Parameter recovery and model selection in mixed Rasch models

2011 ◽  
Vol 65 (2) ◽  
pp. 251-262 ◽  
Author(s):  
David Preinerstorfer ◽  
Anton K. Formann
Keyword(s):  
Model Selection ◽  
Rasch Models ◽  
Parameter Recovery

scholarly journals Ignoring a Multilevel Structure in Mixture Item Response Models: Impact on Parameter Recovery and Model Selection

2017 ◽  
Vol 42 (2) ◽  
pp. 136-154 ◽  
Author(s):  
Woo-yeol Lee ◽  
Sun-Joo Cho ◽  
Sonya K. Sterba
Keyword(s):  
Model Selection ◽  
Item Response ◽  
Cluster Size ◽  
Response Model ◽  
Item Response Model ◽  
Number Of Clusters ◽  
Parameter Recovery ◽  
Single Level ◽  
Multilevel Structure ◽  
Level Data

The current study investigated the consequences of ignoring a multilevel structure for a mixture item response model to show when a multilevel mixture item response model is needed. Study 1 focused on examining the consequence of ignoring dependency for within-level latent classes. Simulation conditions that may affect model selection and parameter recovery in the context of a multilevel data structure were manipulated: class-specific ICC, cluster size, and number of clusters. The accuracy of model selection (based on information criteria) and quality of parameter recovery were used to evaluate the impact of ignoring a multilevel structure. Simulation results indicated that, for the range of class-specific ICCs examined here (.1 to .3), mixture item response models which ignored a higher level nesting structure resulted in less accurate estimates and standard errors ( SEs) of item discrimination parameters when the number of clusters was larger than 24 and the cluster size was larger than six. Class-varying ICCs can have compensatory effects on bias. Also, the results suggested that a mixture item response model which ignored multilevel structure was not selected over the multilevel mixture item response model based on Bayesian information criterion (BIC) if the number of clusters and cluster size was at least 50, respectively. In Study 2, the consequences of unnecessarily fitting a multilevel mixture item response model to single-level data were examined. Reassuringly, in the context of single-level data, a multilevel mixture item response model was not selected by BIC, and its use would not distort the within-level item parameter estimates or SEs when the cluster size was at least 20. Based on these findings, it is concluded that, for class-specific ICC conditions examined here, a multilevel mixture item response model is recommended over a single-level item response model for a clustered dataset having cluster size [Formula: see text] and the number of clusters [Formula: see text].

The Multiple Faces of Risk-Taking

2016 ◽  
Vol 32 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Florian Schmitz ◽  
Karsten Manske ◽  
Franzis Preckel ◽  
Oliver Wilhelm
Keyword(s):  
Risk Taking ◽  
Basic Research ◽  
Formal Models ◽  
Experimental Paradigm ◽  
Nomological Network ◽  
Balloon Analogue Risk Task ◽  
Parameter Recovery ◽  
Task Specificity ◽  
Available Information ◽  
Criterion Variables

Abstract. The Balloon-Analogue Risk Task (BART; Lejuez et al., 2002 ) is one of the most popular behavioral tasks suggested to assess risk-taking in the laboratory. Previous research has shown that the conventionally computed score is predictive, but neglects available information in the data. We suggest a number of alternative scores that are motivated by theories of risk-taking and that exploit more of the available data. These scores can be grouped around (1) risk-taking, (2) task performance, (3) impulsive decision making, and (4) reinforcement sequence modulation. Their theoretical rationale is detailed and their validity is tested within the nomological network of risk-taking, deviance, and scholastic achievement. Two multivariate studies were conducted with youths (n = 435) and with adolescents/young adults (n = 316). Additionally, we tested formal models suggested for the BART that decompose observed behavior into a set of meaningful parameters. A simulation study with parameter recovery was conducted, and the data from the two studies were reanalyzed using the models. Most scores were reliable and differentially predictive of criterion variables and may be used in basic research. However, task specificity and the generally moderate validity do not warrant use of the experimental paradigm for diagnostic purposes.

Review of Rasch Models for Measurement.

1989 ◽  
Vol 34 (3) ◽  
pp. 297-297
Author(s):  
No authorship indicated
Keyword(s):  
Rasch Models

Right-Wing Authoritarians Aren't Very Funny: RWA, Personality, and Creative Humor Production

2020 ◽  
Author(s):  
Paul Silvia ◽  
Alexander P. Christensen ◽  
Katherine N. Cotter
Keyword(s):  
Young Adults ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Openness To Experience ◽  
Rasch Models ◽  
Right Wing ◽  
Right Wing Authoritarianism ◽  
Humor Appreciation ◽  
Negative Effect

Right-wing authoritarianism (RWA) has well-known links with humor appreciation, such as enjoying jokes that target deviant groups, but less is known about RWA and creative humor production—coming up with funny ideas oneself. A sample of 186 young adults completed a measure of RWA, the HEXACO-100, and 3 humor production tasks that involved writing funny cartoon captions, creating humorous definitions for quirky concepts, and completing joke stems with punchlines. The humor responses were scored by 8 raters and analyzed with many-facet Rasch models. Latent variable models found that RWA had a large, significant effect on humor production (β = -.47 [-.65, -.30], p < .001): responses created by people high in RWA were rated as much less funny. RWA’s negative effect on humor was smaller but still significant (β = -.25 [-.49, -.01], p = .044) after controlling for Openness to Experience (β = .39 [.20, .59], p < .001) and Conscientiousness (β = -.21 [-.41, -.02], p = .029). Taken together, the findings suggest that people high in RWA just aren’t very funny.

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