Journal•ISSN: 0007-1102
British Journal of Mathematical and Statistical Psychology
British Psychological Society
About: British Journal of Mathematical and Statistical Psychology is an academic journal published by British Psychological Society. The journal publishes majorly in the area(s): Sample size determination & Population. It has an ISSN identifier of 0007-1102. Over the lifetime, 1307 publications have been published receiving 52971 citations. The journal is also known as: The British journal of mathematical & statistical psychology.
Topics: Sample size determination, Population, Estimator, Item response theory, Type I and type II errors
Papers published on a yearly basis
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TL;DR: A tutorial is provided illustrating an approach to estimation of and inference about direct, indirect, and total effects in statistical mediation analysis with a multicategorical independent variable that reproduces the observed and adjusted group means while also generating effects having simple interpretations.
Abstract: Virtually all discussions and applications of statistical mediation analysis have been based on the condition that the independent variable is dichotomous or continuous, even though investigators frequently are interested in testing mediation hypotheses involving a multicategorical independent variable (such as two or more experimental conditions relative to a control group). We provide a tutorial illustrating an approach to estimation of and inference about direct, indirect, and total effects in statistical mediation analysis with a multicategorical independent variable. The approach is mathematically equivalent to analysis of (co)variance and reproduces the observed and adjusted group means while also generating effects having simple interpretations. Supplementary material available online includes extensions to this approach and Mplus, SPSS, and SAS code that implements it.
2,318 citations
TL;DR: Methods for obtaining tests of fit of structural models for covariance matrices and estimator standard errors which are asymptotically distribution free are derived.
Abstract: Methods for obtaining tests of fit of structural models for covariance matrices and estimator standard errors which are asymptotically distribution free are derived. Modifications to standard normal theory tests and standard errors which make them applicable to the wider class of elliptical distributions are provided. A random sampling experiment to investigate some of the proposed methods is described.
1,772 citations
TL;DR: In this paper, a Monte Carlo study is conducted where five prototypical cases of non-normal variables are generated and two normal theory estimators, ML and GLS, are compared to Browne's (1982) ADF estimator.
Abstract: This paper considers the problem of applying factor analysis to non-normal categorical variables. A Monte Carlo study is conducted where five prototypical cases of non-normal variables are generated. Two normal theory estimators, ML and GLS, are compared to Browne's (1982) ADF estimator. A categorical variable methodology (CVM) estimator of Muthen (1984) is also considered for the most severely skewed case. Results show that ML and GLS chi-square tests are quite robust but obtain too large values for variables that arc severely skewed and kurtotic. ADF, however, performs well.
Parameter estimate bias appears non-existent for all estimators. Results also show that ML and GLS estimated standard errors are biased downward. For ADF no such standard error bias was found. The CVM estimator appears to work well when applied to severely skewed variables that had been dichotomized. ML and GLS results for a kurtosis only case showed no distortion of chi-square or parameter estimates and only a slight downward bias in estimated standard errors. The results are compared to those of other related studies.
1,584 citations
TL;DR: A simple and efficient method of estimating points on the psychometric function, and thus of estimating absolute and difference limens, is described.
Abstract: A simple and efficient method of estimating points on the psychometric function, and thus of estimating absolute and difference limens, is described. An illustration of the method is given in which sensitivity to inter-aural time differences is measured.
1,204 citations
TL;DR: This paper explores the origin of these limitations, and introduces an alternative and more stable agreement coefficient referred to as the AC1 coefficient, and proposes new variance estimators for the multiple-rater generalized pi and AC1 statistics, whose validity does not depend upon the hypothesis of independence between raters.
Abstract: Pi (pi) and kappa (kappa) statistics are widely used in the areas of psychiatry and psychological testing to compute the extent of agreement between raters on nominally scaled data. It is a fact that these coefficients occasionally yield unexpected results in situations known as the paradoxes of kappa. This paper explores the origin of these limitations, and introduces an alternative and more stable agreement coefficient referred to as the AC1 coefficient. Also proposed are new variance estimators for the multiple-rater generalized pi and AC1 statistics, whose validity does not depend upon the hypothesis of independence between raters. This is an improvement over existing alternative variances, which depend on the independence assumption. A Monte-Carlo simulation study demonstrates the validity of these variance estimators for confidence interval construction, and confirms the value of AC1 as an improved alternative to existing inter-rater reliability statistics.
1,173 citations