Hypothesis Of Formation Research Articles

Informative hypothesis testing in the EffectLiteR framework: A tutorial

In this paper, we illustrate how the typical workflow in analyzing psychological data, including analysis of variance and null hypothesis significance testing, may fail to bridge the gap between research questions and statistical procedures. It fails, because it does not provide us with the quantities of interest, which are often average and conditional effects, and it is insufficient, because it does not take the expectations of the researcher about these quantities into account. Using a running example, we demonstrate that the EffectLiteR framework as well as informative hypothesis testing are more suitable to narrow the gap between research questions and statistical procedures. Furthermore, we provide two empirical data examples, one in the context of linear regression and one in the context of the generalized linear model, to further illustrate the use of informative hypothesis testing in the EffectLiteR framework.

Sexual revictimisation risk mitigation among survivors of sexual violence and trafficking: A multi-method feasibility study of a body-oriented treatment module

ABSTRACT Background The majority of sexual violence survivors become sexually revictimised, and migrant populations are at particular risk. Prior research indicates that limited body awareness, dissociation, and difficulties in communicating boundaries might be underlying mechanisms of sexual revictimisation. Mental healthcare disregarding sexual revictimisation may insufficiently address the population’s needs. Aims In the current study, a novel individually delivered body-oriented module was considered for its feasibility amongst migrant survivors of sexual violence and trafficking. Method Thirteen migrant sexual violence survivors engaged in the module in an outpatient mental health care setting in the Netherlands. A multi-method approach was applied to tap into patients’ and therapists’ perspectives on the feasibility of the module using pre-, during, and posttreatment questionnaires, evaluation forms, and (group) interviews. Results Patients and therapists reported treatment outcomes by the module’s aims, and treatment adherence was high. Bayesian informative hypothesis evaluation revealed that, for seven out of 12 patients (58.33%), body awareness, and self-efficacy in communicating boundaries increased, while body dissociation simultaneously decreased during treatment. Discussion Findings suggest that a body-oriented module for sexual revictimisation risk mitigation is feasible amongst migrant survivors. Given the small sample size and lack of a control group, the findings should be interpreted cautiously.

Unsupervised hypernymy directionality prediction using context terms

Hypernymy directionality prediction is an important task in Natural Language Processing (NLP) due to its significant usages in natural language understanding and generation. Many supervised and unsupervised methods have been proposed for this task. Supervised methods require labeled examples, which are not readily available for many domains; besides, supervised models for this task that are trained on data from one domain performs poorly on data in a different domain. Therefore, unsupervised methods that are universally applicable for all domains are preferred. Existing unsupervised methods for hypernymy directionality prediction are outdated and suffer from poor performance. Specifically, they do not leverage distributional pre-trained vectors from neural language models, which have shown to be very effective in diverse NLP tasks. In this paper, we present DECIDE, a simple yet effective unsupervised method for hypernymy directionality prediction that exploits neural pre-trained vectors of words in context. By utilizing the distributional informativeness hypothesis over the context vectors, DECIDE predicts the hypernym directionality between a pair of words with a high accuracy. Extensive experiments on seven datasets demonstrate that DECIDE outperforms or achieves comparable performance to existing unsupervised and supervised methods.

A tutorial on aggregating evidence from conceptual replication studies using the product Bayes factor.

The product Bayes factor (PBF) synthesizes evidence for an informative hypothesis across heterogeneous replication studies. It can be used when fixed- or random effects meta-analysis fall short. For example, when effect sizes are incomparable and cannot be pooled, or when studies diverge significantly in the populations, study designs, and measures used. PBF shines as a solution for small sample meta-analyses, where the number of between-study differences is often large relative to the number of studies, precluding the use of meta-regression to account for these differences. Users should be mindful of the fact that the PBF answers a qualitatively different research question than other evidence synthesis methods. For example, whereas fixed-effect meta-analysis estimates the size of a population effect, the PBF quantifies to what extent an informative hypothesis is supported in all included studies. This tutorial paper showcases the user-friendly PBF functionality within the bain R-package. This new implementation of an existing method was validated using a simulation study, available in an Online Supplement. Results showed that PBF had a high overall accuracy, due to greater sensitivity and lower specificity, compared to random-effects meta-analysis, individual participant data meta-analysis, and vote counting. Tutorials demonstrate applications of the method on meta-analytic and individual participant data. The example datasets, based on published research, are included in bain so readers can reproduce the examples and apply the code to their own data. The PBF is a promising method for synthesizing evidence for informative hypotheses across conceptual replications that are not suitable for conventional meta-analysis.

