🎓 Tidy tools for academics
Install the development version from Github with:
## install devtools if not already
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
## install tidysem from Github
devtools::install_github("mkearney/tidysem")Load the package (it, of course, plays nicely with tidyverse).
## load tidyverse
library(tidyverse)
## load tidysem
library(tidysem)Conduct latent variable analysis using structural equation modeling.
## mutate data and then specify and estimate model
sem1 <- polcom %>%
mutate(therm_2 = therm_2 / 10,
therm_1 = 10 - therm_1 / 10) %>%
tidy_sem_model(news =~ news_1 + news_2 + news_3 + news_4 + news_5 + news_6,
ambiv_sexism =~ ambiv_sexism_1 + ambiv_sexism_2 + ambiv_sexism_3 +
ambiv_sexism_4 + ambiv_sexism_5 + ambiv_sexism_6,
partisan =~ a*therm_1 + a*therm_2,
ambiv_sexism ~ age + sex + hhinc + edu + news + partisan) %>%
tidy_sem()
## print model summary
sem1 %>%
tidy_summary()
#> # A tidy model
#> Model formula : news =~ news_1 + news_2 + news_3 + news_4 + news_5 + news_6
#> ambiv_sexism =~ ambiv_sexism_1 + ambiv_sexism_2 + ambiv_sexism_3 + ambiv_sexism_4 +
#> ambiv_sexism_5 + ambiv_sexism_6
#> partisan =~ a * therm_1 + a * therm_2
#> ambiv_sexism ~ age + sex + hhinc + edu + news + partisan
#> Model type : Structural Equation Model (SEM)
#> Model pkg::fun : lavaan::sem()
#> Model data : 235 (observations) X 18 (variables)
#> $fit
#> fit_stat n df estimate p.value stars
#> chisq 235 127 239.579 <.001 ***
#> aic 235 - 0.907 -
#> bic 235 - 0.892 -
#> cfi 235 - 16138.684 -
#> tli 235 - 16256.310 -
#> rmsea 235 - 0.061 -
#> srmr 235 - 0.073 -
#> R^2:ambiv_sexism 235 - 0.379 -
#>
#> $coef
#> term est se est.se p.value stars std.est
#> news =~ news_1 1.000 <.001 - - 0.173
#> news =~ news_2 1.592 0.722 2.204 0.028 * 0.340
#> news =~ news_3 5.069 2.095 2.419 0.016 * 0.781
#> news =~ news_4 5.587 2.312 2.417 0.016 * 0.851
#> news =~ news_5 3.493 1.485 2.353 0.019 * 0.520
#> news =~ news_6 1.255 0.683 1.838 0.066 + 0.196
#> ambiv_sexism =~ ambiv_sexism_1 1.000 <.001 - - 0.825
#> ambiv_sexism =~ ambiv_sexism_2 0.942 0.067 14.043 <.001 *** 0.801
#> ambiv_sexism =~ ambiv_sexism_3 0.795 0.067 11.844 <.001 *** 0.706
#> ambiv_sexism =~ ambiv_sexism_4 0.743 0.064 11.647 <.001 *** 0.697
#> ambiv_sexism =~ ambiv_sexism_5 0.902 0.062 14.644 <.001 *** 0.825
#> ambiv_sexism =~ ambiv_sexism_6 0.904 0.064 14.185 <.001 *** 0.807
#> partisan =~ therm_1 1.000 <.001 - - 0.577
#> partisan =~ therm_2 1.000 <.001 - - 0.592
#> ambiv_sexism ~ age -0.004 0.005 -0.824 0.410 -0.051
#> ambiv_sexism ~ sex -0.271 0.130 -2.089 0.037 * -0.130
#> ambiv_sexism ~ hhinc -0.021 0.023 -0.878 0.380 -0.057
#> ambiv_sexism ~ edu -0.088 0.069 -1.279 0.201 -0.083
#> ambiv_sexism ~ news 0.130 0.215 0.607 0.544 0.047
#> ambiv_sexism ~ partisan 0.347 0.069 5.032 <.001 *** 0.592