An a priori solution to the replication crisis

Possibly, the replication crisis constitutes the most important problem in psychology. It calls into question whether psychology is a science. Existing conceptualizations of replicability depend on effect sizes; the larger the population effect size, the greater the probability of replication. This is problematic and contributes to the replication crisis. A different conceptualization, not dependent on population effect sizes, is desirable. The proposed solution features the closeness of sample means to their corresponding population means, in both the original and replication experiments. If the researcher has specified the sampling precision desired, it is possible to calculate the probability of replication, prior to data collection, and without dependence on the population effect size or expected population effect size. In addition, it is not necessary to know population means or standard deviations, nor sample means or standard deviations, to employ the proposed a priori way of thinking about replicability.     

The assessment of emotional clarity via response times to emotion items: shedding light on the response process and its relation to emotion regulation strategies

Researchers have begun to use response times (RTs) to emotion items as an indirect measure of emotional clarity. Our first aim was to scrutinise the properties of this RT measure in more detail than previously. To be able to provide recommendations as to whether (and how) emotional intensity – as a possible confound – should be controlled for, we investigated the specific form of the relation between emotional intensity and RTs to emotion items. In particular, we assumed an inverted U-shaped relation at the item level. Moreover, we analysed the RT measure’s convergent validity with respect to individuals’ confidence in their emotion ratings. As a second aim, we compared the predictive validity of emotional clarity measures (RT measure, self-report) with respect to daily emotion regulation. The results of three experience sampling studies showed that the association between emotional intensity and RT followed an inverted U shape. RT was in part related to confidence. Emotional clarity measures were unrelated to reappraisal. There was some evidence that lower emotional clarity was related to a greater use of suppression. The findings highlight that emotional intensity and squared emotional intensity should be controlled for when using the RT measure of emotional clarity in future research.     

Lax monitoring versus logical intuition: The determinants of confidence in conjunction fallacy

The general assumption that people fail to notice discrepancy between their answer and the normative answer in the conjunction fallacy task has been challenged by the theory of Logical Intuition. This theory suggests that people can detect the conflict between the heuristic and normative answers even if they do not always manage to inhibit their intuitive choice. This theory gained support from the finding that people report lower levels of confidence in their choice after they commit the conjunction fallacy compared to when their answer is not in conflict with logic. In four experiments we asked the participants to give probability estimations to the options of the conflict and no-conflict versions of the tasks in the original set-up of the experiment or in a three-option design. We found that participants perceive probabilities for the options of the conflict version less similar than for the no-conflict version. As people are less confident when choosing between more similar options, this similarity difference is proposed to serve as a mediator in the task in a way that the conflict and no-conflict conditions have their effects on confidence ratings through manipulating the similarity of the answer options.     

Developing pre-service teachers’ confidence: real-time coaching in teacher education

Teacher quality continues to be of major concern in the Western world, and identifying the most effective approach to teacher training remains a contested area. It has been argued that teachers resist change because they lack motivation, have inadequate knowledge and expertise to modify their practices, and are reluctant to take risks for fear of having their confidence damaged. While remaining skeptical of such a deficit view, our interest is in pedagogic approaches which seek to better prepare teachers for teaching in contemporary society and, thus, to enhance their ‘effectiveness.’ This article discusses the use of a Real-Time Coaching (RTC) model designed to enhance pre-service teachers’ practical skills for contemporary classroom teaching. The model focuses upon pre-service teachers micro-teaching while simultaneously gaining feedback via a headset in real time; this is then combined with multiple collaborative feedback cycles within a learning community in order to foster reflective practice. To collect information about participants’ experiences with the RTC process, two rounds of semi-structured interviews were conducted with participants. Our findings indicate the RTC model has the capacity to foster a sense of confidence and ownership of learning by developing practical skills alongside affective attributes such as resilience, efficacy, and a disposition toward continual improvement.     

Head and Trunk Control While Walking in Older Adults with Diabetes: Effects of Balance Confidence

Investigations of gait in older adults with diabetes mellitus (DM) have been primarily focused on lower limb biomechanical parameters. Yet, the upper body accounts for two thirds of the body's mass, and head and trunk control are critical for balance. The authors examined head and trunk control during self-selected comfortable, fast, and dual-task walking and the relationship between balance confidence and potential head-trunk stiffening strategies in older adults with DM without diagnosed diabetic peripheral neuropathy (DPN). Twelve older adults with DM without diagnosed DPN (DM group) and 12 without DM (no-DM group) were recruited. Walking speed, peak-to-peak head and trunk roll displacement, head and trunk roll velocity, and head-trunk correlation were measured while walking at a self-selected comfortable or fastest possible speed with or without a secondary cognitive task. The Activities-specific Balance Confidence scale measured balance confidence. Subtle group differences in axial segmental control (lower trunk roll velocity; higher head-trunk correlation) were apparent in older adults with DM even in the absence of DPN. Balance confidence was 19% lower in the DM group than in the no-DM group, and partially explained (34%) the group difference in head-trunk stiffening. These results emphasize the need for proactive monitoring of postural control and balance confidence before the onset of DPN.     

