ImputeTestData: This main function imputes for missing responses using...

In TestDataImputation: Missing Item Responses Imputation for Test and Assessment Data

Description

This function imputes for all missing responses using the selected imputation method. Integrated scores are obtained by rounding imputed values to the closest possible response value.

Usage

ImputeTestData(
  test.data,
  Mvalue = "NA",
  max.score = 1,
  method = "LW",
  round.decimal = 0
)

Arguments

test.data	Test data set (a data frame or a matrix) containing missing responses. Missing values are coded as NA or other values (e.g., 8, 9).
Mvalue	Missing response indicators in the data (e.g. "NA", "8", "9", etc.). Mvalue="NA" by default.
max.score	The max possible response value in test data. By default max.score=1 (i.e.,binary test data). max.score = 2 if the response categories are (0, 1, 2), etc. Note: For IN and RF, the lowest response value should be zero (i.e., incorrect).
method	Missing response imputation methods. "LW" (by default) represents listwise that deletes all examinees who reported missing responses (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>) "IN" means treating all missing responses as incorrect (see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; Pohl et al., 2014 <doi: 10.1177/0013164413504926>). "PM" imputes for all missing responses of an examinee by his/her mean on the available items. "IM" imputes for all missing responses of an item by its mean on the available responses. "TW" imputes for all missing responses using two-way imputation (if an examinee has no response to all items, the missing responses are replaced by item means first; see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>). "RF" imputes for all missing responses using response function imputation (Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>). "LR" imputes for all missing responses using logistic regression (for binary responses) and polytomous regression (for polytmous responses) with mice package (Van Buuren & Groothuis-Oudshoorn, 2011 <doi: 10.18637/jss.v045.i03>). "PMM" imputes for all missing responses using predictive mean matching with mice package (Van Buuren & Groothuis-Oudshoorn, 2011 <doi: 10.18637/jss.v045.i03>). "EM" imputes for all missing responses using EM imputation with the Amelia package (Honaker et al., 2011 <doi: 10.18637/jss.v045.i07>). The imputed values are then rounded to the closest possible response value. (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
round.decimal	The number of digits or decimal places for the imputed value. The default value is 0.

Value

A data frame with all missing responses replaced by integrated imputed values.

References

De Ayala, R. J., Plake, B. S., & Impara, J. C. (2001). "The impact of omitted responses on the accuracy of ability estimation in item response theory." Journal of Educational Measurement, 38(3), 213-234. doi:10.1111/j.1745-3984.2001.tb01124.x.

Finch, H. (2008). "Estimation of Item Response Theory Parameters in the Presence of Missing Data." Journal of Educational Measurement, 45(3), 225-245. doi: 10.1111/j.1745-3984.2008.00062.x.

Honaker, J., King, G., & Blackwell, M. (2011). "Amelia II: A program for missing data." Journal of statistical software, 45(1), 1-47. doi: 10.18637/jss.v045.i07.

Lord, F. M. (1974). " Quick estimates of the relative efficiency of two tests as a function of ability level." Journal of Educational Measurement, 11(4), 247-254. doi: 10.1111/j.1745-3984.1974.tb00996.x.

Mislevy, R. J., & Wu, P. K. (1996). " Missing responses and IRT ability estimation: Omits, choice, time limits, and adaptive testing. " ETS Research Report Series, 1996(2), i-36. doi: 10.1002/j.2333-8504.1996.tb01708.x.

Pohl, S., Gräfe, L., & Rose, N. (2014). "Dealing with omitted and not-reached items in competence tests evaluating approaches accounting for missing responses in item response theory models. " Educational and Psychological Measurement, 74(3), 423-452. doi: 10.1177/0013164413504926.

Sijtsma, K., & Van der Ark, L. A. (2003). "Investigation and treatment of missing item scores in test and questionnaire data." Multivariate Behavioral Research, 38(4), 505-528. doi: 10.1207/s15327906mbr3804_4.

Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). "mice: Multivariate imputation by chained equations in R." Journal of statistical software, 45(1), 1-67. DOI: 10.18637/jss.v045.i03.

Examples

 
        ImputeTestData(test.data, Mvalue="NA",max.score=1, method ="TW",round.decimal=0)

TestDataImputation documentation built on Oct. 19, 2021, 1:07 a.m.