Description
add 2-3 pages onto this paper that is previously written and add the following:1. A descriptive statistics table 2. Analysis 1 – average tech attitudes by gender (bar graph) 3. Analysis 2 – average tech by age groups (bar graph).
Do that, write about your findings
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Uglialoro 1
Julianna Uglialoro
Professor Svec
SOC 212
4 December 2023
Average Differences in Tech Attitudes by Gender
Introduction
The primary motivation for conducting the study lies in understanding the patterns,
frequency, and nature of technology use among the population. This motivation spells a greater
understanding of core factors pertaining to greater use of the internet and technology. The
contemporary environment enjoys an established internet infrastructure that has been utilized by
social media platforms. As a method to change the traditional communication systems and improve
the interaction between people, the attitude towards internet use depicts the level of addiction in
the population. There is a disparity in the level of internet between genders and different ages. It
is essential to note that females and older people are becoming addicted to their technological
gadgets through consuming internet content. This is an indicator of the role of the internet in
influencing the population, especially females.
Background
Modernization and industrialization played a pivotal role in making work easier for
contemporary society. Among the products of modernization is the improved innovation in the
technology sector, which has produced portable gadgets. Also, the internet has been incorporated
into technology and has created avenues for companies to exploit. The aftermath of the internet is
social media, which is a platform to share information and creative content. Gender disparity in
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internet use is also an expected phenomenon since females are more social, and their male
counterparts are continuously seeking ways of engagement (Khan, 237). Also, the nature of
females in exploring content, such as admiring the lifestyle of other people and searching for
commodities, explains why their attitude towards internet use is different. The older people are
also part of the social construct, but their experience growing up was different. The contemporary
environment is an age of information and trends, and it is hard to keep up. With reduced social life
and work, older people have time to explore the internet and try to catch up with information.
The attitudes towards internet use spell the nature of the application of technology devices
within the population. Cai et al. (2017) detail that males show a favorable attitude towards internet
use, detailing critical understanding and appeal to internet use patterns as they relate to the
community. The nature of using the internet, however, does not show a massive difference with
the female gender, spelling a widespread understanding of the significance of the internet in the
community. Therefore, internet use patterns differ based on gender, self-efficacy, affect causes,
and beliefs about the internet. The attitude towards internet use is critical in creating relevant
insight into every aspect of usage and undertakings.
Methodology
Data is taken from the general population based on random sampling. More to the point,
the data is collected on age, gender, income category, marital status, and attitude toward the
Internet. The data analysis is conducted through statistical analysis of SPSS to detail every possible
outcome. Moreover, Excel is a vital tool with functionalities that can expose some of the anomalies
in the data. Therefore, the use of SPSS and Excel in exploring the data can be integral in
showcasing patterns and relationships of variables. The General Social Survey (GSS) monitors the
trends, behaviors, and attitudes of the population by performing cross-sectional interviews (Wolf,
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625). These interviews are structured to represent and evaluate the nature of the social sphere and
are an indicator of prevalent social problems existing in society. The collection of samples
considers the diversity in the population and employs all types of methods, including the utilization
of technology. The data has parameters such as hours spent on TV, and this can showcase the
differences in tech attitudes by gender.
Analysis
Comparative analysis on the use of technology by different genders and grouping of data
avails relevant data. Using statistical means of central tendencies such as averages is also
significant in depicting the variations in tech attitudes by gender. Although the focus is disparity
by gender, other parameters, such as age groups, can lead to the insertion of scatter plots and bar
graphs, which are effective visual representations of different data sets.
Results
The study details that gender influences attitudes toward internet technology use. This also
infers the possibility of lesser usage among females and older people. Females and older people
consume more time on television when compared to males and young individuals. On internet use,
the males dominate since they are techno-savvy and integrate easily with new devices.
Conclusion
Gender and age play a critical point in addressing internet usage patterns. Females have
less internet usage, based on their beliefs, while males have a higher possibility of internet usage.
