The Role of Plausible Values in Large-Scale Surveys.

By Margaret Wu. 2005

413 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/0b0338fd6a1dd90bb629f3128e39a84befe129e8

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In Management Science and Operations Research

In Statistics and Probability

In Water Science and Technology

Citations in the corpus

The Influence of Socioeconomic Status, Self-Efficacy, and Anxiety on Mathematics Achievement in England, Greece, Hong Kong, the Netherlands, Turkey, and the USA (2015, background): “Plausible values represent the range of abilities that a student might reasonably possess based on a combination of their responses to the subset of items that they receive and other relevant background information (Mislevy, 1991; Wu, 2005).”
The acquisition of problem solving competence: evidence from 41 countries that math and science education matters (2014, methodology): “The resulting plausible values could be regarded as valid indicators of students’ abilities in problem solving (Wu 2005).”
Relationship between affect and achievement in science and mathematics in Malaysia and Singapore (2012, background): “‘Plausible values are multiple imputations of the unobservable latent achievement for each student’ (Wu 2005, 114).”
Effect size measures for multilevel models: definition, interpretation, and TIMSS example (2018, methodology): “Several resources are available to learn more about these topics (Martin and Mullis 2012; Meinck and Vandenplas 2012; Rogers and Stoeckel 2008; Snijders and Bosker 2012; Wu 2005) and specific software is available to aid in analysis, such as the BIFIE package (BIFIE 2017) which is implemented in R.”
Expectancy value interactions and academic achievement: Differential relationships with achievement measures (2019, methodology): “PVs are used to gain more accurate estimates regarding associations on population level by correcting students’ ability for measurement error (see Wu, 2005; Von Davier, Gonzalez, & Mislevy, 2009).”
Comparing the Similarities and Differences of PISA 2003 and TIMSS (2010, background): “Readers can refer to Wu (2005b) for a more detailed explanation on plausible values.”
Assessment and learning: fields apart? (2017, background): “An overall score for each student is produced, and projections of their likely performance on items they were not exposed to is imputed (Wu, 2005).”
Mathematics Achievement: The Role of Homework and Self-Efficacy Beliefs (2011, background): “The plausible values were random elements from the set of scores that could be attributed to each student and their variation helped capture the measurement error at the individual student level (Wu, 2005).”
Using plausible values in secondary analysis in large-scale assessments (2017, background): “This paper differs from Wu (2005), von Davier et al. (2009), and Carstens and Hastedt (2010) articles as the focus is onMLMs (as described in Gelman and Hill (2006), which neither of them covered).”
Analyzing the Gender Gap in Math Achievement: Evidence from a Large-Scale US Sample (2013, background): “These plausible values represented random elements from the set of scores that could be attributed to each student, and the variation within these values was representative of the measurement error at the individual student level (Wu, 2005).”
Gender Segregation of Adolescent Science Career Plans in 50 Countries. (2012, neutral): “, five plausible values (OECD, 2009, p. 43; Wu, 2005).”