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Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background. Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction. Highlights of the new edition include: More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings. Greater emphasis on the use of R packages; readers can download the R code from the Routledge website. Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4). A new four-option data set from the IASQ (Instrumental Attitude toward Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6). Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).

Publication bias remains a controversial issue in psychological science. The tendency of psychological science to avoid publishing null results produces a situation that limits the replicability assumption of science, as replication cannot be meaningful without the potential acknowledgment of failed replications. We argue that the field often constructs arguments to block the publication and interpretation of null results and that null results may be further extinguished through questionable researcher practices. Given that science is dependent on the process of falsification, we argue that these problems reduce psychological science's capability to have a proper mechanism for theory falsification, thus resulting in the promulgation of numerous \"undead\" theories that are ideologically popular but have little basis in fact.

Findings of studies on the unique effects of reasoning and working memory regarding complex problem solving are inconsistent. To find out if these inconsistencies are due to a lack of symmetry between the studies, [the authors] reconsidered the findings of three published studies on this issue, which resulted in conflicting conclusions regarding the inter-relations between reasoning, working memory, and complex problem solving. This was achieved by analysing so far unpublished problem solving data from the study of Bühner, Krumm, Ziegler, and Plücken (2006) [...]. One of the three published studies indicated unique effects of working memory and reasoning on complex problem solving using aggregated scores, a second study found no unique contribution of working memory using only figural scores, and a third study reported a unique influence only for reasoning using only numerical scores. [The] data [of the authors] featured an evaluation of differences across content facets and levels of aggregation of the working memory scores. Path models showed that the results of the first study could not be replicated using content aggregated scores; the results of the second study could be replicated if only figural scores were used, and the results of the third study could be obtained by using only numerical scores. For verbal content, none of the published results could be replicated. This leads to the assumption that not only symmetry is an issue when correlating non-symmetrical data, but that content also has to be taken into account when comparing different studies on the same topic. (Orig.).

BACKGROUND: Suboptimal vitamin B status might affect cognitive performance in early childhood. We tested the hypothesis that short-term supplementation with folic acid and selected B vitamins improves cognitive function in healthy children in a population with relatively low folate status. METHODS: We screened 1,002 kindergarten children for suboptimal folate status by assessing the total urinary para-aminobenzoylglutamate excretion. Two hundred and fifty low ranking subjects were recruited into a double blind, randomized, controlled trial to receive daily a sachet containing 220 μg folic acid, 1.1 mg vitamin B₂, 0.73 mg B₆, 1.2 μg B₁₂ and 130 mg calcium, or calcium only for 3 months. Primary outcomes were changes in verbal IQ, short-term memory and processing speed between baseline and study end. Secondary outcomes were urinary markers of folate and vitamin B₁₂ status, acetyl-para-aminobenzoylglutamate and methylmalonic acid, respectively, and, in a subgroup of 120 participants, blood folate and plasma homocysteine. RESULTS: Pre- and post-intervention cognitive measurements were completed by 115 children in the intervention and 122 in the control group. Compared to control, median blood folate increased by about 50 % (P for difference, P < 0.0001). Homocysteine decreased by 1.1 μmol/L compared to baseline, no change was seen in the control group (P for difference P < 0.0001) and acetyl-para-aminobenzoylglutamate was 4 nmol/mmol higher compared to control at the end of the intervention (P < 0.0001). We found no relevant differences between the groups for the cognitive measures. CONCLUSION: Short-term improvement of folate and homocysteine status in healthy children does not appear to affect cognitive performance.

The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is known that the standard biometric model can be expected to misrepresent the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. We aimed to shed some light on this general issue, by undertaking three simulation studies. In each, we investigated the parameter recovery performance of five modes- Falconer’s coefficient and the SEM models, ACDE, ADE, ACE, and AE- when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was a set of population covariance matrices yielded under the four twin designs, monozygotic-reared together, monozygotic-reared apart, dizygotic-reared together, and dizygotic-reared apart; in study 2, the architecture was identical to that of study 1, but the informational input was monozygotic-reared together and dizygotic-reared together; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was the same as in study 1. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimationemployed. The results showed that the more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/

To provide researchers with a means of assessing the fit of the structural component of structural equation models, structural fit indices- modifications of the composite fit indices, RMSEA, SRMR, and CFI- have recently been developed. We investigated the performance of four of these structural fit indices- RMSEA-P, RMSEAs, SRMRs, and CFIs-, when paired with widely accepted cutoff values, in the service of detecting structural misspecification. In particular, by way of simulation study, for each of seven fit indices- 3 composite and 4 structural-, and the traditional chi-square test of perfect composite fit, we estimated the following rates: a) Type I error rate (i.e., the probability of (incorrect) rejection of a correctly specified structural component), under each of four degrees of misspecification in the measurement component; and b) Power (i.e., the probability of (correct) rejection of an incorrectly specified structural model), under each condition formed of the pairing of one of three degrees of structural misspecification with one of four degrees of measurement component misspecification. In addition to sample size, the impacts of two model features, incidental to model misspecification- number of manifest variables per latent variable and magnitude of factor loading- were investigated. The results suggested that, although the structural fit indices performed relatively better than the composite fit indices, none of the goodness-of-fit index with a fixed cutoff value pairings was capable of delivering an entirely satisfactory Type I error rate/Power balance, [RMSEAs, .05] failing entirely in this regard. Of the remaining pairings; a) RMSEA-P and CFIs suffered from a severely inflated Type I error rate; b) despite the fact that they were designed to pick up on structural features of candidate models, all pairings- and especially, RMSEA-P and CFIs-manifested sensitivities to model features, incidental to structural misspecification; and c) although, in the main, behaving in a sensible fashion, SRMRs was only sensitive to structural misspecification when it occurred at a relatively high degree.

Statistical hypothesis testing is commonly used to assess the fit of data to the Rasch models. Such tests of fit are problematical as they are sensitive to sample size and the number of parameters in the model. Furthermore, the null distributions of the statistical test may deviate from a distribution with a known parametric shape. Accordingly, in this study, a number of descriptive fit statistics for the Rasch model, based on the tenets of Andersen's LR test and Fischer-Scheiblechner's S test, are suggested and compared using simulation studies. The results showed that some of the measures were sensitive to sample size while some were insensitive to model violations. Andersen's [chi square]/df measure was found to be the best measure of fit.