The Partial Credit Model

Masters, Geoff N

doi:10.1007/978-1-4757-2691-6_6

Cited by 188 publications

(124 citation statements)

References 28 publications

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“…The iteration process continues until the maximum difference in item and person values during successive iterations converges to a preset value. When this is complete, the difference in person ability and item difficulty values will produce the Rasch probabilities of success, and as a result, the ordinal level data are transformed into interval level data that is suitable for inference (Masters & Wright, 1997).…”

Section: The Dichotomous Modelmentioning

confidence: 99%

“…That is, person free measures and item free calibrations are obtained. Therefore abstract measures that transcend specific persons" responses to specific items at a specific time are achieved (Masters & Wright, 1997). …”

Section: The Dichotomous Modelmentioning

confidence: 99%

“…It can be used in any situation in which performances on an item or an assessment criterion are recorded in two or more ordered categories, and where there is an aim of obtaining measures on a latent unidimensional variable (Masters & Wright, 1997). These ordered categories can be considered as a series of steps.…”

Section: The Partial Credit Modelmentioning

confidence: 99%

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Students’ Understanding of Statistical Inference: Implications for Teaching

Reaburn

2018

Structuring the Thesis

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Section: The Dichotomous Modelmentioning

confidence: 99%

Section: The Dichotomous Modelmentioning

confidence: 99%

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Students’ Understanding of Statistical Inference: Implications for Teaching

Reaburn

2018

Structuring the Thesis

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“…Rasch software programs such as MINISTEP and QUEST print out the "infit" and "outfit" goodness-of-fit statistics along with the estimated parameters (Masters & Wright, 1997). These statistics can be used to further examine the results obtained from R. They can also be used for instructional purposes.…”

Section: Results Comparisonmentioning

confidence: 99%

“…requires weighting the standardized residuals by their variances, thus lessening the impact of unexpected responses (Bond & Fox, 2001;Masters & Wright, 1997), as shown at the top of the next page.…”

Section: Results Comparisonmentioning

confidence: 99%

Using the open-source statistical language R to analyze the dichotomous Rasch model

2006

Behavior Research Methods

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R, an open-source statistical language and data analysis tool, is gaining popularity among psychologists currently teaching statistics. R is especially suitable for teaching advanced topics, such as fitting the dichotomous Rasch model-a topic that involves transforming complicated mathematical formulas into statistical computations. This article describes R's use as a teaching tool and a data analysis software program in the analysis of the Rasch model in item response theory. It also explains the theory behind, as well as an educator's goals for, fitting the Rasch model with joint maximum likelihood estimation. This article also summarizes the R syntax for parameter estimation and the calculation of fit statistics. The results produced by R is compared with the results obtained from MINISTEP and the output of a conditional logit model. The use of R is encouraged because it is free, supported by a network of peer researchers, and covers both basic and advanced topics in statistics frequently used by psychologists.

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Item Response Theory (IRT) Models for Rating Scale Data

Engelhard

2005

Encyclopedia of Statistics in Behavioral Science

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The purpose of this entry is to provide a brief description of several IRT models that can be used to analyze rating scale data. Rating scale data are defined as responses scored in two or more ordered categories. Rating scale data represent the most common formats for collecting information from examinees or raters. The basic idea that motivates the use of IRT models for rating scale data is that the scoring of m + 1 ordered categories with ordered integers (0, 1, …, m ) using the assumption that there are equal intervals between the categories may not be justified. IRT models provide a framework to explicitly examine this assumption, and parameterize the categories without this assumption. The specific IRT models that are described in this entry are direct models (Partial Credit Model, Generalized Partial Credit, and Rating Scale Model) and indirect models (Graded Response Model and Modified Graded Response Model). Graphical procedures are used to illustrate various aspects of these IRT models.

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The Partial Credit Model

Cited by 188 publications

References 28 publications

Students’ Understanding of Statistical Inference: Implications for Teaching

Students’ Understanding of Statistical Inference: Implications for Teaching

Using the open-source statistical language R to analyze the dichotomous Rasch model

Item Response Theory (IRT) Models for Rating Scale Data

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