AbortionAmbivalence

0th

Percentile

American Ambivalence towards Abortion Policy

Data about attitudes towards abortion policy in the US. Cross-section data from the US General Social Survey 1982 with oversample of African American respondents.

Keywords
datasets
Usage
data("AbortionAmbivalence")
Details

The data were prepared and analyzed by Alvarez and Brehm (1995). A detailed discussion of the variables is provided in their Appendix A and the model is developed in their Section 3.

The data were reanalyzed by Altman and McDonald (2003) with focus on numerical accuracy and by Keele and Park (2006) with focus on interpretability.

Format

A data frame containing 1860 observations on 20 variables.

health
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if the woman's own health is seriously endangered by the pregnancy?
rape
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if she became pregnant as a result of rape?
defect
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if there is a strong chance of serious defect in the baby?
poor
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if the family has a very low income and cannot afford any more children?
nomore
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if she is married and does not want any more children?
single
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if she is not married and does not want to marry the man?
any
factor. Answer to the question: Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if the woman wants it for reason?
ethnicity
factor indicating ethnicity. Is the individual African-American ("afam") or not ("other")?
gender
factor indicating gender.
religion
factor indicating religious preference ("catholic" or "other").
religiousness
Religious intensity as coded by Alvarez and Brehm (1995).
religiousness2
Religious intensity in an alternative coding suggested by Altman and McDonald (1995).
church
Numeric coding of frequency of attending church.
erameans
factor. Answer to the question: Do you understand what the Equal Rights Amendment (ERA) means?
erasupport
Intensity of support for ERA.
pros
Number of arguments in favor of abortion named by the subject.
cons
Number of arguments against abortion named by the subject.
importance
Numeric coding of subjective importance of abortion issue.
information
Numeric coding of self-assessment of information on abortion issue available to the subject.
firmness
Numeric coding of subjective firmness of opinion on abortion.

Source

Online supplements to Altman and McDonald (2003). http://dx.doi.org/10.1093/pan/mpg016

References

Altman M, McDonald MP (2003). “Replication with Attention to Numerical Accuracy.” Political Analysis, 11, 302--307.

Alvarez RM, Brehm J (1995). “American Ambivalence towards Abortion Policy: Development of a Heteroskedastic Probit Model of Competing Values.” American Journal of Political Science, 39(4), 1055--1082.

Keele LJ, Park DK (2006). Ambivalent about Ambivalence: A Re-Examination of Heteroskedastic Probit Models. Unpublished manuscript.

See Also

hetglm

Aliases
  • AbortionAmbivalence
Examples
data("AbortionAmbivalence")

## first model for mother's health
ab_health <- hetglm(
  health ~ ethnicity + gender + religion + religiousness + church + erameans + erasupport |
  pros * cons + importance + information + firmness, data = AbortionAmbivalence)
summary(ab_health)

## corresponding model with analytical gradients but numerical Hessian
ab_health2 <- update(ab_health, method = "BFGS", hessian = TRUE)
summary(ab_health2)

## Alvarez and Brehm (1995), Table 1, p. 1069
## (see also Altman and McDonald, 2003, Supplement, Tables 4-10)
tab1 <- sapply(names(AbortionAmbivalence)[1:7], function(x) {
  f <- as.formula(paste(x,
    "~ ethnicity + gender + religion + religiousness + church + erameans + erasupport",
    "| pros * cons + importance + information + firmness"))
  f0 <- as.formula(paste(x, "~ 1"))
  m <- hetglm(f, data = AbortionAmbivalence)
  m0 <- hetglm(f0, data = model.frame(m))
  c(Percent_yes = as.vector(100 * prop.table(table(AbortionAmbivalence[[x]]))["yes"]),
    coef(m)[c(1:10, 14, 11:13)],
    Heteroskedasticity = as.vector(summary(m)$lrtest[1]),
    N = nobs(m),
    Goodness_of_fit = 2 * as.vector(logLik(m) - logLik(m0))
  )
})
round(tab1, digits = 2)


if(require("AER")) {
## compare Wald tests with different types of standard errors
coeftest(ab_health)
coeftest(ab_health2)
coeftest(ab_health,  vcov = sandwich)
coeftest(ab_health2, vcov = sandwich)
coeftest(ab_health,  vcov = vcovOPG)
coeftest(ab_health2, vcov = vcovOPG)

ab_health_tstat <- cbind(
  "A-Info"     = coeftest(ab_health)[,3],
  "N-Info"     = coeftest(ab_health2)[,3],
  "A-Sandwich" = coeftest(ab_health,  vcov = sandwich)[,3],
  "N-Sandwich" = coeftest(ab_health2, vcov = sandwich)[,3],
  "A-OPG"      = coeftest(ab_health,  vcov = vcovOPG)[,3],
  "N-OPG"      = coeftest(ab_health2, vcov = vcovOPG)[,3]
)
round(ab_health_tstat, digits = 3)
}
Documentation reproduced from package glmx, version 0.1-1, License: GPL-2 | GPL-3

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