For almost half a century, Paul Meehl educated psychologists about how the mindless use of null-hypothesis significance tests made research on theories in the social sciences basically ...uninterpretable. In response to the replication crisis, reforms in psychology have focused on formalizing procedures for testing hypotheses. These reforms were necessary and influential. However, as an unexpected consequence, psychological scientists have begun to realize that they may not be ready to test hypotheses. Forcing researchers to prematurely test hypotheses before they have established a sound “derivation chain” between test and theory is counterproductive. Instead, various nonconfirmatory research activities should be used to obtain the inputs necessary to make hypothesis tests informative. Before testing hypotheses, researchers should spend more time forming concepts, developing valid measures, establishing the causal relationships between concepts and the functional form of those relationships, and identifying boundary conditions and auxiliary assumptions. Providing these inputs should be recognized and incentivized as a crucial goal in itself. In this article, we discuss how shifting the focus to nonconfirmatory research can tie together many loose ends of psychology’s reform movement and help us to develop strong, testable theories, as Paul Meehl urged.
The primary objection to debiasing-training interventions is a lack of evidence that they improve decision making in field settings, where reminders of bias are absent. We gave graduate students in ...three professional programs (N = 290) a one-shot training intervention that reduces confirmation bias in laboratory experiments. Natural variance in the training schedule assigned participants to receive training before or after solving an unannounced business case modeled on the decision to launch the Space Shuttle Challenger. We used case solutions to surreptitiously measure participants’ susceptibility to confirmation bias. Trained participants were 19% less likely to choose the inferior hypothesis-confirming solution than untrained participants. Analysis of case write-ups suggests that a reduction in confirmatory hypothesis testing accounts for their improved decision making in the case. The results provide promising evidence that debiasing-training effects transfer to field settings and can improve decision making in professional and private life.
Digital innovation is drastically reforming the provision of payment services. Information Technology has modernised the various aspects of our lives, and the shift from traditional to digital ...banking has been gradual and remains ongoing and is constituted by differing degrees of banking service digitization. With the advent of digitization, paperless banking has become a reality for everyone. The advancement of technology has been a significant influence on the growth of the banking business. Previously, banking transactions took a long time. Customers were expected to have physical records of their banking transactions or histories. It is essential to first understand the fundamentals of banking to fully comprehend how it will change in the digital era. The paper examines the adaptation and perception of the digital payment system provided by the bank. The study’s objectives are to investigate how customers view utilising digital banking to make payments, how they pay for merchandise, how they experience problems while conducting online transactions, and how satisfied they are with the digital banking services they receive. The study is descriptive, with a sample size of 500. The instrument for data collection was a questionnaire, and ANOVA was used for hypothesis testing.
Reusing Natural Experiments HEATH, DAVIDSON; RINGGENBERG, MATTHEW C.; SAMADI, MEHRDAD ...
The Journal of finance (New York),
August 2023, Letnik:
78, Številka:
4
Journal Article
Recenzirano
Odprti dostop
ABSTRACT
After a natural experiment is first used, other researchers often reuse the setting, examining different outcome variables. We use simulations based on real data to illustrate the multiple ...hypothesis testing problem that arises when researchers reuse natural experiments. We then provide guidance for future inference based on popular empirical settings including difference‐in‐differences, instrumental variables, and regression discontinuity designs. When we apply our guidance to two extensively studied natural experiments, business combination laws and the Regulation SHO pilot, we find that many results that were statistically significant using single hypothesis testing do not survive corrections for multiple hypothesis testing.
Methods of merging several p-values into a single p-value are important in their own right and widely used in multiple hypothesis testing. This paper is the first to systematically study the ...admissibility (in Wald's sense) of p-merging functions and their domination structure, without any information on the dependence structure of the input p-values. As a technical tool, we use the notion of e-values, which are alternatives to p-values recently promoted by several authors. We obtain several results on the representation of admissible p-merging functions via e-values and on (in)admissibility of existing p-merging functions. By introducing new admissible p-merging functions, we show that some classic merging methods can be strictly improved to enhance power without compromising validity under arbitrary dependence.
