help for mi_twowayJean-François HAMEL

Two-way imputations: imputing missing item responses in questionnaires for computing scores

mi_twoway varlist, [scorename(newvarname)
replace
add(#)
style(keyword)
clear
da
iterate(#)]

Description

mi_twoway is an implementation of the multiple imputation procedure
proposed by Van Ginkel for computing scores on questionnaires containing missing item responses.

Two methods are available: imputations based on a fixed effects two-way ANOVA, and imputations
generated using data augmentation based on a mixed effect two-way ANOVA (with a random person effect
assumed to follow a Normal distribution and a fixed item effect.

mi_twoway is fully compatible with the Sata mi procedures. The data is set
and imputed using mi_twoway, but all the estimations using multiple imputations are
performed using the standard mi estimate procedures.

varlist is the list of the variables containing the item responses of the questionnaire
(with possible missing data).

Options

scorename specifies the name of the new variable containing, for each individual, the value of the
score computed as the sum of the item responses. scorename is missing for each individual with at least one item
missing response.

replace allows to replace individual scores in existing variables

add specifies the number of imputations to add; required with no imputations

style specifies in which style should be recorded the data: wide, mlong, flong,
or flongsep; see [MI] styles.

clear allows performing new imputations by remouving the previous one.

da generates imputations using data augmentation based on a mixed effect two-way
ANOVA (with a random person effect and a fixed item effect. By default, imputations are generated
using a fixed effects two-way ANOVA.

iterate defines the number of iterations of the data augmentation algorithm.
By default, this number is fixed to 10.

Example

Simulation of the data (using simirt):

. simirt, nbobs(200) dim(5) group(0.5) deltagroup(0.4) clear(1)

Creating the missing data, with a non-response rate of 10%:

. set more off (2)
. forvalues j=1/5{(3)
2. replace item`j'=. if runiform()<0.1(4)
3. }(5)

Generating 10 multiple imputations using a fixed effects two-way ANOVA:

. mi_twoway item*, scorename(score) add(10) style(wide)(6)

Modeling score depending on group covariate using multiple imputations estimates (mi_estimate):

. mi estimate: regress score i.group(7)

Changing the way to impute data, using data augmentation based on a mixed effect two-way ANOVA:

. mi_twoway item*, scorename(score) replace add(10) style(wide) da clear(8)

Changing the style of the data from wide to mlong (mi_convert):

. mi convert mlong(9)

Removal of the multiple imputations (mi_unset):

. mi extract 0, clear(10)

References

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