More to the point, older people have a lesser frequency of using the internet, as they believe they
need help understanding internet use. On the contrary, females use a significant portion of their
daily hours to watch television since they might be staying at home while their partners are at
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work. Also, the old have more time courtesy of their job status and use television as a form of
entertainment.
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Works Cited
Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis.
Computers & Education, 105, 1-13.
Khan, M. Laeeq. “Social media engagement: What motivates user participation and consumption
on YouTube?.” Computers in human behavior 66 (2017): 236-247.
Wolf, Christof, et al. “Conducting general social surveys as self-administered mixed-mode
surveys.” Public Opinion Quarterly 85.2 (2021): 623-648.
2022 GSS
(Cross-section
Study)
DOCUMENTATION
AND PUBLIC USE
FILE CODEBOOK
(Release 1)
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CONTENTS
Citation of This Document ………………………………………………………………………………………………………………….. iv
Copyright 2021-2023 NORC………………………………………………………………………………………………………………… iv
INTRODUCTION ………………………………………………………………………………………………………………………………………………… 1
Introduction to the General Social Survey (GSS) ……………………………………………………………………………………. 1
Introduction to the International Social Survey Programme (ISSP)………………………………………………………….. 2
Study Overview …………………………………………………………………………………………………………………………………… 4
Table 1. Key Aspects of the 2022 GSS Cross-section……………………………………………………………………………. 5
Datafile Overview ……………………………………………………………………………………………………………………………….. 6
NOTE ON MEASUREMENT AND INTERPRETATION ………………………………………………………………………………………… 7
Total Survey Error Perspective for GSS Trend Estimates ………………………………………………………………………….. 7
DESIGN GOALS OF THE 2018-2024 GSS AND TRANSITION EXPERIMENTS…………………………………………………. 10
DESIGN OF THE 2022 GSS CROSS-SECTION …………………………………………………………………………………………………. 12
Summary of major changes ………………………………………………………………………………………………………………. 12
Multimode Design (Contact Protocol and Survey Mode) ………………………………………………………………………… 12
Respondent Selection ………………………………………………………………………………………………………………………………. 13
Follow-on Studies …………………………………………………………………………………………………………………………………….. 13
Table 2. Key Aspects of the 2022 Follow-on Studies …………………………………………………………………………… 14
Oversample………………………………………………………………………………………………………………………………………………. 15
NOTES ON THE WEB MODE IN THE 2022 GSS ………………………………………………………………………………………………. 16
Web Mode Wording Changes ………………………………………………………………………………………………………………….. 16
Don’t Know and No Answer Responses…………………………………………………………………………………………………… 16
SEX …………………………………………………………………………………………………………………………………………………………… 17
Optimization of Items for Web Mode ………………………………………………………………………………………………………. 17
Screenshots of Changes……………………………………………………………………………………………………………………. 18
Figure 1: Visual Display of a Survey Question in the 2022 GSS Cross-section web mode…………………… 18
RESERVE CODES AND MISSING VALUES……………………………………………………………………………………………………….. 19
Reserve Codes for Missing Values……………………………………………………………………………………………………… 19
2022 GSS Cross-section-specific Layout and Experiments ……………………………………………………………………………. 21
GSS Layout & Questionnaires …………………………………………………………………………………………………………….. 21
Figure 2: Questionnaire Layout for the 2022 GSS Cross-section …………………………………………………………. 21
Experiments in the 2022 GSS Cross-section ……………………………………………………………………………………….. 22
Mode Assignment and Non-response Follow-up Experiment …………………………………………………………………. 22
Figure 3: Mode Assignment and Non-response Follow-up Experiment Design …………………………………… 22
Volunteered Responses …………………………………………………………………………………………………………………………… 23
Use of Grids ……………………………………………………………………………………………………………………………………………… 24
Figure 4: Example of Gridded Variables—Set 1…………………………………………………………………………………….. 24
Gender-neutral Wording …………………………………………………………………………………………………………………….. 25
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Vocabulary Experiment ……………………………………………………………………………………………………………………………. 25
