FIPC Linking Across Multidimensional Test Forms: Effects of Confounding Difficulty within Dimensions

Kim, Sohee; Cole, Ki; Mwavita, Mwarumba

doi:10.1080/15305058.2018.1428980

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Arai and Mayekawa (2011) and Kim et al. (2018) show that bias in item parameter estimates decreases with an increasing number of items with parameter estimates available from previous calibrations. Second, the software used for scaling the PISA data, mdltm (von Davier, 2005), combines multiple updates of the prior latent ability distribution with multiple EM cycles.…”

Section: Discussionmentioning

confidence: 99%

“…Other research on FIPC indicates that it may yield biased model parameters under certain conditions. Bias depends on the sample size (Hanson & Béguin, 2002; Kang & Petersen, 2012), the number of items with parameters available from previous calibrations (e.g., Arai & Mayekawa, 2011; Kim, Cole, & Mwavita, 2018), the amount of cross‐national DIF (Sachse, Roppelt, & Haag, 2016), and shifts in the latent ability distributions across assessments (e.g., Baldwin, Baldwin, & Nering, 2007; Keller, Keller, & Baldwin, 2007). Keller and Keller (2011, 2015), however, showed that FIPC works best for complex changes in the latent ability distributions and in cases where the content of the assessment changes.…”

Section: Purpose Of the Study And Research Questionsmentioning

confidence: 99%

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The Benefits of Fixed Item Parameter Calibration for Parameter Accuracy in Small Sample Situations in Large‐Scale Assessments

König

Khorramdel

Yamamoto

et al. 2020

Educational Measurement

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Large‐scale assessments such as the Programme for International Student Assessment (PISA) have field trials where new survey features are tested for utility in the main survey. Because of resource constraints, there is a trade‐off between how much of the sample can be used to test new survey features and how much can be used for the initial item response theory (IRT) scaling. Utilizing real assessment data of the PISA 2015 Science assessment, this article demonstrates that using fixed item parameter calibration (FIPC) in the field trial yields stable item parameter estimates in the initial IRT scaling for samples as small as n = 250 per country. Moreover, the results indicate that for the recovery of the county‐specific latent trait distributions, the estimates of the trend items (i.e., the information introduced into the calibration) are crucial. Thus, concerning the country‐level sample size of n = 1,950 currently used in the PISA field trial, FIPC is useful for increasing the number of survey features that can be examined during the field trial without the need to increase the total sample size. This enables international large‐scale assessments such as PISA to keep up with state‐of‐the‐art developments regarding assessment frameworks, psychometric models, and delivery platform capabilities.

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Section: Discussionmentioning

confidence: 99%

Section: Purpose Of the Study And Research Questionsmentioning

confidence: 99%

The Benefits of Fixed Item Parameter Calibration for Parameter Accuracy in Small Sample Situations in Large‐Scale Assessments

König

Khorramdel

Yamamoto

et al. 2020

Educational Measurement

View full text Add to dashboard Cite

show abstract

“…FPC has been widely studied in the context of unidimensional IRT (UIRT; Ban, Hanson, Wang, Yi, & Harris, ; Hu, Rogers, & Vukmirovic, ; Kang & Petersen, ; Keller & Keller, ; Kim, ; Kim & Kang, ). Recently, Kim, Cole, and Mwavita () have examined the performance of the FPC method for linking multidimensional test forms using the unidimensional two‐parameter logistic model.…”

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confidence: 99%

Two IRT Fixed Parameter Calibration Methods for the Bifactor Model

Kim

2019

J Educational Measurement

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New items are often evaluated prior to their operational use to obtain item response theory (IRT) item parameter estimates for quality control purposes. Fixed parameter calibration is one linking method that is widely used to estimate parameters for new items and place them on the desired scale. This article provides detailed descriptions of two fixed parameter calibration methods for the bifactor model and compares their relative performance through simulation. The two methods, which were natural generalizations of their counterparts in the unidimensional context, are the one prior weights updating and multiple expectation‐maximization (EM) cycles (OWU‐MEM) and multiple prior weights updating and multiple EM cycles (MWU‐MEM) methods. In addition, for comparison purposes, the separate calibration method with Haebara linking was included in the simulation. In general, the MWU‐MEM method recovered item parameters well for both equivalent and nonequivalent groups, whereas the OWU‐MEM method worked well only for equivalent groups. With a few exceptions, the MWU‐MEM and Haebara methods showed comparable item parameter recovery.

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FIPC Linking Across Multidimensional Test Forms: Effects of Confounding Difficulty within Dimensions

Cited by 2 publications

References 19 publications

The Benefits of Fixed Item Parameter Calibration for Parameter Accuracy in Small Sample Situations in Large‐Scale Assessments

The Benefits of Fixed Item Parameter Calibration for Parameter Accuracy in Small Sample Situations in Large‐Scale Assessments

Two IRT Fixed Parameter Calibration Methods for the Bifactor Model

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