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Advances in Social Science Research Using R

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Psychoco: Psychometric Computing in R

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This special volume features eleven contributions to psychometric computing, a research area that integrates psychometrics and computational methods in statistics. Topics covered include structural equation modeling, item response theory, probabilistic choice modeling, and other modeling approaches prevalent in or useful for psychometric researc...

This special volume features eleven contributions to psychometric computing, a research area that integrates psychometrics and computational methods in statistics. Topics covered include structural equation modeling, item response theory, probabilistic choice modeling, and other modeling approaches prevalent in or useful for psychometric research. Each contributed paper is accompanied by a software package published on the Comprehensive R Archive Network. This introduction gives a brief overview of the volume. Minimize

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University of California, Los Angeles

Year of Publication:

2012-05-01T00:00:00Z

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article

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English

Subjects:

psychometric computing ; structural equation models ; item response theory ; Rasch model ; choice models ; paired comparisons ; preferences ; computerized adaptive testing ; R ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; L...

psychometric computing ; structural equation models ; item response theory ; Rasch model ; choice models ; paired comparisons ; preferences ; computerized adaptive testing ; R ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics Minimize

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http://www.jstatsoft.org/v48/i01/paper

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A new method for detecting differential item functioning in the Rasch model

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Differential item functioning (DIF) can lead to an unfair advantage or disadvantage for certain subgroups in educational and psychological testing. Therefore, a variety of statistical methods has been suggested for detecting DIF in the Rasch model. Most of these methods are designed for the comparison of pre-specified focal and reference groups,...

Differential item functioning (DIF) can lead to an unfair advantage or disadvantage for certain subgroups in educational and psychological testing. Therefore, a variety of statistical methods has been suggested for detecting DIF in the Rasch model. Most of these methods are designed for the comparison of pre-specified focal and reference groups, such as males and females. Latent class approaches, on the other hand, allow to detect previously unknown groups exhibiting DIF. However, this approach provides no straightforward interpretation of the groups with respect to person characteristics. Here we propose a new method for DIF detection based on model-based recursive partitioning that can be considered as a compromise between those two extremes. With this approach it is possible to detect groups of subjects exhibiting DIF, which are not prespecified, but result from combinations of observed ovariates. These groups are directly interpretable and can thus help understand the psychological sources of DIF. The statistical background and construction of the new method is first introduced by means of an instructive example, and then applied to data from a general knowledge quiz and a teaching evaluation. ; item response theory, IRT, Rasch model, di erential item functioning, DIF, structural change, multidimensionality. Minimize

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preprint

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Statistical Properties of a Test for Random Forest Variable Importance

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Abstract. Random forests have become a widely-used predictive model in many scientific disciplines within the past few years. Additionally, they are increasingly popular for assessing variable importance, e.g., in genetics and bioinformatics. We highlight both advantages and limitations of different variable importance scores and associated test...

Abstract. Random forests have become a widely-used predictive model in many scientific disciplines within the past few years. Additionally, they are increasingly popular for assessing variable importance, e.g., in genetics and bioinformatics. We highlight both advantages and limitations of different variable importance scores and associated testing procedures, especially in the context of correlated predictor variables. For the test of Breiman and Cutler (2008), we investigate the statistical properties and find that the power of the test depends both on the sample size and the number of trees, an arbitrarily chosen tuning parameter, leading to undesired results that nullify any significance judgments. Moreover, the specification of the null hypothesis of this test is discussed in the context of correlated predictor variables. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-12-04

Source:

http://epub.ub.uni-muenchen.de/2111/1/techreport.pdf

http://epub.ub.uni-muenchen.de/2111/1/techreport.pdf Minimize

Document Type:

text

Language:

en

Subjects:

feature selection ; variable importance ; permutation tests

feature selection ; variable importance ; permutation tests Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Statistical sources of variable selection bias in classification tree algorithms based on the gini index

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Evidence for variable selection bias in classification tree algorithms based on the Gini Index is reviewed from the literature and embedded into a broader explanatory scheme: Variable selection bias in classification tree algorithms based on the Gini Index can be caused not only by the statistical effect of multiple comparisons, but also by an i...

Evidence for variable selection bias in classification tree algorithms based on the Gini Index is reviewed from the literature and embedded into a broader explanatory scheme: Variable selection bias in classification tree algorithms based on the Gini Index can be caused not only by the statistical effect of multiple comparisons, but also by an increasing estimation bias and variance of the splitting criterion when plug-in estimates of entropy measures like the Gini Index are employed. The relevance of these sources of variable selection bias in the different simulation study designs is examined. Variable selection bias due to the explored sources applies to all classification tree algorithms based on empirical entropy measures like the Gini Index, Deviance and Information Gain, and to both binary and multiway splitting algorithms. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2012-07-30

Source:

http://epub.ub.uni-muenchen.de/1789/1/paper_420.pdf

http://epub.ub.uni-muenchen.de/1789/1/paper_420.pdf Minimize

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text

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en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Flexible Rasch Mixture Models with Package psychomix

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Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estim...

Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estimation of the item parameters (given the raw scores) along with flexible specification of two model building blocks: (1) Mixture weights for the unobserved classes can be treated as model parameters or based on covariates in a concomitant variable model. (2) The distribution of raw score probabilities can be parametrized in two possible ways, either using a saturated model or a specification through mean and variance. The function raschmix() in the R package psychomix provides these models, leveraging the general infrastructure for fitting mixture models in the flexmix package. Usage of the function and its associated methods is illustrated on artificial data as well as empirical data from a study of verbally aggressive behavior. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2012-05-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

mixed Rasch model ; Rost model ; mixture model ; flexmix ; R ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics

mixed Rasch model ; Rost model ; mixture model ; flexmix ; R ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics Minimize

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http://www.jstatsoft.org/v48/i07/paper

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Title:

Flexible Rasch Mixture Models with Package psychomix

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Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estim...

Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estimation of the item parameters (given the raw scores) along with flexible specification of two model building blocks: (1) Mixture weights for the unobserved classes can be treated as model parameters or based on covariates in a concomitant variable model. (2) The distribution of raw score probabilities can be parametrized in two possible ways, either using a saturated model or a specification through mean and variance. The function raschmix() in the R package "psychomix" provides these models, leveraging the general infrastructure for fitting mixture models in the "flexmix" package. Usage of the function and its associated methods is illustrated on artificial data as well as empirical data from a study of verbally aggressive behavior. ; mixed Rasch model, Rost model, mixture model, flexmix, R Minimize

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preprint

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Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics, accepted for publication

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ePubWU, the institutional repository of the WU Vienna University of Economics and Business, is provided by the University Library and the IT-Services. The aim is to enable open access to the scholarly output of the WU.

ePubWU, the institutional repository of the WU Vienna University of Economics and Business, is provided by the University Library and the IT-Services. The aim is to enable open access to the scholarly output of the WU. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-15

Source:

http://epub.wu.ac.at/1274/1/document.pdf

http://epub.wu.ac.at/1274/1/document.pdf Minimize

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text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Accounting for individual differences in BradleyTerry models by means of recursive partitioning

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The preference scaling of a group of subjects may not be homogeneous, but different groups of subjects with certain characteristics may show different preference scalings, each of which can be derived from paired comparisons by means of the Bradley-Terry model. Usually, either different models are fit in predefined subsets of the sample, or the ...

The preference scaling of a group of subjects may not be homogeneous, but different groups of subjects with certain characteristics may show different preference scalings, each of which can be derived from paired comparisons by means of the Bradley-Terry model. Usually, either different models are fit in predefined subsets of the sample, or the effects of subject covariates are explicitly specified in a parametric model. In both cases, categorical covariates can be employed directly to distinguish between the different groups, while numeric covariates are typically discretized prior to modeling. Here, a semi-parametric approach for recursive partitioning of Bradley-Terry models is introduced as a means for identifying groups of subjects with homogeneous preference scalings in a data-driven way. In this approach, the covariates that – in main effects or in-teractions – distinguish between groups of subjects with different preference orderings, are detected automatically from the set of candidate covariates. One main advantage of this approach is that sensible partitions in numeric covariates are also detected automatically. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-12

Source:

http://epub.ub.uni-muenchen.de/10588/1/BTL2mob.pdf

http://epub.ub.uni-muenchen.de/10588/1/BTL2mob.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Bradley-Terry model ; subject covariates ; recursive partitioning

Bradley-Terry model ; subject covariates ; recursive partitioning Minimize

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310 Collections of general statistics *(computed)*

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Title:

Detecting Invariance in

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presenting: Basil Abou El-KombozScope of this talk groups of subjects may ◮ show higher preferences for certain stimuli ◮ have an easier time answering certain test items

presenting: Basil Abou El-KombozScope of this talk groups of subjects may ◮ show higher preferences for certain stimuli ◮ have an easier time answering certain test items Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-11-06

Source:

http://web.warwick.ac.uk/statsdept/user-2011/TalkSlides/Contributed/17Aug_1705_FocusV_3-Psychometrics_2-ElKomboz.pdf

http://web.warwick.ac.uk/statsdept/user-2011/TalkSlides/Contributed/17Aug_1705_FocusV_3-Psychometrics_2-ElKomboz.pdf Minimize

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text

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en

Subjects:

Strobl et al

Strobl et al Minimize

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