Other Wording Experiments ……………………………………………………………………………………………………………….. 25
Household Composition ……………………………………………………………………………………………………………………. 26
Summary …………………………………………………………………………………………………………………………………………. 26
Table 3: Experiments and Variables……………………………………………………………………………………………………… 26
Future Releases………………………………………………………………………………………………………………………………… 29
FIELDING …………………………………………………………………………………………………………………………………………………………. 30
Figure 5: Number of Interviews Completed per Month in 2022 …………………………………………………………… 30
SAMPLING DESIGN AND WEIGHTS ………………………………………………………………………………………………………………… 32
The 2022 GSS Cross-section Sample …………………………………………………………………………………………………. 32
Table 3: Yields by Experimental Condition …………………………………………………………………………………………… 33
GSS Post-stratification Weights …………………………………………………………………………………………………………. 33
Post-stratification Weighting Methodology Steps in 2022 ………………………………………………………………………. 33
Historic Weights for the GSS Cumulative Cross-section……………………………………………………………………….. 36
HOW TO READ GSS CODEBOOK TABLES ……………………………………………………………………………………………………….. 38
Figure 6: Codebook table excerpt…………………………………………………………………………………………………………. 38
Appendix A: 2022 GSS Cross-section Outcomes ……………………………………………………………………………………………. 40
Table A1: Unweighted 2022 GSS Cross-section Response Rate Information ……………………………………… 40
Appendix B: Mode Sensitivity in the 2022 GSS Cross-Section ………………………………………………………………………… 41
Introduction ………………………………………………………………………………………………………………………………………….. 41
Preliminary analysis ……………………………………………………………………………………………………………………………… 41
Preliminary results………………………………………………………………………………………………………………………………… 42
Appendix C: Sample Code for Analysis ……………………………………………………………………………………………………………. 45
Data Use ………………………………………………………………………………………………………………………………………….. 45
SAS only …………………………………………………………………………………………………………………………………………………… 45
STATA only ………………………………………………………………………………………………………………………………………………. 45
SAS …………………………………………………………………………………………………………………………………………………………… 46
STATA ………………………………………………………………………………………………………………………………………………………. 46
Data Analysis (example) ……………………………………………………………………………………………………………………. 46
SAS …………………………………………………………………………………………………………………………………………………………… 46
STATA ………………………………………………………………………………………………………………………………………………………. 46
APPENDIX D: Other GSS Datasets and Documentation ………………………………………………………………………………….. 47
Table C1: GSS Design Features: Cross-sectional and Panel Components ………………………………………….. 48
2022 GSS Cross-section Public Use File Codebook………………………………………………………………………………………… 49
CODEBOOK | iv
Citation of This Document
In publications, please acknowledge the original source. The citation for this Public Use File is:
Davern, Michael; Bautista, Rene; Freese, Jeremy; Herd, Pamela; and Morgan, Stephen L.; General Social Survey
1972-2022. [Machine-readable data file]. Principal Investigator, Michael Davern; Co-Principal Investigators,
Rene Bautista, Jeremy Freese, Pamela Herd, and Stephen L. Morgan. NORC ed. Chicago, 2023. 1 datafile
(Release 1) and 1 codebook (2022 Release 1).
Copyright 2021-2023 NORC
Permission is hereby granted, free of charge, to any person obtaining a copy of this codebook, portions thereof,
and the associated documentation (the “codebook”), to use the codebook, including, without limitation, the
rights to use, copy, modify, merge, publish, and distribute copies of the codebook, and to permit persons to
whom the codebook is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or portions of the
codebook. Any distribution of the codebook must be free of charge to the recipient, except for charges to
recover duplicating costs.
The codebook is provided “as is,” without warranty of any kind, express or implied, including but not limited to
the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the
authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract,
tort or otherwise, arising from, out of, or in connection with the codebook or the use or other dealings in the
codebook.
