Unidimensional Interpretations for Multidimensional Test Items

Kahraman, Nilüfer

doi:10.1111/jedm.12012

Cited by 11 publications

(15 citation statements)

References 17 publications

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“…The fact that the least error was obtained for the discrimination parameters when the correlation between dimensions was 0.45 is likely due to the fact that the average angular distance of the items is comparable to the correlation between dimensions. Furthermore, as reported in a similar study done by Kahraman (2013), the error associated with the discrimination parameters increases as the correlation between dimensions increases, when modeling multidimensional response data with a unidimensional IRT model and ignoring the second dimension. Kahraman proposed that the projection IRT model be used to estimate the discrimination parameter when modeling multidimensional data as unidimensional.…”

Section: Parameter Asupporting

confidence: 54%

Quantitative Psychology Research

Ark

Bolt

Wang

et al. 2015

Springer Proceedings in Mathematics &Amp; Statistics

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This book series features volumes composed of select contributions from workshops and conferences in all areas of current research in mathematics and statistics, including operation research and optimization. In addition to an overall evaluation of the interest, scientific quality, and timeliness of each proposal at the hands of the publisher, individual contributions are all refereed to the high quality standards of leading journals in the field. Thus, this series provides the research community with well-edited, authoritative reports on developments in the most exciting areas of mathematical and statistical research today.L. Andries van der Ark • Daniel M. Bolt

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Section: Parameter Asupporting

confidence: 54%

Quantitative Psychology Research

Ark

Bolt

Wang

et al. 2015

Springer Proceedings in Mathematics &Amp; Statistics

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“…In another study carried out by Gocer Sahin (2016), a multidimensional test with a semi-mixed structure was estimated as unidimensional, and the same unexpected pattern related to correlation and test parameters was obtained. A similar study carried out by Kahraman (2013) reported that errors of discrimination increased as the correlation increased when the second dimension of the multidimensional test was ignored and then estimated as unidimensional.…”

Section: Introductionmentioning

confidence: 72%

“…Thus, one factor that makes this study different than others is the test structure. Although the results in the studies conducted by Kahraman (2013), Gocer Sahin, Walker, and Gelbal (2015), Gocer Sahin (2016) appear to be promising, they have not explained the possible reasons behind that results. So, in this study, the focus was on the interaction between correlation and items.…”

Section: Introductionmentioning

confidence: 91%

The Interaction Effect of the Correlation between Dimensions and Item Discrimination on Parameter Estimation

Şahın¹,

Eser

Gelbal

2018

Eğitimde Ve Psikolojide Ölçme Ve Değerlendirme Dergisi

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There are some studies in the literature that have considered the impact of modeling multidimensional mixed structured tests as unidimensional. These studies have demonstrated that the error associated with the discrimination parameters increases as the correlation between dimensions increases. In this study, the interaction between items' angles on coordinate system and the correlations between dimensions was investigated when estimating multidimensional tests as unidimensional. Data were simulated based on two dimensional, and two-parameter compensatory MIRT model. Angles of items were determined as 0.15 o ; 0.30 o ; 0.45 o ; 0.60 o and 0.75 o respectively. The correlations between ability parameters were set to 0.15, 0.30, 0.45, 0.60 and 0.75 respectively, which are same with the angles of discrimination parameters. The ability distributions were generated from standard normal, positively and negatively skewed distributions. A total of 75 (5 x 5 x 3) conditions were studied: five different conditions for the correlation between dimensions; five different angles of items and three different ability distributions. For all conditions, the number of items was fixed at 25 and the sample size was fixed at n = 2,000. Item and ability parameter estimation were conducted using BILOG. For each condition, 100 replications were performed. The RMSE statistic was used to evaluate parameter estimation errors, when multidimensional response data were scaled using a unidimensional IRT model. Based on the findings, it can be concluded that the pattern of RMSE values especially for discrimination parameters are different from the existing studies in the literature in which multidimensional tests were estimated as unidimensional.

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“…Violations of unidimensionality and the consequences of such violations have been investigated in a number of studies (including Ackerman, 1989;Ansley & Forsyth, 1985;Bonifay, Reise, Scheines, & Meijer, 2015;Crişan, Tendeiro, Meijer, 2017;Drasgow & Parsons, 1983;Kahraman, 2013;Kirisci, Hsu & Yu, 2001;Reckase, 1979;Zhang, 2008). In a related line of research, violations of LI were studied (including Chen & Thissen, 1997;DeMars, 2012;Sireci, Thissen, & Wainer, 1991;Wainer & Thissen, 1996;Yen, 1993).…”

mentioning

confidence: 99%

“…More specific information and recommendations about the conditions of robustness were stated in different ways in the studies, depending on how the violations of unidimensionality were conceptualized and manipulated. The violations were conceptualized and manipulated in many different ways, and this heterogeneity is related to challenges in summarizing the findings, and the absence of clear, widely-accepted recommendations for model users (Kahraman, 2013;Kirisci et al, 2001;Ip, 2010).…”

mentioning

confidence: 99%

Revisiting Strong Assumptions in Psychological Constructs Measurement: Further Studying Deviations from Unidimensionality and Bias in Latent Variable Measurement Models

Rajlic¹

2020

Preprint

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In the realities of measurement in social and behavioral sciences, in addition to the characteristic(s) of the respondents targeted by the measurement, other influences (other characteristics of the respondents and the items) can be reflected by the responses to the items in a measure. In the current study, different levels of deviations from strict unidimensionality in measures and the accuracy of parameter estimates of widely used unidimensional latent variable measurement models were further investigated. Of interest were unidimensionality violations in measures intended/designed as unidimensional (when the items primarily reflect a dominant latent dimension, as intended in a unidimensional measure, but also reflect, to a smaller degree, some additional influences). In the simulated conditions of interest, varying degrees of systematic error (bias) in the unidimensional model item and person parameters estimates were demonstrated (e.g., factor loadings overestimation and measurement error underestimation). The strength of the relevant relations and the size of bias were examined. If the size of these systematic distortions is uncommunicated, various negative consequences can ensue for substantive research and applied measurement (in relation to the reliability, validity, and fairness of research and measurement outcomes), when the models estimates are used. The utility of the approach employed in the study was discussed.

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Unidimensional Interpretations for Multidimensional Test Items

Cited by 11 publications

References 17 publications

Quantitative Psychology Research

Quantitative Psychology Research

The Interaction Effect of the Correlation between Dimensions and Item Discrimination on Parameter Estimation

Revisiting Strong Assumptions in Psychological Constructs Measurement: Further Studying Deviations from Unidimensionality and Bias in Latent Variable Measurement Models

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