mokken (version 3.0.4)

twoway: Two-way imputation

Description

Returns a single or multiple completed data sets using two-way imputation with normally distributed errors.

Usage

twoway(X, nCompletedDataSets = 1, minX = defaultMinX, maxX = defaultMaxX, seed = FALSE)

Arguments

X

matrix or data frame of integer data containing the score of now(X) respondents to nicol(X) items. Typically X contains missing values.

nCompletedDataSets

Number of completed data sets.

minX

Minimum item score. By default, the minimum item score is the lowest score found in the data.

maxX

Maximum item score. By default, the maximum item score is the highest score found in the data.

seed

Seed for random sampling. If seed = FALSE (default), no seed is given, otherwise seed must be a numeric value. Replications having the same seed result in exactly the same outcome value.

Value

The result is X for which the missing values have been replaced by imputed values. For multiple imputations, the result is a list of matrices/data frames. For single imputations, the result is a matrix/data frame.

Details

For single imputation (nCompletedDataSets == 1, default) the function returns an object of the same class as X, for multiple imputation (nCompletedDataSets > 1) the function returns a list. References for two-way imputation include Bernaards and Sijtsma (2000), Sijtsma and Van der Ark (2003), and Van Ginkel, Van der Ark, and Sijtsma (2007).

References

Bernaards, C. A., & Sijtsma, K. (2000). Influence of simple imputation and EM methods on factor analysis when item nonresponse in questionnaire data is nonignorable Multivariate Behavioral Research, 35, 321-364. https://doi.org/10.1207/S15327906MBR3503_03

Sijtsma, K., & Van der Ark, L. A. (2003). Investigation and treatment of missing item scores in test and questionnaire data. Multivariate Behavioral Research, 38, 505-528. https://doi.org/10.1207/s15327906mbr3804_4

Van Ginkel, J. R., Van dec Ark, L. A., & Sijtsma, K. (2007). Multiple imputation of item scores in test and questionnaire data, and influence on psychometric results. Multivariate aBehavioral Research, 42, 387-414. https://doi.org/10.1080/00273170701360803

See Also

DS14, recode

Examples

# NOT RUN {
  data(DS14)

  # Handle missing data and recode negatively worded items
  X <- DS14[, 3 : 16]
  X <- twoway(X)
  X <- recode(X, c(1, 3))
  head(X)
# }