Please Choose Your Project Variables From The List ✓ Solved

Please choose your project variables from the following list

Please choose your project variables from the following list of GSS 2016 variables: For dependent variable (topic of interest) - Education: educ (highest year of school completed), degree (r’s highest degree); Employment/Labor/Class: wrkstat (Labor force status), hrs1 (hours worked last week), class (Subjective class identification), unemp (Ever unemployed in last ten yrs), parsol (Rs living standard compared to parents), stress (How often does r find work stressful), jobsat (How satisfied is r with his/her job); Family: marital (marital status), divorce (ever been divorced or separated), sibs (number of brothers and sisters), childs (number of children), agekdbrn (R’s age when 1st child born), hapmar (Happiness of marriage), chldidel (Ideal number of children), sexfreq (Frequency of sex during last year), sexornt (Sexual orientation), spanking (Favor spanking to discipline child), fechld (Mother working doesn't hurt children), fepresch (Preschool kids suffer if mother works), fefam (Better for man to work, woman tend home), marhomo (Homosexuals should have right to marry), divlaw (Divorce law); Health and mental health: happy (General happiness), health (Condition of health), mntlhlth (Days of poor mental health past 30 days), hivtest (Have you ever been tested for hiv), cesd1 (How much time felt depressed in past wk); Income: realrinc (Rs income in constant $), income16 (Total family income), rincom16 (Respondents income); Military: vetyears (Years in armed forces); Politics: partyid (Political party affiliation), cappun (Favor or oppose death penalty for murder), gunlaw (Favor or oppose gun permits), grass (should marijuana be made legal), tapphone (Should authorities have right to tap phone conversation); Religion: relig (Rs religious preference), fund (How fundamentalist is r currently), attend (How often r attends religious services), reliten (Strength of affiliation), postlife (Belief in life after death), pray (How often does r pray), popespks (Pope is infallible on matters of faith or morals), relig16 (Religion in which raised), denom16 (Denomination in which r was raised), fund16 (How fundamentalist was r at age 16), sprel (Spouses religious preference), spden (Specific denomination, spouse), spfund (How fundamentalist is spouse currently), prayer (Bible prayer in public schools); Social relations: socrel (Spend evening with relatives), socommun (Spend evening with neighbor), socfrend (Spend evening with friends), socbar (Spend evening at bar); Technology: tvhours (Hours per day watching tv), wwwhr (Www hours per week), newsfrom (Main source of information about events in the news), twitter (Use twitter), facebook (Use facebook); Opinions about government spending: natspac, natenvir, natheal, natcrime, natdrug, nateduc, natarms, natfare, natsoc, natenrgy, helppoor, helpsick; Opinions on types of speech/teaching: spkrac, colrac, colhomo, spkmslm, colmslm, revspeak; Opinions about race: wrkwayup; Opinions about abortion: abdefect, abnomore, abhlth, abpoor, abrape, absingle, abany; Opinions about sex: pillok, sexeduc, premarsx, teensex, xmarsex, homosex, pornlaw; Opinions about euthanasia/suicide: letdie1, suicide1, suicide2, suicide3, suicide4. For independent variable (variable that influences the DV) - sex, race, eth1, born, res16, family16, mawrkgrw, incom16, relig16, hhtype1, educ, degree, paeduc, maeduc, wrkstat, hrs1, class, unemp, parsol, stress, jobsat, marital, divorce, sibs, childs, agekdbrn, hapmar, sexfreq, sexornt, happy, health, mntlhlth, hivtest, cesd1, realrinc, income16, rincom16, vetyears, partyid, relig, fund, attend, reliten, postlife, pray, sprel, spden, spfund, prayer, socrel, socommun, socfrend, socbar, tvhours, wwwhr, newsfrom, twitter, facebook. For more information about the variables: go to the GSS Data Explorer website, click 'Search Variables', enter a keyword or variable name, and filter to year 2016.

Paper For Above Instructions

Project selection and research question

This project will use the 2016 General Social Survey (GSS) to model educational attainment (years of schooling) as the dependent variable (educ). The principal independent variables will be race and sex, with a set of socioeconomic and family-background controls including parental education (paeduc, maeduc), family income when respondent was 16 (incom16), and age (to adjust cohort effects). The core research question is: To what extent do race and sex predict years of schooling (educ) in the 2016 GSS sample, after adjusting for family background and early-life socioeconomic resources?

