Robustness of Item Parameter Estimation Programs to Assumptions of Unidimensionality and Normality

Kirisci, Levent; Hsu, Tse-Chi; Yu, Lifa

doi:10.1177/01466210122031975

Cited by 90 publications

(92 citation statements)

References 53 publications

(82 reference statements)

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“…However, unidimensional IRT models are robust to moderate degrees of multidimensionality as defined by factor analyses, particularly where the dimensions are highly correlated and/or where the test/index length is more than 20 items and/or the sample size is more than 250 (Kirisci et al, 2001). Local independence is also an important assumption, i.e.…”

Section: Reliabilitymentioning

confidence: 99%

Improving the measurement of material deprivation at the European Union level

Guio

Marlier

Gordon

et al. 2016

Journal of European Social Policy

103

115

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In June 2010, European Union (EU) Heads of State and Government adopted a social inclusion target as part of the new ‘Europe 2020 Strategy’: to lift at least 20 million people in the EU from the risk of poverty and exclusion by 2020. One of the three indicators used to monitor progress towards this target is the EU indicator of severe material deprivation (MD). A main limitation of this indicator is the weak reliability of some of the items it is based on. For this reason, a thematic module on MD was included in the 2009 wave of the EU Statistics on Income and Living Conditions (EU-SILC) survey. This article assesses the 2009 EU-SILC MD data and proposes an analytical framework for developing robust EU MD indicators. It carries out a systematic item by item analysis at both EU and country levels to identify the MD items which satisfactorily meet suitability, validity, reliability and additivity criteria across the EU. This approach has resulted in a proposed 13-item MD indicator covering some key aspects of living conditions which are customary across the whole EU covering a broad range of basic (food, clothes, shoes, etc.) as well as social (Internet, regular leisure activities, etc.) necessities.

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Section: Reliabilitymentioning

confidence: 99%

Improving the measurement of material deprivation at the European Union level

Guio

Marlier

Gordon

et al. 2016

Journal of European Social Policy

103

115

View full text Add to dashboard Cite

show abstract

“…In IRT, where the application of unidimensional measurement models dominates, a number of studies have explored the robustness of item parameter estimates to violations of unidimensionality (e.g., Drasgow & Parsons, 1983;Folk & Green, 1989;Kirisci, Hsu, & Yu, 2001). This research has generally shown that if a strong general factor exists in the data, then the estimated IRT item parameters are relatively unbiased when fit to a unidimensional measurement model.…”

mentioning

confidence: 99%

When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling? An Evaluation of the DETECT Multidimensionality Index

Bonifay

Reise

Scheines

et al. 2015

Structural Equation Modeling: A Multidisciplinary Journal

112

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In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT "essential unidimensionality" index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided.

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“…Contrary to the beliefs held by many substantive researchers, simulation research indicates that results from IRT models can be nontrivially biased when the true population distribution is nonnormal (Abdel-fattah, 1994;Boulet, 1996;de Ayala & SavaBolesta, 1999;DeMars, 2003;Kirisci, Hsu, & Yu, 2001;Seong, 1990 ;Stone, 1992; van den Oord, 2005;Zwinderman & van den Wollenberg, 1990). Specifically, MML estimates of item parameters increase in bias as the distribution deviates further from normality (Boulet, 1996;Stone, 1992;Woods, 2006Woods, , 2007aWoods, , 2007bWoods, , 2008Woods & Lin, 2009;Woods & Thissen, 2006).…”

Section: Effects Of Nonnormalitymentioning

confidence: 97%

Estimating the Nominal Response Model Under Nonnormal Conditions

Preston

Reise

2013

Educational and Psychological Measurement

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The nominal response model (NRM), a much understudied polytomous item response theory (IRT) model, provides researchers the unique opportunity to evaluate within-item category distinctions. Polytomous IRT models, such as the NRM, are frequently applied to psychological assessments representing constructs that are unlikely to be normally distributed in the population. Unfortunately, models estimated using estimation software with the MML/EM algorithm frequently employs a set of normal quadrature points, effectively ignoring the true shape of the latent trait distribution. To address this problem, the current research implements an alternative estimation approach, Ramsay Curve Item Response Theory (RC-IRT), to provide more accurate item parameter estimates modeled under the NRM under normal, skewed, and bimodal latent trait distributions for ordered polytomous items. Based on the results of improved item parameter recovery under RC-IRT, it is recommended that RC-IRT estimation be implemented whenever a researcher considers the construct being measured has the potential of being nonnormally distributed.

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Robustness of Item Parameter Estimation Programs to Assumptions of Unidimensionality and Normality

Cited by 90 publications

References 53 publications

Improving the measurement of material deprivation at the European Union level

Improving the measurement of material deprivation at the European Union level

When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling? An Evaluation of the DETECT Multidimensionality Index

Estimating the Nominal Response Model Under Nonnormal Conditions

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