Haven enables R to read and write various data formats used by other statistical packages by wrapping the fantastic ReadStat C library written by Evan Miller. Haven is part of the tidyverse.
#read spss files .sav
#install.packages("haven", lib="~/lib/r-cran")
#install.packages("haven", lib="C:/Program Files/R/lib")
library(haven)
#project string
pstr = "csv/dataset-iss-2016-subset1"
pstr = "../../csv/dataset-iss-2016-subset1"
#read sav
dfs = read_sav(paste0(pstr, ".sav"))
#get labels
dfl = data.frame(label=sapply(dfs, function(x) attributes(x)$label))
dfl$vars = rownames(dfl)
#export csv
write.table(dfs, paste0(pstr, ".csv"), sep="\t", row.names=F)
#read csv
dfs = read.table(paste0(pstr, ".csv"), sep='\t', header=T, strip.white=TRUE, stringsAsFactors=FALSE)The lavaan (latent variable analysis) package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.
#install.packages("lavaan", dependencies=TRUE, lib="~/lib/r-cran")
#install.packages("lavaan", dependencies=TRUE, lib="C:/Program Files/R/lib")
library(lavaan)## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.#specify formulas for latent variables
animosity =~ ANI1 + ANI2 + ANI3 + ANI4
ethnocentrism =~ ETHNO1 + ETHNO2 + ETHNO3
#specify covariance, measurement model
iss.cfa.model <- '
#animosity =~ ANI1 + ANI2 + ANI3 + ANI4
animosity =~ ANI1 + ANI2 + ANI3 + ANI4 + ANI5
ethnocentrism =~ ETHNO1 + ETHNO2 + ETHNO3
'
#fit model
fit <- cfa(iss.cfa.model, data=dfs)
#check standardized factor loadings (check significance values <0.05)
#Standardized Regression Weights, all factor loadings are high (i.e., >.70)
inspect(fit, what="std")## $lambda
## anmsty ethncn
## ANI1 0.847 0.000
## ANI2 0.937 0.000
## ANI3 0.893 0.000
## ANI4 0.721 0.000
## ANI5 -0.095 0.000
## ETHNO1 0.000 0.869
## ETHNO2 0.000 0.839
## ETHNO3 0.000 0.940
##
## $theta
## ANI1 ANI2 ANI3 ANI4 ANI5 ETHNO1 ETHNO2 ETHNO3
## ANI1 0.283
## ANI2 0.000 0.123
## ANI3 0.000 0.000 0.203
## ANI4 0.000 0.000 0.000 0.480
## ANI5 0.000 0.000 0.000 0.000 0.991
## ETHNO1 0.000 0.000 0.000 0.000 0.000 0.245
## ETHNO2 0.000 0.000 0.000 0.000 0.000 0.000 0.297
## ETHNO3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.117
##
## $psi
## anmsty ethncn
## animosity 1.000
## ethnocentrism 0.307 1.000#check if model fits data, commonly accepted thresholds
#Chi-square: p > 0.05
#CFI: > 0.90
#TLI: > 0.95 (0.90)
#RMSEA: < 0.10
summary(fit, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-8 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 17
##
## Number of observations 123
##
## Model Test User Model:
##
## Test statistic 39.642
## Degrees of freedom 19
## P-value (Chi-square) 0.004
##
## Model Test Baseline Model:
##
## Test statistic 673.798
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.968
## Tucker-Lewis Index (TLI) 0.953
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1399.814
## Loglikelihood unrestricted model (H1) -1379.993
##
## Akaike (AIC) 2833.628
## Bayesian (BIC) 2881.435
## Sample-size adjusted Bayesian (BIC) 2827.682
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.094
## 90 Percent confidence interval - lower 0.052
## 90 Percent confidence interval - upper 0.135
## P-value RMSEA <= 0.05 0.043
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.056
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## animosity =~
## ANI1 1.000 1.183 0.847
## ANI2 1.037 0.074 13.937 0.000 1.227 0.937
## ANI3 1.014 0.078 13.010 0.000 1.199 0.893
## ANI4 0.831 0.090 9.267 0.000 0.983 0.721
## ANI5 -0.112 0.110 -1.021 0.307 -0.132 -0.095
## ethnocentrism =~
## ETHNO1 1.000 1.145 0.869
## ETHNO2 1.174 0.097 12.068 0.000 1.344 0.839
## ETHNO3 1.125 0.081 13.845 0.000 1.289 0.940
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## animosity ~~
## ethnocentrism 0.416 0.140 2.972 0.003 0.307 0.307
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ANI1 0.552 0.087 6.374 0.000 0.552 0.283
## .ANI2 0.211 0.056 3.750 0.000 0.211 0.123
## .ANI3 0.366 0.068 5.423 0.000 0.366 0.203
## .ANI4 0.893 0.123 7.248 0.000 0.893 0.480
## .ANI5 1.931 0.246 7.837 0.000 1.931 0.991
## .ETHNO1 0.427 0.078 5.441 0.000 0.427 0.245
## .ETHNO2 0.762 0.125 6.111 0.000 0.762 0.297
## .ETHNO3 0.219 0.076 2.870 0.004 0.219 0.117
## animosity 1.399 0.244 5.736 0.000 1.000 1.000
## ethnocentrism 1.312 0.222 5.903 0.000 1.000 1.000Let’s continue by dowloading the current github repo and import it to the R environment.
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