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Mathematical, Statistical and Computing Psychology - RSS Feedhttp://psychsource.bps.org.ukAn improved stochastic <fc xmlns="http://www.wiley.com/namespaces/wiley">EM</fc> algorithm for large‐scale full‐information item factor analysis
http://psychsource.bps.org.uk/details/journalArticle/11117002/An-improved-stochastic-EM-algorithm-for-largescale-fullinformation-item-factor-a.html
In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large‐scale full‐information item factor analysis. Innovations have been made on its implementation, including an
adaptive‐rejection‐based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization
in the M step, and diagnostic procedures for determining the burn‐in size...]]>2018-12-03T00:00:00ZAsymptotic bias of normal‐distribution‐based maximum likelihood estimates of moderation effects with data missing at random
http://psychsource.bps.org.uk/details/journalArticle/11115135/Asymptotic-bias-of-normaldistributionbased-maximum-likelihood-estimates-of-moder.html
Moderation analysis is useful for addressing interesting research questions in social sciences and behavioural research. In
practice, moderated multiple regression (MMR) models have been most widely used. However, missing data pose a challenge, mainly
because the interaction term is a product of two or more variables and thus is a non‐linear function of the involved variables.
Normal‐distribution‐based maximum likelihood (NML) has been...]]>2018-11-25T00:00:00ZRobust regression: Testing global hypotheses about the slopes when there is multicollinearity or heteroscedasticity
http://psychsource.bps.org.uk/details/journalArticle/11114979/Robust-regression-Testing-global-hypotheses-about-the-slopes-when-there-is-multi.html
A well‐known concern regarding the usual linear regression model is multicollinearity. As the strength of the association
among the independent variables increases, the squared standard error of regression estimators tends to increase, which can
seriously impact power. This paper examines heteroscedastic methods for dealing with this issue when testing the hypothesis
that all of the slope parameters are equal to zero via a robust ridge...]]>2018-11-23T00:00:00ZEffect size, statistical power, and sample size for assessing interactions between categorical and continuous variables
http://psychsource.bps.org.uk/details/journalArticle/11114978/Effect-size-statistical-power-and-sample-size-for-assessing-interactions-between.html
The reporting and interpretation of effect size estimates are widely advocated in many academic journals of psychology and
related disciplines. However, such concern has not been adequately addressed for analyses involving interactions between categorical
and continuous variables. For the purpose of improving current practice, this article presents fundamental features and theoretical
developments for the variance of standardized slopes...]]>2018-11-23T00:00:00ZOptimal designs for the generalized partial credit model
http://psychsource.bps.org.uk/details/journalArticle/11113918/Optimal-designs-for-the-generalized-partial-credit-model.html
Analysing ordinal data is becoming increasingly important in psychology, especially in the context of item response theory.
The generalized partial credit model (GPCM) is probably the most widely used ordinal model and has found application in many
large‐scale educational assessment studies such as PISA. In the present paper, optimal test designs are investigated for estimating
persons’ abilities with the GPCM for calibrated tests when...]]>2018-11-19T00:00:00ZA caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters
http://psychsource.bps.org.uk/details/journalArticle/11113919/A-caveat-on-the-SavageDickey-density-ratio-The-case-of-computing-Bayes-factors-f.html
The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more
parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or
more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct
Bayes factor if the prior distribution of the nuisance parameters under...]]>2018-11-19T00:00:00ZBayesian evaluation of informative hypotheses for multiple populations
http://psychsource.bps.org.uk/details/journalArticle/11107985/Bayesian-evaluation-of-informative-hypotheses-for-multiple-populations.html
The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models.
For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF).
Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations.
If samples of unequal size are...]]>2018-10-21T00:00:00ZWhen does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration
http://psychsource.bps.org.uk/details/journalArticle/11107984/When-does-measurement-error-in-covariates-impact-causal-effect-estimates-Analyti.html
The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if
all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased
ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment. But is
it always necessary to use latent covariates? How well do analysis of...]]>2018-10-21T00:00:00ZObituary
http://psychsource.bps.org.uk/details/journalArticle/11104115/Obituary.html
]]>2018-10-03T00:00:00ZIssue Information
http://psychsource.bps.org.uk/details/journalArticle/11104114/Issue-Information.html
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