American Ambivalence towards Abortion Policy

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Description

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.

Usage

1
data("AbortionAmbivalence")

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.

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.

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. http://www.polisci.ohio-state.edu/faculty/lkeele/hetprob.pdf

See Also

hetglm

Examples

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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)
}