Do election results resolve economic uncertainty? Evidence from Indian election 2024

We examine the immediate short-term effects of the election results using event study analysis on 1546 NSE-listed firms and find significant adverse market reactions around the counting day. However, the market recovers with expectations of a stable government. Sectoral analysis showed varied recovery across sectors. Cross-sectional analysis indicates that larger firms and those with better fundamentals manage uncertainties better, while volatile and highly-priced stocks suffer more. The findings theoretically contribute to the semi-strong form of the efficient market and uncertain information hypotheses.

Hypotheses Generation through Team Science or Fishing Expedition

Modern biological science depends on hypotheses generation, which can best be obtained without pre-conceptions, and by collective teamwork and individual brilliance. Therefore, ‘open’ searches can lead to informative hypotheses generation that in turn can be framed, delved into and tested, ultimately leading to novel mechanistic insights and translation into informative diagnostics and therapeutics.

Corporate fraud and independent director's re‐appointment: Information hypothesis or favouritism hypothesis?

AbstractUsing the analytical framework of social identity theory, this paper explores how a special corporate governance arrangement in the Chinese capital market, i.e., independent directors' re‐appointment, affects corporate ethical behaviours. Using the bivariate probit model, we find that independent directors’ re‐appointment significantly increases corporate fraud propensity, indicating that the favouritism towards re‐appointed independent directors by firms generated from social identity plays a dominant role in the corporate governance outcome in a relationship‐based society. Our results remain consistent after using an exogenous shock to alleviate the endogenous problems. The policy implication of this paper is that the corporate arrangement of re‐appointed independent directors in the Chinese capital market may impair stakeholders’ benefit and weaken business ethics. Top‐level institutional design should be improved and consider thoroughly the effects of social identity on corporate governance outcomes.

The stock market reaction to political and economic changes: the Spanish case

As other European countries, Spain has experienced a turmoil in which new political parties emerged with force after the global financial crisis in 2008. In this context, we analyze whether the empirical implications of the opportunistic and partisan theories, as well as the Uncertain Information Hypothesis, are met in the Spanish stock market, considering the size and the industry of the companies. The horizon of our study takes into account the seven general elections, 24 regional elections and 4 European elections held from 2002 to 2019, period in which Spain is fully integrated in the economic and monetary union and under the macroeconomic imbalance procedure. Our results do not support the opportunistic and partisan theories, or the Uncertain Information Hypothesis. Our evidence suggests that the short-term negative market reaction to the general elections is linked to the uncertainty with a change in the political sign of the incumbent. Besides, it is not related to size or industry characteristics.

Reduced Thickness of the Retina in de novo Parkinson's Disease Shows A Distinct Pattern, Different from Glaucoma.

Parkinson's disease (PD) patients experience visual symptoms and retinal degeneration. Studies using optical coherence tomography (OCT) have shown reduced thickness of the retina in PD, also a key characteristic of glaucoma. To identify the presence and pattern of retinal changes in de novo, treatment-naive PD patients compared to healthy controls (HC) and early primary open angle glaucoma (POAG) patients. Macular OCT data (10×10 mm) were collected from HC, PD, and early POAG patients, at the University Medical Center Groningen. Bayesian informative hypotheses statistical analyses were carried out comparing HC, PD-, and POAG patients, within each retinal cell layer. In total 100 HC, 121 PD, and 78 POAG patients were included. We showed significant reduced thickness of the inner plexiform layer and retinal pigment epithelium in PD compared to HC. POAG patients presented with a significantly thinner retinal nerve fiber layer, ganglion cell layer, inner plexiform layer, outer plexiform layer, and outer photoreceptor and subretinal virtual space compared to PD. Only the outer segment layer and retinal pigment epithelium were significantly thinner in PD compared to POAG. De novo PD patients show reduced thickness of the retina compared to HC, especially of the inner plexiform layer, which differs significantly from POAG, showing a more extensive and widespread pattern of reduced thickness across layers. OCT is a useful tool to detect retinal changes in de novo PD, but its specificity versus other neurodegenerative disorders has to be established.

A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models

In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models. We discuss practical advantages of the Bayesian approach over frequentist methods and conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (a) incorporate prior knowledge and beliefs in analyses, (b) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (c) quantify the uncertainty of effect-size estimates, and (d) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, and data and code are available at https://osf.io/rh2sw/ . Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.

Testing informative hypotheses in factor analysis models using bayes factors.

Testing informative hypotheses in factor analysis models using bayes factors.

