| dc.description.abstract | Many item-response models (IRM) used to estimate student abilities and test item parameters in
large-scale assessments (LSA) do not account for the effect of student low motivation. This
effect may pose a threat to the validity of test data interpretations. The first purpose of this study
was to evaluate the effect of removing examinees with low motivation on the estimates of
examinee abilities and test-item parameters calibrations using an item response theory
model. The second purpose was to examine the significance of the relationship between
students’ motivation and their mathematics achievement, as measured by the LSA. Student
motivation as defined by expectancy-value theory and self-efficacy theory was identified from
self-report data using a principal component analysis. Two components scores, mathematics
values and interest, were computed for each examinee to create two groups of examinees with
high and low motivation. These groups were used to examine the effect of removing examinees
with low motivation on the estimates of test item parameters and student abilities between a 3-
parameter logistic (3PL) and modified 3PL IRM. The effect of student motivation on their
academic achievement was examined using a hierarchical linear model (HLM). The results
suggested that the modified 3PL IRM seemed to minimize the effect of low motivation on the
estimates of student abilities and test item parameters when compared to the 3PL. The results
from the HLM suggested that student mathematics values and interest are significant predictors
of students’ academic achievement. The outcome of this study builds on the research work of
Swerdzewski, Harmes, and Finneys (2011) and supports the research work of Wise and DeMars
(2006); Wolf and Smith (1995) in the use of IRM and measures of student motivation to provide
more accurate interpretations of LSA data. | |
