#' Industrialization-Democracy Data
#'
#' A dataset from Bollen (1989) containing measures of political
#' democracy and industrialization for 75 developing countries
#' in 1960 and 1965. The variables are as follows:
#'
#' \itemize{
#' \item y1. freedom of the press, 1960
#' \item y2. freedom of political opposition, 1960
#' \item y3. fairness of elections, 1960
#' \item y4. effectiveness of elected legislature, 1960
#' \item y5. freedom of the press, 1965
#' \item y6. freedom of political opposition, 1965
#' \item y7. fairness of elections, 1965
#' \item y8. effectiveness of elected legislature, 1965
#' \item x1. natural log of GNP per capita, 1960
#' \item x2. natural log of energy consumption per capita, 1960
#' \item x3. arcsin of square root of percentage of labor force in industry, 1960
#' }
#'
#' @docType data
#' @keywords datasets
#' @name bollen1989a
#' @usage bollen1989a
#' @format A data frame with 75 rows and 9 variables
#'
#' @examples
#'
#' \dontrun{
#' model <- '
#' Eta1 =~ y1 + y2 + y3 + y4
#' Eta2 =~ y5 + y6 + y7 + y8
#' Xi1 =~ x1 + x2 + x3
#' Eta1 ~ Xi1
#' Eta2 ~ Xi1
#' Eta2 ~ Eta1
#' y1 ~~ y5
#' y2 ~~ y4
#' y2 ~~ y6
#' y3 ~~ y7
#' y4 ~~ y8
#' y6 ~~ y8 devtools::build_win()
#' '
#' }
#'
#' @references
#' Bollen, K. A. (1989). Structural equation models.
#' New York: Wiley-Interscience.
NULL
#' Union sentiment data
#'
#' A dataset from McDonald and Clelland (1984) reanalyzed by
#' Bollen (1989) containing data on union sentiment of
#' southern nonunion textile workers.
#'
#' \itemize{
#' \item deferenc. deference (submissiveness) to managers
#' \item laboract. support for labor activism
#' \item unionsen. sentiment towards unions
#' \item yrsmill. log of years spent in textile mill
#' \item age. centered age
#' }
#'
#' @docType data
#' @keywords datasets
#' @name bollen1989b
#' @usage bollen1989b
#' @format A data frame with 173 rows and 5 variables
#'
#' @examples
#'
#' \dontrun{
#' model <- '
#' unionsen ~ deferenc + laboract + yrsmill
#' deferenc ~ age
#' laboract ~ age + deferenc
#' yrsmill ~~ age
#' '
#' }
#'
#' @references
#' Bollen, K. A. 1989. Structural Equations with Latent Variables.
#' New York: Wiley
#'
#' McDonald, A, J., & Clelland, D. A. (1984). Textile Workers
#' and Union Sentiment. Social Forces, 63(2), 502–521.
NULL
#' Attractiveness and academic ability
#'
#' This data comes from a study by Felson and Borhnstedt (1979)
#' of perceived attractiveness and academic ability in teenagers,
#' sixth through ninth grade. The six variables are perception of
#' academic ability (academic), perception of physical
#' attractiveness (attract), grade point average (gpa),
#' height, weight, and a strangers' rating of attractiveness
#' (rating).
#'
#' \itemize{
#' \item acad.
#' \item athl.
#' \item attract.
#' \item gpa.
#' \item height.
#' \item weight.
#' \item rating.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name felson1979
#' @usage felson1979
#' @format A data frame with 209 rows and 7 variables
#'
#' @examples
#'
#' \dontrun{
#' model <- '
#' acad ~ gpa + attract
#' attract ~ height + weight + rating + acad
#' '
#' }
#'
#'
#' @references
#' Felson, R.B. & Bohrnstedt, G.W. (1979). "Are the good
#' beautiful or the beautiful good?" The relationship between
#' children's perceptions of ability and perceptions of
#' physical attractiveness. Social Psychology Quarterly,
#' 42, 386–392.
NULL
#' Subjective class data
#'
#' The following data is from Bollen (1989) using data from
#' Kluegel et al. (1977). These data include measures of actual
#' income (inc) and occupational prestige (occ), measures
#' of respondents' subjective assessments of income (subinc),
#' occupational prestige (subocc), and overall SES status (subgen).
#'
#' \itemize{
#' \item occ. actual occupational prestige
#' \item inc. actual income
#' \item subocc. respondents' subjective assessments of prestige
#' \item subinc. respondents' subjective assessments of income
#' \item subgen. respondents' subjective assessments of SES status
#' }
#'
#' @docType data
#' @keywords datasets
#' @name bollen1989c
#' @usage bollen1989c
#' @format A data frame with 432 rows and 5 variables
#'
#' @examples
#'
#'\dontrun{
#' model <- '
#' subinc ~ inc + subocc
#' subocc ~ occ + subinc
#' subgen ~ subinc + subocc
#' subinc ~~ subocc + subgen
#' subocc ~~ subgen
#' inc ~~ occ
#' '
#'}
#'
#' @references
#' Bollen, K. A. 1989. Structural Equations with Latent Variables.