Rationale and hypotheses

Education is a central outcome linked to lifetime earnings, health, and social mobility (Goldin & Katz, 2008; Autor, 2014). Prior research finds persistent racial and gender gaps in attainment that are partly explained by family background but also by structural barriers and labor-market expectations (Alon & Tienda, 2005). Hypotheses:

• H1: Race will be significantly associated with years of education: non-Hispanic white respondents will report higher educ than Black and other racial minority respondents, controlling for family background (Alon & Tienda, 2005).
• H2: Sex differences in educ will be small or favor women in the 2016 cohort, once background covariates are included (Goldin & Katz, 2008).
• H3: Parental education and family income at age 16 will mediate part of the race and sex associations with educ (socioeconomic mediation; Long & Freese, 2006).

Data and variable operationalization

Data source: GSS 2016 (NORC at the Univ. of Chicago). Dependent variable: educ (continuous, years of education). Primary independent variables: race (categorical: White, Black, Other), sex (binary: male/female). Key controls: age (continuous), paeduc and maeduc (parents' highest years of schooling, continuous), incom16 (family income at age 16, ordinal or continuous if available), born (nativity), and hhtype1 (household type). Missing values will be handled using multiple imputation when appropriate (Little & Rubin, 2019).

Analytic strategy

Because educ is measured in years, OLS regression is appropriate as a primary model (Gelman & Hill, 2007). Model sequence:

1. Model 1: educ ~ race + sex + age (baseline demographics).
2. Model 2: Add parental education and family income at 16 (mediation test).
3. Model 3: Add additional controls (nativity, household type, cohort interactions) and test race*sex interactions to examine intersectional patterns.

Diagnostics: check linearity, residuals, multicollinearity (VIF), and heteroskedasticity; use robust standard errors if needed. Weighted analyses will be run if survey weights are required for population inference (NORC, 2016). Sensitivity analyses: repeat with degree (categorical) using ordered logistic regression to assess consistency (Long & Freese, 2006).

Expected results and interpretation

Based on prior literature, I expect race differences favoring White respondents in years of education, with partial attenuation after adding parental education and early-life income (Alon & Tienda, 2005). Sex differences are likely small or indicate higher average educ for women in recent cohorts (Goldin & Katz, 2008). Effect sizes will be reported as coefficients (years difference) and marginal effects for categorical predictors to facilitate interpretation (Gelman & Hill, 2007).

Limitations and ethical considerations

Limitations include cross-sectional data (no causal claims about change over time), potential measurement error in retrospective items (e.g., incom16), and unobserved confounding (e.g., school quality). Survey nonresponse and item-missingness require careful treatment (Little & Rubin, 2019). Ethical considerations involve respectful reporting of group differences, avoiding stigmatizing language, and acknowledging structural causes rather than individual blame (APA ethical reporting guidance).

Contribution and relevance

This analysis provides a focused, replicable test of how race and sex associate with educational attainment in a nationally representative 2016 US sample, with clear operational definitions, an explicit model plan, and sensitivity checks. Findings can inform research on educational inequality and guide policy discussions about interventions to reduce disparities.

Analysis plan checklist

• Acquire GSS 2016 data and codebook; confirm variable coding and permissible values (NORC, 2016).
• Perform descriptive statistics and visualizations by race and sex.
• Estimate regression models with sequential covariate inclusion.
• Conduct diagnostics, multiple imputation for missing data, and weighted analyses if appropriate.
• Document code (R or Stata) and produce reproducible output and interpretation.

Conclusion

By selecting educ as the dependent variable and race and sex as principal independent variables (with parental education and early-life income as controls), this project targets key axes of social inequality using well-documented GSS measures. The analytical plan (OLS, diagnostics, sensitivity checks) follows best practices for survey research and will generate interpretable estimates of how demographic and background factors shape educational attainment in the 2016 US sample (Gelman & Hill, 2007; Long & Freese, 2006).

References

• NORC at the University of Chicago. (2016). General Social Survey, 2016. GSS Data Explorer. https://gssdataexplorer.norc.org/ (GSS variables, codebook).
• Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• Long, J. S., & Freese, J. (2006). Regression Models for Categorical Dependent Variables Using Stata. Stata Press.
• Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). Wiley.
• Goldin, C., & Katz, L. F. (2008). The Race between Education and Technology. Harvard University Press.
• Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among the “other 99 percent”. Science, 344(6186), 843–851.
• Alon, S., & Tienda, M. (2005). Assessing the “college for all” hypothesis: stratification and persistence in higher education. American Sociological Review, 70(4), 613–633.
• OECD. (2016). Education at a Glance 2016: OECD Indicators. OECD Publishing.
• National Center for Education Statistics. (2019). The Condition of Education. U.S. Department of Education.
• APA Publication Manual & Reporting Standards. (2020). American Psychological Association. (Guidance for ethical, non-stigmatizing reporting of group differences).