RESUMO
This simulation study explores the effects of missing data mechanisms, proportions of missing data, sample size, and test length on the biases and standard errors of item parameters using the Rasch measurement model. When responses were missing completely at random (MCAR) or missing at random (MAR), item parameters were unbiased. When responses were missing not at random (MNAR), item parameters were severely biased, especially when the proportion of missing responses was high. Standard errors were primarily affected by sample size, with larger samples associated with smaller standard errors. Standard errors were inflated in MCAR and MAR conditions, while MNAR standard errors were similar to what they would have been, had the data been complete. This paper supports the conclusion that the Rasch model can handle varying amounts of missing data, provided that the missing responses are not MNAR.
Assuntos
Interpretação Estatística de Dados , Modelos Estatísticos , Viés , Psicometria , Tamanho da AmostraRESUMO
This paper investigates a strategy for accounting for correct guessing with the Rasch model that we entitled the Guessing Adjustment. This strategy involves the identification of all person/item encounters where the probability of a correct response is below a specified threshold. These responses are converted to missing data and the calibration is conducted a second time. This simulation study focuses on the effects of different probability thresholds across varying conditions of sample size, amount of correct guessing, and item difficulty. Biases, standard errors, and root mean squared errors were calculated within each condition. Larger probability thresholds were generally associated with reductions in bias and increases in standard errors. Across most conditions, the reduction in bias was more impactful than the decrease in precision, as reflected by the RMSE. The Guessing Adjustment is an effective means for reducing the impact of correct guessing and the choice of probability threshold matters.