Cognitive-Behavioral Therapy for Complicated Grief Reactions: Treatment Protocol and Preliminary Findings From a Naturalistic Setting

In bereavement, some individuals develop complicated grief reactions (CGR), including symptoms of prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD), and depression. Symptoms of PGD often co-occur with other complicated grief reactions, e.g., PTSD and depression, pointing to the need for a transdiagnostic understanding of CGR to inform treatment. In this paper, a transdiagnostic cognitive-behavioral therapy for CGR called “CBTgrief” is explained, including its theoretical framework and treatment content. CBTgrief is based on previous cognitive-behavioral conceptualizations of PGD, PTSD, and depression and includes 12 sessions consisting of exposure, cognitive restructuring, and behavioral activation. Preliminary findings of CBTgrief are evaluated in terms of feasibility, acceptability, and treatment satisfaction in a sample of eight older bereaved individuals treated for different types of CGR in a naturalistic clinical setting. Additionally, theory-driven hypotheses about changes in CGR and theoretical proposed core maintaining processes are evaluated using Bayesian informative hypotheses testing. Preliminary findings, limitations, implications, and future directions are discussed.

Reference frames for learning analytics dashboards: The progress and social reference frame and occupational self‐efficacy

AbstractBackgroundThe potential of learning analytics dashboards in virtual reality simulation‐based training environments to influence occupational self‐efficacy via self‐reflection phase processes in the Chemical industry is still not fully understood. Learning analytics dashboards provide feedback on learner performance and offer points of comparison (i.e., comparison with one's own past performance or comparison with peer performance) to help learners make sense of their feedback.ObjectivesWe present a theoretical framework for describing learning analytics reference frames and investigate the impact of feedback delivered through dashboards with different reference frames on occupational self‐efficacy, while controlling for workplace self‐reflection.MethodsThis experimental study engaged 42 chemical operator employees, aged between 18 and 55 years, each with at least one year of experience. We utilised a two‐group design to ask two research question each with three competing hypotheses related to changes in occupational self‐efficacy, employing Bayesian informative hypothesis evaluation.Results and ConclusionsResults for the primary research question suggest that dashboards with progress reference frames do not elicit greater change to self‐efficacy than those with social reference frames, however, they may elicit equal change. Furthermore, dashboards with social reference frames may elicit greater change to self‐efficacy than those with progress reference frames. Exploratory results found that dashboards with progress reference frames may elicit greater positive directional change than those with social reference frames and that they may elicit equal directional change.These findings contribute to the understanding of self‐efficacy beliefs within the Chemical industry, with potential impacts on skill development. The research may inform the design of targeted interventions and training programs to influence self‐efficacy. From a practical perspective, this research suggests that careful consideration is needed when choosing reference frames in learning analytics dashboards due to their potential consequences on the formation of learner self‐efficacy.

An exploration of healthcare use in older people waiting for and receiving Australian community-based aged care services.

Home care packages (HCPs) facilitate older individuals to remain at home, with longer HCP wait times associated with increased mortality risk. We analyze healthcare cost data pre- and post-HCP access to inform hypotheses around the effects of healthcare use and mortality risk. Regression models were used to assess the impact of delayed HCP access on healthcare costs and to compare costs whilst waiting and in the 6- and 12 month periods post-HCP access for 16 629 older adults. Average wait time for a HCP was 89.7 days (SD = 125.6) during the study period. Wait-time length had no impact on any healthcare cost category or time period. However, total per day healthcare costs were higher in the 6 and 12 months post-receipt of a HCP (AU$61.5, AU$63, respectively) compared with those in the time waiting for a HCP (AU$48.1). Inpatient care accounted for a higher proportion of total healthcare costs post-HCP (AU$45.1, AU$46.3, respectively) compared with in the wait time (AU$30.6), whilst spending on medical services and pharmaceuticals reduced slightly in the 6 month (AU$7.1, AU$6.3) and 12 month (AU$7.2, AU$6.3) post-HCP periods compared with in the wait time (AU$7.9, AU$7.1). Increased spending post-HCP on inpatient care or non-health support afforded by HCPs may offer protective effects for mortality and risk of admission to aged care. Further research should explore the association between delayed access to inpatient care for geriatric syndromes and mortality to inform recommendations on extensions to residential care outreach services into the community to improve the timely identification of the need for inpatient care. Geriatr Gerontol Int 2023; 23: 899-905.

