The use of test scores from large-scale assessment surveys: psychometric and statistical considerations

Abstract

Abstract Background Economists are making increasing use of measures of student achievement obtained through large-scale survey assessments such as NAEP, TIMSS, and PISA. The construction of these measures, employing plausible value (PV) methodology, is quite different from that of the more familiar test scores associated with assessments such as the SAT or ACT. These differences have important implications both for utilization and interpretation. Although much has been written about PVs, it appears that there are still misconceptions about whether and how to employ them in secondary analyses. Methods We address a range of technical issues, including those raised in a recent article that was written to inform economists using these databases. First, an extensive review of the relevant literature was conducted, with particular attention to key publications that describe the derivation and psychometric characteristics of such achievement measures. Second, a simulation study was carried out to compare the statistical properties of estimates based on the use of PVs with those based on other, commonly used methods. Results It is shown, through both theoretical analysis and simulation, that under fairly general conditions appropriate use of PV yields approximately unbiased estimates of model parameters in regression analyses of large scale survey data. The superiority of the PV methodology is particularly evident when measures of student achievement are employed as explanatory variables. Conclusions The PV methodology used to report student test performance in large scale surveys remains the state-of-the-art for secondary analyses of these databases.

Keywords

• Large-scale assessment
• Imputation
• Plausible values
• Conditioning model
• IRT
• Unbiasedness