#' New York: Wiley
#'
#' Kluegel, J. R., Singleton, R., & Starnes, C. E. (1977).
#' Subjective Class Identification: A Multiple Indicator
#' Approach. American Sociological Review, 42(4), 599–611.
NULL
#' Perceived accessibility data
#'
#' Data come from a survey that was conducted in rural clusters of
#' Tanzania in 1993. The goal was to collect information on the perceived
#' accessibility of a specific family planning facility that serviced each
#' cluster. Six informants were chosen: 3 female and 3 male. New informants
#' were chosen for each cluster. Each informant was independently asked to
#' rate the accessibility of the facility, and how easy it was to get to the
#' facility. More specifically the women informants were asked to rate how
#' women of childbearing age perceived the accessibility and easiness and
#' men were asked to rate how accessible and easy men perceived access to
#' the clinic to be. Higher values indicate greater accessibility and ease
#' of travel. The female informants' ratings are 1 to 3 and the male
#' informants' ratings are 4 to 6.
#'
#' \itemize{
#' \item access1.
#' \item access2.
#' \item access3.
#' \item access4.
#' \item access5.
#' \item access6.
#' \item easy1.
#' \item easy2.
#' \item easy3.
#' \item easy4.
#' \item easy5.
#' \item easy6.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name bollen1996
#' @usage bollen1996
#' @format A data frame with 220 rows and 12 variables
#'
#' @examples
#'
#'\dontrun{
#' model <- '
#' femaleAccess =~ access1 + access2 + access3
#' maleAccess =~ access4 + access5 + access6
#' femaleEasy =~ easy1 + easy2 + easy3
#' maleEasy =~ easy4 + easy5 + easy6
#' '
#'}
#'
#' @references
#' Bollen, K. A., Speizer, I. S., & Mroz, T. A. (1996). Family Planning
#' Facilities in Rural Tanzania: His and Her Perceptions of Time and Distance.
NULL
#' Reisenzein data
#'
#' This dataset comes from Reisenzein (1986). In this paper Reisenzein designed
#' a randomized experiment to test Weiner's attribution-affect model of
#' helping behavior. According to this theory, whether people help others is
#' determined by their anger or sympathy. Anger and sympathy are affected by
#' perceived controllability. If the individuals have gotten into difficult
#' situations as a result of their own controllable actions, then this
#' negatively affects sympathy and positively affects anger of the potential
#' helpers. The opposite holds if the situation seems beyond the individuals’
#' control. This data comes from an experiment that describes a person
#' collapsing and lying on the floor of a subway. Subjects were told that the
#' person was either drunk (controllable situation) or ill
#' (uncontrollable situation). This randomized story was intended to affect
#' perceptions of controllability, and controllability in turn affected
#' feelings of sympathy and anger. Finally, sympathy should positively affect
#' helping behavior while anger would negatively affect helping.
#'
#' \itemize{
#' \item Z1. Eliciting Situation
#' \item Z2. How controllable, do you think, is the cause of the person's
#' present condition? (1 = not at all under personal control, 9 = completely
#' under personal control).
#' \item Z3. How responsible, do you think, is that person for his present
#' condition? (1 = not at all responsible, 9 = very much responsible).
#' \item Z4. I would think that it was the person's own fault that he is in
#' the present situation. (1 = no. not at all. 9 = yes, absolutely so).
#' \item Z5. How much sympathy would you feel for that person? (1 = none at
#' all. 9 = very much).
#' \item Z6. I would feel pity for this person. (1 = none at all, 9 = very
#' much).
#' \item Z7. How much concern would you feel for this person? (1 = none al
#' all, 9 = very much).
#' \item Z8. How angry would you feel at that person? (1 = not at all, 9 =
#' very much).
#' \item Z9. How irritated would you feel by that person? (1 = not at all, 9
#' = very much).
#' \item Z10. I would feel aggravated by that person. (1 = not at all, 9 =
#' very much so).
#' \item Z11. How likely is it that you would help that person? (1 =
#' definitely would not help. 9 = definitely would help).
#' \item Z12. How certain would you feel that you would help the person?
#' (1 = not at all certain. 9 = absolutely certain).
#' \item Z13. Which of the following actions would you most likely engage in?
#' 1 = not help at all; 2 = try to alert other bystanders, but stay
#' uninvolved myself; 3 = try to inform the conductor or another official in
#' charge; 4 = go over and help the person to a seat; 5 = help in any way
#' that might be necessary, including if necessary first aid and/or
#' accompanying the person to a hospital.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name reisenzein1986
#' @usage reisenzein1986
#' @format A data frame with 138 rows and 13 variables
#'
#'
#' @references
#' Reisenzein, R. (1986). A Structural Equation Analysis of Weiner's
#' Attribution-Affect Model of Helping Behavior. Journal of Personality
#' and Social Psychology, 50(6), 1123–33.
NULL
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