O Comitê de Pronunciamentos Contábeis - CPC foi criado por meio da Resolução do Conselho Federal de Contabilidade - CFC n. 1055/2005 e tem como principal função a emissão dos pronunciamentos ...contábeis com objetivo de harmonização das normas brasileiras de Contabilidade às internacionais - IFRS. Na área acadêmica têm surgido diversas pesquisas após a adoção da normatização internacional, trazendo em destaque o efeito da aplicação dessas normas sobre algum instrumento contábil. Sob esse contexto, este artigo teve como objetivo geral identificar a quantidade de pareceres de auditoria com ressalva e parágrafo de ênfase após a implantação da IFRS nos dois anos iniciais. Especificamente pretendeu-se verificar se o número de pareceres das empresas auditadas por uma BIG FOUR foi maior em relação as outras firmas de auditoria não pertencentes a esse grupo. Foram pesquisadas empresas nacionais de capital aberto da BM&F BOVESPA, exceto seguradoras e bancos, no período de 2008 PRÉ IFRS a 2011 PÓS IFRS, referente à adoção das normas que entraram em vigor a partir de 2010, por meio da Lei n. 11638/2007. A pesquisa foi desenvolvida por um estudo estatístico descritivo e inferencial cujas variáveis qualitativas foram o tipo de pareceres emitido com ressalva, sem ressalva e negativa de opinião, tipo de auditoras BIG FOUR e NÃO BIG FOUR, que foram descritas por suas frequentes absolutas e relativas e que em seguida foram comparadas entre quatro anos, por meio de testes de hipóteses de QUI QUADRADO (X2). Os resultados encontrados por meio dos testes estatísticos foram que os pareceres sem ressalva foram mais frequentes em todos os anos, porém, apenas em 2010 pareceres com ressalva foram mais frequentes aos demais anos, e as negativas de opinião surgiram apenas em 2011. Embora, a frequência com BIG FOUR tenha sido menor no decorrer dos anos, principalmente a partir de 2010, os pareceres com ressalva foram mais frequentes por empresas BIG FOUR.
Applied Asymptotics Brazzale, A. R.; Davison, A. C.; Reid, N.
05/2007, Letnik:
v.Series Number 23
eBook
In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is ...sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.
Although null hypothesis significance testing (NHST) is the agreed gold standard in medical decision making and the most widespread inferential framework used in medical research, it has several ...drawbacks. Bayesian methods can complement or even replace frequentist NHST, but these methods have been underutilised mainly due to a lack of easy-to-use software. JASP is an open-source software for common operating systems, which has recently been developed to make Bayesian inference more accessible to researchers, including the most common tests, an intuitive graphical user interface and publication-ready output plots. This article provides a non-technical introduction to Bayesian hypothesis testing in JASP by comparing traditional tests and statistical methods with their Bayesian counterparts.
The comparison shows the strengths and limitations of JASP for frequentist NHST and Bayesian inference. Specifically, Bayesian hypothesis testing via Bayes factors can complement and even replace NHST in most situations in JASP. While p-values can only reject the null hypothesis, the Bayes factor can state evidence for both the null and the alternative hypothesis, making confirmation of hypotheses possible. Also, effect sizes can be precisely estimated in the Bayesian paradigm via JASP.
Bayesian inference has not been widely used by now due to the dearth of accessible software. Medical decision making can be complemented by Bayesian hypothesis testing in JASP, providing richer information than single p-values and thus strengthening the credibility of an analysis. Through an easy point-and-click interface researchers used to other graphical statistical packages like SPSS can seemlessly transition to JASP and benefit from the listed advantages with only few limitations.
Many constructs in management studies, such as perceptions, personalities, attitudes, and behavioral intentions, are not directly observable. Typically, empirical studies measure such constructs ...using established scales with multiple indicators. When the scales are used in a different population, the items are translated into other languages or revised to adapt to other populations, it is essential for researchers to report the quality of measurement scales before using them to test hypotheses. Researchers commonly report the quality of these measurement scales based on Cronbach’s alpha and confirmatory factor analysis results. However, these results are usually inadequate and sometimes inappropriate. Moreover, researchers rarely consider sampling errors for these psychometric quality measures. In this best practice paper, we first critically review the most frequently-used approaches in empirical studies to evaluate the quality of measurement scales when using structural equation modeling. Next, we recommend best practices in assessing reliability, convergent and discriminant validity based on multiple criteria and taking sampling errors into consideration. Then, we illustrate with numerical examples the application of a specifically-developed R package, measureQ, that provides a one-stop solution for implementing the recommended best practices and a template for reporting the results. measureQ is easy to implement, even for those new to R. Our overall aim is to provide a best-practice reference for future authors, reviewers, and editors in reporting and reviewing the quality of measurement scales in empirical management studies.
Selectively publishing results that support the tested hypotheses (“positive” results) distorts the available evidence for scientific claims. For the past decade, psychological scientists have been ...increasingly concerned about the degree of such distortion in their literature. A new publication format has been developed to prevent selective reporting: In Registered Reports (RRs), peer review and the decision to publish take place before results are known. We compared the results in published RRs (N = 71 as of November 2018) with a random sample of hypothesis-testing studies from the standard literature (N = 152) in psychology. Analyzing the first hypothesis of each article, we found 96% positive results in standard reports but only 44% positive results in RRs. We discuss possible explanations for this large difference and suggest that a plausible factor is the reduction of publication bias and/or Type I error inflation in the RR literature.
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