The British Journal of Mathematical and Statistical Psychology 
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This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.Correction to 'a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models'
Br J Math Stat Psychol. 2023 Oct 25. doi: 10.1111/bmsp.12325. Online ahead of print.NO ABSTRACTPMID:37881106 | DOI:10.1111/bmsp.12325 (Source: The British Journal of Mathematical and Statistical Psychology)
Source: The British Journal of Mathematical and Statistical Psychology - October 26, 2023 Category: Statistics Source Type: research
Correction to 'a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models'
Br J Math Stat Psychol. 2023 Oct 25. doi: 10.1111/bmsp.12325. Online ahead of print.NO ABSTRACTPMID:37881106 | DOI:10.1111/bmsp.12325 (Source: The British Journal of Mathematical and Statistical Psychology)
Source: The British Journal of Mathematical and Statistical Psychology - October 26, 2023 Category: Statistics Source Type: research
Correction to 'a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models'
Br J Math Stat Psychol. 2023 Oct 25. doi: 10.1111/bmsp.12325. Online ahead of print.NO ABSTRACTPMID:37881106 | DOI:10.1111/bmsp.12325 (Source: The British Journal of Mathematical and Statistical Psychology)
Source: The British Journal of Mathematical and Statistical Psychology - October 26, 2023 Category: Statistics Source Type: research
Correction to 'a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models'
Br J Math Stat Psychol. 2023 Oct 25. doi: 10.1111/bmsp.12325. Online ahead of print.NO ABSTRACTPMID:37881106 | DOI:10.1111/bmsp.12325 (Source: The British Journal of Mathematical and Statistical Psychology)
Source: The British Journal of Mathematical and Statistical Psychology - October 26, 2023 Category: Statistics Source Type: research
Correction to 'a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models'
Br J Math Stat Psychol. 2023 Oct 25. doi: 10.1111/bmsp.12325. Online ahead of print.NO ABSTRACTPMID:37881106 | DOI:10.1111/bmsp.12325 (Source: The British Journal of Mathematical and Statistical Psychology)
Source: The British Journal of Mathematical and Statistical Psychology - October 26, 2023 Category: Statistics Source Type: research
Correction to 'a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models'
Br J Math Stat Psychol. 2023 Oct 25. doi: 10.1111/bmsp.12325. Online ahead of print.NO ABSTRACTPMID:37881106 | DOI:10.1111/bmsp.12325 (Source: The British Journal of Mathematical and Statistical Psychology)
Source: The British Journal of Mathematical and Statistical Psychology - October 26, 2023 Category: Statistics Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data
We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter es...
Source: The British Journal of Mathematical and Statistical Psychology - October 16, 2023 Category: Statistics Authors: David Jendryczko Fridtjof W Nussbeck Source Type: research
Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?
Br J Math Stat Psychol. 2023 Nov;76(3):646-678. doi: 10.1111/bmsp.12293. Epub 2022 Dec 2.ABSTRACTObservational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Cont...
Source: The British Journal of Mathematical and Statistical Psychology - October 3, 2023 Category: Statistics Authors: Ke-Hai Yuan Yongfei Fang Source Type: research