Please contact the GSS team at [email protected] with any questions or requests.
CODEBOOK | v
2022 GENERAL SOCIAL SURVEY CROSS-SECTION CODEBOOK, RELEASE 1
(Codebook for the Machine-Readable Data File 2022 General Social Survey Cross-section)
Principal Investigator
Michael Davern
Co-Principal Investigator
René Bautista
Co-Principal Investigator
Jeremy Freese
Co-Principal Investigator
Pamela Herd
Co-Principal Investigator
Stephen L. Morgan
Senior Advisors
Colm O’Muircheartaigh
Susan Paddock
Tom W. Smith
Research Associates (in alphabetical order)
Katherine Burda
Steven Pedlow
Ned English
Benjamin Schapiro
Beth Fisher
Jodie Smylie
Walter Hanley
Jaesok Son
Amy Ihde
Rachel Sparkman
Eyob Moges
Andrew Stern
Abigail Norling Ruggles
Brian Wells
Produced by
NORC at the University of Chicago
This project was supported by
the National Science Foundation
CODEBOOK | 1
INTRODUCTION
Introduction to the General Social Survey (GSS)
The General Social Survey (sometimes, General Social Surveys) is a series of nationally representative crosssectional interviews in the United States that have occurred since 1972. The GSS collects data on contemporary
American society to monitor and explain trends in opinions, attitudes, and behaviors. The GSS has adapted
questions from earlier surveys, thereby allowing researchers to conduct comparisons for up to 80 years. Originally
proposed and developed by James A. Davis, the GSS has been administered by NORC at the University of Chicago
(NORC) and funded by the National Science Foundation (NSF) since its inception. Currently, the GSS is designed
by a set of Primary Investigators (PIs), with input from the GSS Board, comprised of notable researchers within
the scientific community.
The GSS contains a standard core of demographic, behavioral, and attitudinal questions, plus topics of special
interest. Among the topics covered are civil liberties, crime and violence, intergroup tolerance, morality, national
spending priorities, psychological well-being, social mobility, and stress and traumatic events. Altogether, the GSS
is the single best source for sociological and attitudinal trend data covering the United States. It allows researchers
to examine the structure and functioning of society in general, as well as the role played by relevant subgroups
and to compare the United States to other nations. The GSS aims to make high-quality data easily accessible to
scholars, students, policymakers, and others, with minimal cost and waiting.
The GSS has been tracking trends in public opinion since 1972. Throughout, the GSS has taken great care to keep
the survey methodology as comparable over time as possible, which includes everything from keeping the same
sampling approach to not changing question wording. This is done to minimize potential changes due to changes
in methodology and support the study of trends in public opinion in the United States over time. However, due to
the global COVID19 pandemic, the 2021 GSS Cross-section implemented significant methodological adaptions for
the safety of respondents and interviewers, most notably shifting to an address-based sampling with push to web
and a web self-administered questionnaire. The 2022 GSS Cross-section bridges the traditional face-to-face data
collection of the GSS from 1972-2018 with the web-based collection of 2021, keeping many of the questionnaire
changes brought about by the new mode, while reverting to a mixed mode data collection that include face to face,
web and telephone. The 2022 GSS was designed to facilitate a comparison to the 2018 GSS; that is, the 2022 study
aimed to resemble the 2018 GSS. Additionally, the 2022 GSS retained several methodological experiment
conducted in the 2021 GSS that are specific to the web mode. The 2022 GSS is the first round of a multi-round
transition to a mixed-mode survey, fielded both face-to-face and via web self-administered questionnaire.
