binHS5imps: List of imputed Holzinger & Swineford (1939) dichotomized...

In lavaan.mi: Fit Structural Equation Models to Multiply Imputed Data

List of imputed Holzinger & Swineford (1939) dichotomized data

Description

A version of the classic Holzinger and Swineford (1939) dataset, with missing data imposed on variables x5 and x9:

Details

• x5 is missing not at random (MNAR) by deleting the lowest 30% of x5 values.

• x9 is missing at random (MAR) conditional on age, by deleting x9 values for the youngest 30% of subjects in the data.

The data are then dichotomized using a median split, and imputed 5 times using the syntax shown in the example. The data include only the 9 tests (x1 through x9) and school.

Author(s)

Terrence D. Jorgensen (University of Amsterdam; [e-mail])

Source

The lavaan package.

References

Holzinger, K., & Swineford, F. (1939). A study in factor analysis: The stability of a bifactor solution. Supplementary Educational Monograph, no. 48. Chicago, IL: University of Chicago Press.

See Also

lavaan::HolzingerSwineford1939

Examples

 
data(HolzingerSwineford1939, package = "lavaan")

## impose missing data for example
HSMiss <- HolzingerSwineford1939[ , c(paste("x", 1:9, sep = ""),
                                      "ageyr","agemo","school")]
set.seed(123)
HSMiss$x5 <- ifelse(HSMiss$x5 <= quantile(HSMiss$x5, .3), NA, HSMiss$x5)
age <- HSMiss$ageyr + HSMiss$agemo/12
HSMiss$x9 <- ifelse(age <= quantile(age, .3), NA, HSMiss$x9)

## median split
HSbinary <- as.data.frame( lapply(HSMiss[ , paste0("x", 1:9)],
                                  FUN = cut, breaks = 2, labels = FALSE) )
HSbinary$school <- HSMiss$school

## impute binary missing data using mice package
library(mice)
set.seed(456)
miceImps <- mice(HSbinary)
## save imputations in a list of data.frames
binHS5imps <- list()
for (i in 1:miceImps$m) binHS5imps[[i]] <- complete(miceImps, action = i)

lavaan.mi documentation built on April 3, 2025, 9:36 p.m.