When equating two tests, the traditional approach is to use common test takers and/or common items. Here, the idea is to use variables correlated with the test scores (e.g., school grades and other test scores) as a substitute for common items in a non-equivalent groups with covariates (NEC) design. This is performed in the framework of kernel equating and with an extension of the method developed for post-stratification equating in the non-equivalent groups with anchor test design. Real data from a college admissions test were used to illustrate the use of the design. The equated scores from the NEC design were compared with equated scores from the equivalent group (EG) design, that is, equating with no covariates as well as with equated scores when a constructed anchor test was used. The results indicate that the NEC design can produce lower standard errors compared with an EG design. When covariates were used together with an anchor test, the smallest standard errors were obtained over a large range of test scores. The results obtained, that an EG design equating can be improved by adjusting for differences in test score distributions caused by differences in the distribution of covariates, are useful in practice because not all standardized tests have anchor tests.

This paper examined observed score linear equating in two different data collection designs, the equivalent groups design and the nonequivalent groups design, when information from covariates (i.e., background variables correlated with the test scores) was included. The main purpose of the study was to examine the effect (i.e., bias, variance, and mean squared error) on the estimators of including this additional information. A model for observed score linear equating with covariates first was suggested. As a second step, the model was used in a simulation study to show that the use of covariates such as gender and education can increase the accuracy of an equating by reducing the mean squared error of the estimators. Finally, data from two administrations of the Swedish Scholastic Assessment Test were used to illustrate the use of the model.

In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the kernel method of test equating, which is a conjoint framework of test equating, has gained popularity. The kernel method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counter balanced, non-equivalent groups with anchor test using either chain equating or poststratification equating, and non-equivalent groups using covariates. An R package for the kernel method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.

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