Mental threat rehearsal increases fear generalization

Background and objectivesFear generalization to harmless stimuli characterizes anxiety-related disorders, but much remains unknown about its determinants. Based on studies showing that mental imagery of threat can increase conditioned fear responding, we tested whether it also facilitates fear generalization, and whether threat inflation moderates this effect. MethodsIn a fear conditioning study, 120 participants first completed an acquisition phase, in which one of two pictures was followed by an aversive sound (human scream). Then, the sound was presented 11 times at an increasing (threat inflation) or constant volume (no threat inflation). Finally, a generalization stimulus was presented, and some participants were asked to imagine the last sound (threat rehearsal) and others were not (no threat rehearsal). ResultsBayesian informative hypotheses tests indicated that imagery-based threat rehearsal increased generalization of threat expectancy, and, combined with threat inflation, it also resulted in stronger generalized distress. Limitationsdue to the absence of a test phase, it is unclear whether our effects would transfer to other GSs and whether they would persist beyond the manipulation phase. ConclusionsMental imagery of threat may put individuals at risk for fear generalization. Future studies should examine whether modulating imagery may prevent clinical anxiety.

Bayesian evidence synthesis for informative hypotheses: An introduction.

To establish a theory one needs cleverly designed and well-executed studies with appropriate and correctly interpreted statistical analyses. Equally important, one also needs replications of such studies and a way to combine the results of several replications into an accumulated state of knowledge. An approach that provides an appropriate and powerful analysis for studies targeting prespecified theories is the use of Bayesian informative hypothesis testing. An additional advantage of the use of this Bayesian approach is that combining the results from multiple studies is straightforward. In this article, we discuss the behavior of Bayes factors in the context of evaluating informative hypotheses with multiple studies. By using simple models and (partly) analytical solutions, we introduce and evaluate Bayesian evidence synthesis (BES) and compare its results to Bayesian sequential updating. By doing so, we clarify how different replications or updating questions can be evaluated. In addition, we illustrate BES with two simulations, in which multiple studies are generated to resemble conceptual replications. The studies in these simulations are too heterogeneous to be aggregated with conventional research synthesis methods. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The bright side of analyst coverage on corporate innovation: Evidence from China

Using a sample of A-share listed companies in China from 2007 to 2019, we investigate the effect of analyst coverage on corporate innovation. We find that analyst coverage will promote corporate innovation, supporting the information hypothesis. We also discuss possible mechanisms of how analyst coverage increases innovation. The information and monitor effects are two plausible channels that allow analyst coverage to promote innovation. Heterogeneity analysis shows the positive relation is more pronounced in non-SOEs, in higher intellectual property protection regions. And considering the firms' life cycle, we find firms in the growth and maturity stage are more vulnerably affected by analysts. Further investigation reveals a positive relationship between analyst coverage and external innovation, and the increased innovation ultimately translates into the long-term value of firms. Our research enriches the impact of analyst coverage on innovation and provides new empirical evidence to improve the multi-level capital market.

Evaluation of frequentist test statistics using constrained statistical inference in the context of the generalized linear model

ABSTRACT When faced with a binary or count outcome, informative hypotheses can be tested in the generalized linear model using the distance statistic as well as modified versions of the Wald, the Score and the likelihood-ratio test (LRT). In contrast to classical null hypothesis testing, informative hypotheses allow to directly examine the direction or the order of the regression coefficients. Since knowledge about the practical performance of informative test statistics is missing in the theoretically oriented literature, we aim at closing this gap using simulation studies in the context of logistic and Poisson regression. We examine the effect of the number of constraints as well as the sample size on type I error rates when the hypothesis of interest can be expressed as a linear function of the regression parameters. The LRT shows the best performance in general, followed by the Score test. Furthermore, both the sample size and especially the number of constraints impact the type I error rates considerably more in logistic compared to Poisson regression. We provide an empirical data example together with R code that can be easily adapted by applied researchers. Moreover, we discuss informative hypothesis testing about effects of interest, which are a non-linear function of the regression parameters. We demonstrate this by means of a second empirical data example.

Decentralising for local information? Evidence from state‐owned listed firms in China

AbstractThis study investigates the effect of decentralisation of SOEs on stock price crash risk. In so doing, we test two competing hypotheses. Under the Political Influence Hypothesis, decentralisation aggravates local government's expropriation of minority shareholders (type II agency conflict), and thus increases crash risk. Under the Local Information Hypothesis, decentralisation decreases monitoring distance (type I agency conflict), strengthens external monitoring and thus decreases crash risk. We find robust evidence supporting the Political Influence Hypothesis. Cross‐sectional analyses show that our baseline results are more pronounced when firms are decentralised to the provincial level and politicians have greater incentives to pursue their political objectives. We further show that bad news hoarding and risk‐taking are two potential channels through which SOE decentralisation increases crash risk. Taken together, our results imply that the decentralisation exacerbates the type II agency conflict rather than ameliorates the type I agency conflict in SOEs.

A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2022 APA, all rights reserved).