In 2022 the GSS conducted a methodological experiment to (1) help bridge the 2018 and 2021 data with future
rounds of the GSS and (2) to help rein in growing costs associated with conducting the more traditional in person
field study. The experiment divided the 2022 GSS sample into two conditions. The first condition contacted
sampled households for an in-person field interview first (as has been traditionally done in the GSS) and then
nonrespondents were encouraged to take the survey using a web alternative. In the second condition, sampled
households were presented with a web survey first and then nonrespondents were approached to participated in
an in-person field interview. Details on this experiment are below but the overall goal of the experiment was to
allow analysts to be able to parse out selection into mode differences from potential effects of the mode the survey
was completed in. For more information, see DESIGN GOALS OF THE 2018-2024 GSS AND TRANSITION
EXPERIMENTS.
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The GSS comprises a core set of items (the Replicating Core) that are repeated every round, as well as topical
modules, which may or may not be repeated. The GSS is currently composed of three separate ballots (A, B, and
C), as well as two separate forms (X and Y), which allow for up to six different paths through the interview itself (in
addition to paths determined by respondent answers, such as questions about spouses or partners, or questions
on employment). Not every question in the Replicating Core is asked of every respondent; most only appear on
two of the three ballots. However, every item in the Replicating Core overlaps on at least one ballot with every other
item in the Replicating Core, ensuring that researchers can estimate inter-item correlations. Forms are used for
experiments such as wording variations within questions, ensuring that half of the respondents on each ballot see
the experimental or control conditions of each relevant variable. Within the GSS, these form experiments are
usually assigned mnemonics that end in -Y.
Topical modules are typically assigned to either two full ballots (e.g., A and B) or one full ballot and one half-ballot
(e.g., A and BX), covering two-thirds or half of sample respondents, respectively. However, some topical modules
are included on all ballots. Modules are usually assigned to specific ballots based on one of two conditions: overlap
with other key questions (either ensuring that respondents to specific items also receive specific modules or that
respondents to specific items do not receive specific modules), or time constraints. The GSS tries to balance the
length of all six paths to be approximately equal. Topical modules may be administered via interviewer in any mode
or completed by self-administered questionnaire, depending on the sensitivity of the items included.
Topical modules come from several different sources. While the GSS broadly is funded by NSF, individual modules
may be sponsored by other government agencies, universities, research institutes, or individuals. The GSS typically
includes modules every round that are related to the International Social Survey Programme (ISSP), a consortium
of national-level studies like the GSS (for more information, see Introduction to the International Social Survey
Programme, below). Finally, modules may be solicited by the GSS Scientific Advisory Board or the Principal
Investigators and can be included based on scientific merits and available time in the interview. The number of
GSS modules varies by year.
Additionally, the GSS has implemented experimental designs over time or through collaborations (for instance,
supporting other studies such as the National Organization Studies, National Congregations Study, National
Voluntary Associations Study, and the 2016-2020 General Social Survey-American National Election Studies (GSSANES) Panel), which have led to several ancillary datasets.
Introduction to the International Social Survey Programme (ISSP)
The ISSP is a consortium of nationally representative research studies, like the GSS, who have all agreed to ask
questions on the same topics on an annual basis. It emerged out of bilateral collaboration with NORC and the
German organization Zentrum für Umfragen, Methoden, und Analysen (ZUMA; now part of GESIS-Leibniz Institute
of the Social Sciences). Starting in 1982, each organization devoted a small segment of their national surveys,
ALLBUS and GSS, to a common set of questions. The ISSP was formally established in 1984 by Australia, Germany,
Great Britain, and the United States, and it now has 42 member countries across five continents and collects data
in 70 countries. NORC represents the United States as a country in the ISSP.
ISSP modules have several defining criteria:
•
•
Modules are developed in English and translated to every administered language.
Modules must contain approximately 60 questions and can be supplemented by optional items, as well as
around 40 items on background and demographic characteristics.
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•
•
Modules must contain the same items across all languages and studies, asked in the same order, with minor
variations to account for mode differences or regional differences. 1
Each topical module must be administered within a relatively narrow time frame, usually about 18 months
from the start of the relevant year.
