Targeted memory reactivation during sleep improves emotional memory modulation following imagery rescripting.

Targeted Memory Reactivation (TMR) during sleep benefits memory integration and consolidation. In this pre-registered study, we investigated the effects of TMR applied during non-rapid eye movement (NREM) sleep following modulation and updating of aversive autobiographical memories using imagery rescripting (ImR). During 2-5 nights postImR, 80 healthy participants were repeatedly presented with either idiosyncratic words from an ImR updated memory during sleep (experimental group) or with no or neutral words (control groups) using a wearable EEG device (Mobile Health Systems Lab-Sleepband, MHSL-SB) [1] implementing a close-loop cueing procedure.

Estradiol and progesterone from pregnancy to postpartum: a longitudinal latent class analysis.

Introduction: During the peripartum, women undergo significant hormonal changes that are crucial for fetal development and a healthy pregnancy and postpartum period for mother and infant. Although several studies have determined healthy norm ranges of estradiol and progesterone, there are discrepancies among the reports, rendering it unclear which hormone levels are linked to adverse health outcomes. To account for the impact of sex steroid patterns on health outcomes in mothers and children, a longitudinal assessment of different parameters is needed.

Sample size planning for complex study designs: A tutorial for the mlpwr package.

A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling.

Interpretable machine learning for psychological research: Opportunities and pitfalls.

In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable.

Changes in attitudes towards smoking during smoking cessation courses for Turkish- and Albanian-speaking migrants in Switzerland and its association with smoking behavior: A latent change score approach.

Introduction: Migrant populations usually report higher smoking rates than locals. At the same time, people with a migration background have little or no access to regular smoking cessation treatment. In the last two decades, regular smoking cessation courses were adapted to reach out to Turkish- and Albanian-speaking migrants living in Switzerland.

A New Stopping Criterion for Rasch Trees Based on the Mantel-Haenszel Effect Size Measure for Differential Item Functioning.

To detect differential item functioning (DIF), Rasch trees search for optimal splitpoints in covariates and identify subgroups of respondents in a data-driven way. To determine whether and in which covariate a split should be performed, Rasch trees use statistical significance tests. Consequently, Rasch trees are more likely to label small DIF effects as significant in larger samples.

Different approaches to modeling response styles in divide-by-total item response theory models (part 2): Applications and novel extensions.

Many approaches in the item response theory (IRT) literature have incorporated response styles to control for potential biases. However, the specific assumptions about response styles are often not made explicit. Having integrated different IRT modeling variants into a superordinate framework, we highlighted assumptions and restrictions of the models (Henninger & Meiser, 2020).

Different approaches to modeling response styles in divide-by-total item response theory models (part 1): A model integration.

A large variety of item response theory (IRT) modeling approaches aim at measuring and correcting for response styles in rating data. Here, we integrate response style models of the divide-by-total model family into one superordinate framework that parameterizes response styles as person-specific shifts in threshold parameters. This superordinate framework allows us to structure and compare existing approaches to modeling response styles and therewith makes model-implied restrictions explicit.

Different Styles, Different Times: How Response Times Can Inform Our Knowledge About the Response Process in Rating Scale Measurement.

When respondents use different ways to answer rating scale items, they employ so-called response styles that can bias inferences drawn from measurement. To describe the influence of such response styles on the response process, we investigated relations between extreme, acquiescent, and mid response style and response times in three studies using multilevel modeling. On the response level, agreement and midpoint, but not extreme responses were slower.

IRTree models with ordinal and multidimensional decision nodes for response styles and trait-based rating responses.

IRTree models decompose observed rating responses into sequences of theory-based decision nodes, and they provide a flexible framework for analysing trait-related judgements and response styles. However, most previous applications of IRTree models have been limited to binary decision nodes that reflect qualitatively distinct and unidimensional judgement processes. The present research extends the family of IRTree models for the analysis of response styles to ordinal judgement processes for polytomous decisions and to multidimensional parametrizations of decision nodes.

A new task format for investigating information search and organization in multiattribute decisions.

In research on multiattribute decisions, information is typically preorganized in a well-structured manner (e.g., in attributes-by-options matrices).