Mize19GSS: General Social Survey Data analzed in Mize (2019)
In catregs: Post-Estimation Functions for Generalized Linear Mixed Models
Mize19GSS R Documentation
General Social Survey Data analzed in Mize (2019)
Description
Mize (2019) illustrates how to establish nonlinear moderation in the context of regression models. He illustrates using General Social Survey (GSS) data and provides Stata code to replicate the results. Catregs functions can replicate these results in R.
Usage
data("Mize19GSS")
Format
A data frame with 19337 observations on the following 42 variables.
nosameBa numeric vector
sameokBa numeric vector
polviewsa character vector
agea numeric vector
age10a numeric vector
yeara numeric vector
ida numeric vector
degreea numeric vector
racea numeric vector
partyida character vector
natspaca character vector
natenvira character vector
natheala character vector
natcitya character vector
natcrimea character vector
natdruga character vector
nateduca character vector
natracea character vector
natarmsa character vector
nataida character vector
natfarea character vector
healtha character vector
helpnotBa character vector
conserva character vector
polviews3a character vector
employeda numeric vector
malea numeric vector
womana numeric vector
whitea numeric vector
collegea numeric vector
marrieda numeric vector
parenta character vector
edyrsa numeric vector
incomea numeric vector
hrsworka character vector
parttimea character vector
wagesa numeric vector
conviewSSa numeric vector
year2a numeric vector
yearcata numeric vector
year1976a numeric vector
year1976.2a numeric vector
Source
Mize, Trenton D. 2019. "Best Practices for Estimating, Interpreting, and
Presenting Nonlinear Interaction Effects" Sociological Science 6: 81-117.
Examples
data(Mize19GSS)
head(Mize19GSS)
catregs documentation built on Sept. 11, 2024, 6:18 p.m.
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| Mize19GSS | R Documentation |
General Social Survey Data analzed in Mize (2019)
Description
Mize (2019) illustrates how to establish nonlinear moderation in the context of regression models. He illustrates using General Social Survey (GSS) data and provides Stata code to replicate the results. Catregs functions can replicate these results in R.
Usage
data("Mize19GSS")Format
A data frame with 19337 observations on the following 42 variables.
nosameBa numeric vector
sameokBa numeric vector
polviewsa character vector
agea numeric vector
age10a numeric vector
yeara numeric vector
ida numeric vector
degreea numeric vector
racea numeric vector
partyida character vector
natspaca character vector
natenvira character vector
natheala character vector
natcitya character vector
natcrimea character vector
natdruga character vector
nateduca character vector
natracea character vector
natarmsa character vector
nataida character vector
natfarea character vector
healtha character vector
helpnotBa character vector
conserva character vector
polviews3a character vector
employeda numeric vector
malea numeric vector
womana numeric vector
whitea numeric vector
collegea numeric vector
marrieda numeric vector
parenta character vector
edyrsa numeric vector
incomea numeric vector
hrsworka character vector
parttimea character vector
wagesa numeric vector
conviewSSa numeric vector
year2a numeric vector
yearcata numeric vector
year1976a numeric vector
year1976.2a numeric vector
Source
Mize, Trenton D. 2019. "Best Practices for Estimating, Interpreting, and Presenting Nonlinear Interaction Effects" Sociological Science 6: 81-117.
Examples
data(Mize19GSS)
head(Mize19GSS)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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