miceadds (version 2.10-14)

tw.imputation: Two-Way Imputation

Description

Two-way imputation using the simple method of Sijtsma and van der Ark (2003) and the MCMC based imputation of van Ginkel et al. (2007).

Usage

tw.imputation(data, integer = FALSE)
tw.mcmc.imputation(data, iter = 100, integer = FALSE)

Arguments

data

Matrix of item responses corresponding to a scale

integer

A logical indicating whether imputed values should be integers. The default is FALSE.

iter

Number of iterations

Value

A matrix with original and imputed values

Details

For persons \(p\) and items \(i\), the two-way imputation is conducted by posing a linear model of tau-equivalent measurements: $$X_{pi} = \theta_p + b_i + \varepsilon_{ij} $$ If the score \(X_{pi}\) is missing then it is imputed by $$\hat{X}_{pi} = \tilde{X}_p + b_i $$ where \(\tilde{X}_p\) is the person mean of person \(p\) of the remaining items with observed responses.

The two-way imputation can also be seen as a scaling procedure to obtain a scale score which takes different item means into account.

References

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.

Van Ginkel, J. R., Van der Ark, A., Sijtsma, K., & Vermunt, J. K. (2007). Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation. Computational Statistics & Data Analysis, 51, 4013-4027.

See Also

The two-way imputation method is also implemented in the TestDataImputation::Twoway function of the TestDataImputation package.

Examples

# NOT RUN {
#############################################################################
# EXAMPLE 1: Two-way imputation data.internet
#############################################################################

data(data.internet)
data <- data.internet

#***
# Model 1: Two-way imputation method of Sijtsma and van der Ark (2003)
set.seed(765)
dat.imp <- miceadds::tw.imputation( data )
dat.imp[ 278:281,]
  ##       IN9     IN10    IN11     IN12
  ##   278   5 4.829006 5.00000 4.941611
  ##   279   5 4.000000 4.78979 4.000000
  ##   280   7 4.000000 7.00000 7.000000
  ##   281   4 3.000000 5.00000 5.000000

# }
# NOT RUN {
#***
# Model 2: Two-way imputation method using MCMC
dat.imp <- miceadds::tw.mcmc.imputation( data , iter=3)
dat.imp[ 278:281,]
  ##       IN9     IN10     IN11     IN12
  ##   278   5 6.089222 5.000000 3.017244
  ##   279   5 4.000000 5.063547 4.000000
  ##   280   7 4.000000 7.000000 7.000000
  ##   281   4 3.000000 5.000000 5.000000
# }