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Related Concept Videos

P-value01:10

P-value

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P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
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A complete procedure for testing a claim about a population proportion is provided here.
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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What can we learn from Plausible Values?

Maarten Marsman1,2, Gunter Maris3,4, Timo Bechger4

  • 1Department of Psychology, University of Amsterdam, Nieuwe Prinsengracht 129-B, P.O. Box 15906, 1001  NK, Amsterdam, The Netherlands. m.marsman@uva.nl.

Psychometrika
|April 8, 2016
PubMed
Summary
This summary is machine-generated.

Plausible values consistently estimate latent variable distributions. This finding clarifies misconceptions and improves educational survey analysis.

Keywords:
Bayesian theoryeducational surveysitem response theoryplausible values

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Area of Science:

  • Statistics
  • Educational Measurement
  • Psychometrics

Background:

  • Plausible values are frequently used in educational survey analysis.
  • Misconceptions exist regarding the properties and interpretation of plausible values.
  • Understanding the statistical properties of plausible values is crucial for accurate analysis.

Purpose of the Study:

  • To demonstrate that the marginal distribution of plausible values is a consistent estimator of the true latent variable distribution.
  • To clarify existing misconceptions surrounding the use and interpretation of plausible values.
  • To illustrate the application of plausible values in the analysis of educational survey data.

Main Methods:

  • Theoretical statistical analysis of plausible value distributions.
  • Investigation of convergence properties in an infinite item embedding.
  • Demonstration of consistent estimation of latent variable distributions.

Main Results:

  • The marginal distribution of plausible values is a consistent estimator of the true latent variable distribution.
  • Convergence of this estimation is monotone as the number of items approaches infinity.
  • The study clarifies common misunderstandings about plausible values.

Conclusions:

  • The marginal distribution of plausible values provides a statistically sound method for estimating latent variable distributions.
  • This research offers a clearer theoretical foundation for using plausible values in educational research.
  • The findings support the robust application of plausible values in educational survey analyses.