The role of plausible values in large-scale surveys

Wu, Margaret

doi:10.1016/j.stueduc.2005.05.005

Cited by 295 publications

(224 citation statements)

References 5 publications

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“…Strictly speaking, the WLE is only an unbiased estimate of the population mean when the same test items are administered to all persons (Wu 2005). In large-scale assessments, however, this requirement is not fulfilled.…”

Section: Discussionmentioning

confidence: 99%

“…θ n estimates therefore have to be generated in an additional step, using the parameters of the fitted model. The most suitable method to generate θ n estimates is plausible value imputation (Mislevy et al 1992;von Davier et al 2009;Wu 2005). This method is based on multiple imputation (Rubin 1987): The conditioning model defines how the observed item responses are related to θ, and the plausible-values approach provides a model-based imputation of missing θ n values.…”

Section: First Type Of Missing Data: Inherently Missing θmentioning

confidence: 99%

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Nested multiple imputation in large-scale assessments

Weirich

Haag

Hecht

et al. 2014

Large-scale Assess Educ

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Background: In order to measure the proficiency of person populations in various domains, large-scale assessments often use marginal maximum likelihood IRT models where person proficiency is modelled as a random variable. Thus, the model does not provide proficiency estimates for any single person. A popular approach to derive these proficiency estimates is the multiple imputation of plausible values (PV) to enable subsequent analyses on complete data sets. The main drawback is that all variables that are to be analyzed later have to be included in the imputation model to allow the distribution of plausible values to be conditional on these variables. These background variables (e.g., sex, age) have to be fully observed which is highly unlikely in practice. In several current large-scale assessment programs missing observations on background variables are dummy coded, and subsequently, dummy codes are used additionally in the PV imputation model. However, this approach is only appropriate for small proportions of missing data. Otherwise the resulting population scores may be biased. Methods: Alternatively, single imputation or multiple imputation methods can be used to account for missing values on background variables. With both imputation methods, the result is a two-step procedure in which the PV imputation is nested within the background variable imputation. In the single+multiple-imputation (SMI), each missing value on background variables is replaced by one value. In the multiple+multiple-imputation (MMI), each missing value is replaced by a set of imputed values. MMI is expected to outperform SMI as SMI ignores the uncertainty due to missing values in the background data. Results: In a simulation study, both methods yielded unbiased population estimates under most conditions. Still, the recovery proportion was slightly higher for the MMI method. Conclusions: The advantages of the MMI method are apparent for fairly high proportions of missing values in combination with fairly high dependency between the latent trait and the probability of missing data on background variables.

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Section: Discussionmentioning

confidence: 99%

Section: First Type Of Missing Data: Inherently Missing θmentioning

confidence: 99%

Nested multiple imputation in large-scale assessments

Weirich

Haag

Hecht

et al. 2014

Large-scale Assess Educ

View full text Add to dashboard Cite

show abstract

“…Plausible values take into account that the measurement of students' educational performance contains uncertainty. They represent the range of abilities that a student is likely to have, given the student's item responses (Wu 2005). To estimate population parameters and their standard errors, we use each plausible value separately for any analysis and aggregate the individual results.…”

Section: Dependent Variablementioning

confidence: 99%

Can children break the cycle of disadvantage? Structure and agency in the transmission of education across generations

Burger

Walk

2016

Soc Psychol Educ

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Research has shown that parents tend to pass educational advantage or disadvantage on to their children. However, little is known about the extent to which the intergenerational transmission of education involves children's agency. In this study we drew from two traditions in sociological and social psychological theorizing -the theory of cultural and social reproduction and the theory of human agency -to examine whether agency influences children's educational performance, and if so, whether this influence can be observed among children across social classes. We used data from the Spanish sample of the Program for International Student Assessment (N = 25,003 15-year-olds). Results indicate that the level of child agency was weakly positively related to social class, that child agency impacted on a child's educational performance, and that the positive effect of agency on educational performance did not vary by social class. This suggests that strategies to enhance disadvantaged children's agency may prove useful in reducing social gradients in educational performance. More generally, our findings may ignite a debate about the role that social structure and human agency play in shaping social inequality and mobility.

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“…Plausible values (i.e., estimates of student ability) were used to address issues of biased statistical inferencing and to allow the use of standard statistical tools to estimate population characteristics (Wu, 2005). Overall reliability of all reading comprehension items were estimated within the range of α = 0.86-0.91.…”

Section: Co-occurring Math and Reading Difficulty (Md/rd)mentioning

confidence: 99%

Individual Differences in Fourth-Grade Math Achievement in Chinese and English

McClung

Arya

2018

Front. Educ.

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Language has been widely acknowledged as a determining factor in mathematical achievement. Less understood, however, is the relationship between students' language and their performance on tests of mathematics when taking into consideration the presence of mathematical difficulties. We investigated the effects of two different language systems, Chinese and English, on the mathematical performance of fourth-grade (or age equivalent) students (N = 23,220) with varying levels of demonstrated mathematical and reading ability. For this investigation, we used a subset of the 2011 Progress in International Reading and Literacy Study (PIRLS) and Trends in International Mathematics and Science Study (TIMSS) from students who were tested in Chinese or English in nine countries. Findings from hierarchical linear modeling (HLM) analyses revealed that the main effect of language on mathematical performance remained significant once variables for mathematical ability were added to the model. Further, significant language-by-mathematical ability interactions were observed when controlling for country, gender, maternal education, and age. Thus, the effect of language on mathematical performance may be especially salient in the presence of mathematical difficulties. Implications of these findings include the need for further investigations of language and its effects on mathematical performance for Chinese-and English-speaking students in order to clarify how this relationship may vary within specific language populations.

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The role of plausible values in large-scale surveys

Cited by 295 publications

References 5 publications

Nested multiple imputation in large-scale assessments

Nested multiple imputation in large-scale assessments

Can children break the cycle of disadvantage? Structure and agency in the transmission of education across generations

Individual Differences in Fourth-Grade Math Achievement in Chinese and English

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