Beta-binomial meta-analysis of individual differences based on sample means and standard deviations: Studying reliability of sum scores of binary items.

Individual differences are studied with a multitude of test instruments. Meta-analysis of tests is useful to understand whether individual differences in certain populations can be detected with the help of a class of tests. A method for the quantitative meta-analytical evaluation of test instruments with dichotomous items is introduced. The method assumes beta-binomially distributed test scores, an assumption that has been demonstrated to be plausible in many settings. With this assumption, the method only requires sample means and standard deviations of sum scores (or equivalently means and standard deviations of percent-correct scores), in contrast to methods that use estimates of reliability for a similar purpose. Two parameters are estimated for each sample: mean difficulty and an overdispersion parameter which can be interpreted as the test's ability to detect individual differences. The proposed bivariate meta-analytical approach (random or fixed effects) pools the two parameters simultaneously and allows to perform meta-regression. The bivariate pooling yields a between-sample correlation of mean difficulty parameters and overdispersion parameters. As a side product, reliability estimates are obtained which can be employed to disattenuate correlation coefficients for insufficient reliability when no other estimates are available. A worked example illustrates the method and R code is provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Crohn's Disease as a Possible Risk Factor for Failed Healing in Ileocolic Anastomoses.

Anastomotic leakage (AL) after colorectal resections is a serious complication in abdominal surgery. Especially in patients with Crohn's disease (CD), devastating courses are observed. Various risk factors for the failure of anastomotic healing have been identified; however, whether CD itself is independently associated with anastomotic complications still remains to be validated. A retrospective analysis of a single-institution inflammatory bowel disease (IBD) database was conducted. Only patients with elective surgery and ileocolic anastomoses were included. Patients with emergency surgery, more than one anastomosis, or protective ileostomies were excluded. For the investigation of the effect of CD on AL 141, patients with CD-type L1, B1-3 were compared to 141 patients with ileocolic anastomoses for other indications. Univariate statistics and multivariate analysis with logistic regression and backward stepwise elimination were performed. CD patients had a non-significant higher percentage of AL compared to non-IBD patients (12% vs. 5%, p = 0.053); although, the two samples differed in terms of age, body mass index (BMI), Charlson comorbidity index (CCI), and other clinical variables. However, Akaike information criterion (AIC)-based stepwise logistic regression identified CD as a factor for impaired anastomotic healing (final model: p = 0.027, OR: 17.043, CI: 1.703-257.992). Additionally, a CCI ≥ 2 (p = 0.010) and abscesses (p = 0.038) increased the disease risk. The alternative point estimate for CD as a risk factor for AL based on propensity score weighting also resulted in an increased risk, albeit lower (p = 0.005, OR 7.36, CI 1.82-29.71). CD might bear a disease-specific risk for the impaired healing of ileocolic anastomoses. CD patients are prone to postoperative complications, even in absence of other risk factors, and might benefit from treatment in dedicated centers.

Interactions of scores derived from two groups of variables: Alternating lasso regularization avoids overfitting and finds interpretable scores.

Regression models with interaction terms are common models for moderating relationships. When effects of several predictors from one group-for example, genetic variables-are potentially moderated by several predictors from another-for example, environmental variables-many interaction terms result. This complicates model interpretation, especially when coefficient signs point in different directions. By first forming a score for each group of predictors, the interaction model's dimension is severely reduced. The hierarchical score model is an elegant one-step approach: Score weights and regression model coefficients are estimated simultaneously by an alternating optimization (AO) algorithm. Especially in high dimensional settings, scores remain an effective technique to reduce interaction model dimension, and we propose regularization to ensure sparsity and interpretability of the score weights. A nontrivial extension of the original AO algorithm is presented, which adds a lasso penalty, resulting in the alternating lasso optimization algorithm (ALOA). The hierarchical score model with ALOA is an interpretable statistical learning technique for moderation in potentially high dimensional applications, and encompasses generalized linear models for the main interaction model. In addition to the lasso regularization, a screening procedure called regularization and residualization (RR) is proposed to avoid spurious interactions. ALOA tuning parameter choice and the RR screening procedure are investigated by simulations, and two illustrative applications to depression risk are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Rotate and Project: Measurement of the Intended Concept with Unidimensional Item Response Theory from Multidimensional Ordinal Items.

What to do when item response data are multidimensional but a unidimensional model is preferred in terms of statistical simplicity and ease of interpretability? The projection method for the compensatory logistic multidimensional item response model for dichotomous data leads to a two parameter logistic model with local item dependence. Despite the local item dependence, the model is unidimensional for many practical purposes. Here, Ip's projection method is generalized to the case of the graded response model for polytomous variables, extending the applicability of the method to Likert-type response formats. A secondary aim of the paper is the study of rotation techniques intended for use prior to projection. In contrast to rotations aiming at a simple structure of factor loadings, the proposed techniques increase the variance explained before or after projection, facilitate the interpretation of the projected dimension by variants of target rotations or a mix of both. The method is illustrated with an application to the Highly Sensitive Person Scale and R code is provided.

Looking at DIF From a New Perspective: A Structure-Based Approach Acknowledging Inherent Indefinability.

Differential item functioning (DIF), although highly relevant for psychometric assessment in various fields of psychology, is mathematically not well-defined. Especially the impact, the difference between the means of the person parameters in the focal and the reference group, is a parameter that is not identified without further assumptions. Common DIF detection methods necessarily impose such assumptions, however, in most cases the specific constraints remain quite vague and are implicit in the mathematical algorithms. As an alternative, a structure-based approach is proposed that is independent of the impact and, therefore, not affected by the identification problem. The approach allows to (a) reveal all DIF-relevant information from the data, (b) define indices that quantify the amount of DIF in a test as a whole, and (c) perform item-level DIF analyses. For the last application, it is unavoidable to assume additional identification constraints. However, compared to other existing methods (which also rely on such constraints), the approach suggested here provides advantages as it is not only easier to perform but also much more transparent concerning its underlying assumptions.

Optimal and most exact confidence intervals for person parameters in item response theory models.

The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter θ is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given level of significance in many cases; and, therefore, the corresponding intervals are no longer confidence intervals in terms of the actual definition. In the present work, confidence intervals are defined more precisely by utilizing the relationship between confidence intervals and hypothesis testing. Two approaches to confidence interval construction are explored that are optimal with respect to criteria of smallness and consistency with the standard approach.