ISSP Modules are currently replicated every 10 years, allowing for topics to be studied as both a multinational
snapshot at a single point in time as well as a slowly evolving repeated cross-section of national opinions. While
not every topic is repeated, the longest-running module is up to its fifth replication. ISSP rules require that when a
topic is repeated, at least two-thirds of the questions must be repeated from a previous round, while up to onethird can be new questions.
Since 1985, the ISSP topics have included:
Role of Government
1985, 1990, 1996, 2006, 2016
Social Networks
1986, 2001, 2017
Social Inequality
1987, 1992, 1999, 2009, 2019
Family & Changing Gender Roles
1988, 1994, 2002, 2012, 2022
Work Orientation
1989, 1997, 2005, 2015
Religion
1991, 1998, 2008, 2018
Environment
1993, 2000, 2010, 2020
National Identity
1995, 2003, 2013
Citizenship
2004, 2014
Leisure Time and Sports
1
For example, asking about Congress or Parliament, or asking about the European Union or NAFTA.
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2007 2
Health and Health Care
2011 3, 2022
Study Overview
This codebook focuses on the 2022 GSS Cross-section survey. While this iteration of the Cross-section has
meaningful changes from previous editions, there is much that remains consistent. Just as was done since 2004,
the GSS Cross-section survey administers a full-probability sample approach with samples created from an
adapted form of the United States Postal Service (USPS) metropolitan statistical area (MSA)/county frame area.
More on the GSS conventional design can be found on pages 3177–3178 of the legacy cumulative codebook,
available at the GSS website.4
The GSS has been conducted since 1972 and currently functions as a social indicators program, which highly
values historical trends and continuity. To that end, the GSS’s replicating core contains items that have been asked
since its inception. In some cases, these items were asked on even older surveys, allowing for continuous
measurement of concepts since the 1940s.
GSS variables appear at three different frequencies. Items in the Replicating Core, Household Composition, and
Contact/Validation typically appear every year and are rarely altered (although, in 2022, they feature modification
for the web mode). Items in some topical modules, such as ISSP modules, can appear in multiple years, but not
every year. Finally, items on the rest of the topical modules typically only appear in a single year. In the 2022 GSS,
the ISSP Family and Gender Roles, ISSP Health and Health Care, Shared Capitalism, NIOSH Quality of Working Life,
National Endowment for the Arts and High-Risk Behaviors modules are repeats from previous years. It is important
to note that important updates to questions were made to National Endowment for the Arts (NEA), Shared
Capitalism, and NIOSH QWL. The module named GSS Next is a board-initiated module that has a combination of
old and new GSS variables. The NEA and GSS Next modules were conducted as web-only follow-on interviews.
For more information on these, please see Follow-on Studies.
The 2022 GSS Cross-section includes the following Topical Modules (some of which include updates and
modifications to previously fielded modules):
•
•
•
ISSP Family and Changing Gender Roles: This module has previously been asked four times, beginning in 1988.
It focuses on attitudes and opinions on family life and changing gender roles. Like all ISSP modules, two-thirds
of the items are repeats from previous rounds, while one-third are new items.
ISSP Health and Health Care: This module has previously been asked once in 1988. It focuses on attitudes and
opinions on mental health, physical health, and health care. Like all ISSP modules, two-thirds of the items are
repeats from previous rounds, while one-third are new items.
Shared Capitalism: This module has previously been asked two times, beginning in 2014, and included revisions
in 2022. It focuses on individual’s employer ownership (such as owning stock in the company where they work)
and attitudes on work-based incentives. This module was proposed by Joseph Blasi at Rutgers University.
ISSP items for these years were asked not in the Cross-section of the GSS but on a panel follow-up interview.
Ibidem.
4
https://gss.norc.org/Documents/codebook/gss_codebook.zip
2
3
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•
•
•
•
NIOSH Quality of Working Life: This module has previously been asked five times, beginning in 2002. It focuses
on individual’s work experiences and attitudes on work behaviors. The 2022 module includes revisions and
additions to previous modules. This module was propo
