Affairs {AER} | R Documentation

## Fair's Extramarital Affairs Data

### Description

Infidelity data, known as Fair's Affairs. Cross-section data from a survey conducted by Psychology Today in 1969.

### Usage

    data("Affairs")

### Format

A data frame containing 601 observations on 9 variables.

affairs

  numeric. How often engaged in extramarital sexual intercourse during the past year?

gender

  factor indicating gender.

age

  numeric variable coding age in years: `17.5` = under 20, `22` = 20–24, `27` = 25–29, `32` = 30–34, `37` = 35–39, `42` = 40–44, `47` = 45–49, `52` = 50–54, `57` = 55 or over.

yearsmarried

  numeric variable coding number of years married: `0.125` = 3 months or less, `0.417` = 4–6 months, `0.75` = 6 months–1 year, `1.5` = 1–2 years, `4` = 3–5 years, `7` = 6–8 years, `10` = 9–11 years, `15` = 12 or more years.

children

  factor. Are there children in the marriage?

religiousness

  numeric variable coding religiousness: `1` = anti, `2` = not at all, `3` = slightly, `4` = somewhat, `5` = very.

education

  numeric variable coding level of education: `9` = grade school, `12` = high school graduate, `14` = some college, `16` = college graduate, `17` = some graduate work, `18` = master's degree, `20` = Ph.D., M.D., or other advanced degree.

occupation

  numeric variable coding occupation according to Hollingshead classification (reverse numbering).

rating

  numeric variable coding self rating of marriage: `1` = very unhappy, `2` = somewhat unhappy, `3` = average, `4` = happier than average, `5` = very happy.

### Source

Online complements to Greene (2003). Table F22.2.

http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

### References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

Fair, R.C. (1978). A Theory of Extramarital Affairs. Journal of Political Economy, 86, 45–61.

### See Also

`Greene2003`

### Examples

    data("Affairs")

    ## Greene (2003)
    ## Tab. 22.3 and 22.4
    fm_ols <- lm(affairs ~ age + yearsmarried + religiousness + occupation + rating,
      data = Affairs)
    fm_probit <- glm(I(affairs > 0) ~ age + yearsmarried + religiousness + occupation + rating,
      data = Affairs, family = binomial(link = "probit"))

    fm_tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating,
      data = Affairs)
    fm_tobit2 <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating,
      right = 4, data = Affairs)

    fm_pois <- glm(affairs ~ age + yearsmarried + religiousness + occupation + rating,
      data = Affairs, family = poisson)

    library("MASS")
    fm_nb <- glm.nb(affairs ~ age + yearsmarried + religiousness + occupation + rating,
      data = Affairs)

    ## Tab. 22.6
    library("pscl")
    fm_zip <- zeroinfl(affairs ~ age + yearsmarried + religiousness + occupation + rating | age +
      yearsmarried + religiousness + occupation + rating, data = Affairs)

[Package AER version 1.2-5 Index]