Asymptotic confidence intervals for the Pearson correlation via skewness and kurtosis

When bivariate normality is violated, the default confidence interval of the Pearson correlation can be inaccurate. Two new methods were developed based on the asymptotic sampling distribution of Fisher's z′ under the general case where bivariate normality need not be assumed. In Monte Carlo simulations, the most successful of these methods relied on the (Vale & Maurelli, 1983, Psychometrika, 48, 465) family to approximate a distribution via the marginal skewness and kurtosis of the sample data. In Simulation 1, this method provided more accurate confidence intervals of the correlation in non-normal data, at least as compared to no adjustment of the Fisher z′ interval, or to adjustment via the sample joint moments. In Simulation 2, this approximate distribution method performed favourably relative to common non-parametric bootstrap methods, but its performance was mixed relative to an observed imposed bootstrap and two other robust methods (PM1 and HC4). No method was completely satisfactory. An advantage of the approximate distribution method, though, is that it can be implemented even without access to raw data if sample skewness and kurtosis are reported, making the method particularly useful for meta-analysis. Supporting information includes R code.     

Bias-corrected estimation of the Rudas–Clogg–Lindsay mixture index of fit

Rudas, Clogg, and Lindsay (1994, J. R Stat Soc. Ser. B, 56, 623) introduced the so-called mixture index of fit, also known as pi-star (π*), for quantifying the goodness of fit of a model. It is the lowest proportion of ‘contamination’ which, if removed from the population or from the sample, makes the fit of the model perfect. The mixture index of fit has been widely used in psychometric studies. We show that the asymptotic confidence limits proposed by Rudas et al. (1994, J. R Stat Soc. Ser. B, 56, 623) as well as the jackknife confidence interval by Dayton ( 2003 , Br. J. Math. Stat. Psychol., 56, 1) perform poorly, and propose a new bias-corrected point estimate, a bootstrap test and confidence limits for pi-star. The proposed confidence limits have coverage probability much closer to the nominal level than the other methods do. We illustrate the usefulness of the proposed method in practice by presenting some practical applications to log-linear models for contingency tables.     

Why cannot reasons change your moral decisions? Because they are not persuasive enough: A comment on Stanley et al. (2018)

Stanley et al. (2018) found that the consideration of reasons rarely induced people to change their moral decisions. We challenged this article by assuming what caused such a null or weak effect was that the persuasiveness of reasons provided to oppose the initial decisions was not strong enough. To verify our assumption, this study used Stanley et al.’s (2018) experimental paradigm and manipulated the levels of persuasiveness of reasons. The results revealed (1) that not only strong opposing reasons but also weak affirming reasons could induce changes in moral decision-making and increase decision confidence after altering the decisions; (2) that people with a weak decision confidence tended to change their initial decisions after evaluation of reasons; and (3) that people who maintained their decisions after considering weak opposing reasons enhanced rather than reduced their decision confidence. Overall, these findings demonstrated that moral decision change was a composite outcome of the interaction among reason type, reason persuasiveness and initial decision confidence and that low-quality argumentation had a boomerang effect on moral persuasion. This study re-lifted the role of rational reasoning in moral decision-making and revising, thus posing important amendments to Stanley et al.’s (2018) findings.     

Confidence interval-based sample size determination formulas and some mathematical properties for hierarchical data

The use of hierarchical data (also called multilevel data or clustered data) is common in behavioural and psychological research when data of lower-level units (e.g., students, clients, repeated measures) are nested within clusters or higher-level units (e.g., classes, hospitals, individuals). Over the past 25 years we have seen great advances in methods for computing the sample sizes needed to obtain the desired statistical properties for such data in experimental evaluations. The present research provides closed-form and iterative formulas for sample size determination that can be used to ensure the desired width of confidence intervals for hierarchical data. Formulas are provided for a four-level hierarchical linear model that assumes slope variances and inclusion of covariates under both balanced and unbalanced designs. In addition, we address several mathematical properties relating to sample size determination for hierarchical data via the standard errors of experimental effect estimates. These include the relative impact of several indices (e.g., random intercept or slope variance at each level) on standard errors, asymptotic standard errors, minimum required values at the highest level, and generalized expressions of standard errors for designs with any-level randomization under any number of levels. In particular, information on the minimum required values will help researchers to minimize the risk of conducting experiments that are statistically unlikely to show the presence of an experimental effect.     

情景预见对跨期决策的影响机制

为了探究情景预见对跨期决策的影响机制, 研究采用延迟折扣任务范式, 检验了在情景预见对跨期决策的影响中延迟时间知觉起到的中介作用。两个实验分别操纵了情景预见中未来事件与自我有关的信息和未来事件的情绪特征, 结果均发现情景预见通过改变个体对延迟等待时间的时距知觉影响了跨期决策。想象与自己、与母亲有关的未来事件, 想象积极、中性情绪效价的未来事件都使得被试将延迟等待时间知觉为较短的时间段, 更倾向于选择延迟奖励。想象消极情绪效价的未来事件使得被试将延迟等待时间知觉为较长的时间段, 更加偏好即时奖励。本研究有助于增进人们对情景预见影响跨期决策现象的理解。