to support of gay views, we can’t generalize this finding to the
greater population outside of New Jersey. There are people like Bill Gates, Steve Jobs
and Mark Zuckerberg who do not have a college degree, yet their success is
equivalent or greater than many who do graduate work. They support gay pride and
dedicate their time and money for the homosexual community. The survey might
yield different results if they conducted it again after the legalizing of same-sex
marriages. We can’t say that the survey is reliable or valid since it wasn’t conducted
more than once. Lastly, a factor that we didn’t take into account with political
ideology is that 55% of the people interviewed considered themselves to be
moderates. This skews the results because it doesn’t give us a good analysis on the
impact of liberal and conservative viewpoints on same-sex unions. The survey
should have only used two options, liberal and conservative, in order to have better
data analysis. Because sexual orientation is a huge controversy in the United States,
surveys like the New Jersey Omnibus should be conducted statewide. This would
help the government see where the people of their state stand on serious
controversies like same-sex marriages.
9. References:
Scholarly Journals:
Ellis, Lee; Ames, M. Ashley, Mar 1987, Psychological Bulletin Neurohormonal
functioning and sexual orientation: A theory of homosexuality–heterosexuality, Vol
101(2), 233-258. Retrieved from <http://dx.doi.org/10.1037/0033-
2909.101.2.233>
Haider-Markel, D., & Joslyn, M. (2008). Beliefs About The Origins Of Homosexuality
And Support For Gay Rights: An Empirical Test Of Attribution Theory. Public Opinion
Quarterly, 72(2), 291-310. Retrieved December 15, 2014, Retrieved from
<http://poq.oxfordjournals.org/content/72/2/291.full#content-block>
Sherkat, D., Vries, K., & Creek, S. (2010). Race, Religion, and Opposition to Same-Sex
Marriage. Social Science Quarterly, 91(1), 80-98. Retrieved December 15, 2014,
Retrieved from <http://onlinelibrary.wiley.com/doi/10.1111/j.1540-
6237.2010.00682.x/full>
Williams, S. (1984). Left-Right Ideological Differences in Blaming Victims. Political
Psychology, 5(4), 573-573. Retrieved December 15, 2014, Retrieved from
<http://www.jstor.org/stable/pdfplus/3791228.pdf?acceptTC=true&jpdConfirm=t
rue>
Other:
Star-Ledger, S. (2013, October 22). N.J. legalizes gay marriage after decade-long
push. Retrieved December 15, 2014, Retrieved from
<http://www.nj.com/politics/index.ssf/2013/10/nj_legalizes_gay_marriage_after_d
ecade-long_push.html>
United States Census Bureau. (2013, January 1). Retrieved December 15, 2014,
Retrieved from <http://quickfacts.census.gov/qfd/states/34000.html>
What are the social benefits of education? (2013, January 1). Retrieved December
16, 2014, Retrieved from <http://www.oecd.org/education/skills-beyond-
school/EDIF 2013--N°10 (eng)--v9 FINAL bis.pdf>