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The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.

Based on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.

The exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.

In this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.

The authors derive the necessary mathematics, provide computer simulations, provide links to free and user-friendly computer programs, and analyze real data sets.

Cohen's d, which indexes the difference in means in standard deviation units, is the most popular effect size measure in the social sciences and economics. Not surprisingly, researchers have developed statistical procedures for estimating sample sizes needed to have a desirable probability of rejecting the null hypothesis given assumed values for Cohen's d, or for estimating sample sizes needed to have a desirable probability of obtaining a confidence interval of a specified width. However, for researchers interested in using the sample Cohen's d to estimate the population value, these are insufficient. Therefore, it would be useful to have a procedure for obtaining sample sizes needed to be confident that the sample. Cohen's d to be obtained is close to the population parameter the researcher wishes to estimate, an expansion of the a priori procedure (APP). The authors derive the necessary mathematics, provide computer simulations and links to free and user-friendly computer programs, and analyze real data sets for illustration of our main results.

In this paper, the authors answered the following two questions: The precision question: How close do I want my sample Cohen's d to be to the population value? The confidence question: What probability do I want to have of being within the specified distance?

To the best of the authors’ knowledge, this is the first paper for estimating Cohen's effect size, using the APP method. It is convenient for researchers and practitioners to use the online computing packages.

The purpose of this article is to show the gains that can be made if researchers were to use gain-probability (G-P) diagrams.

The authors present relevant mathematical equations, invented examples and real data examples.

G-P diagrams provide a more nuanced understanding of the data than typical summary statistics, effect sizes or significance tests.

Gain-probability diagrams provided a much better basis for making decisions than typical summary statistics, effect sizes or significance tests.

G-P diagrams provide a completely new way to traverse the distance from data to decision-making implications.

Four experiments were performed to test a proposal that hierarchically restrictive and partially restrictive trait dimensions (Reeder & Brewer, 1979) pertaining to morality can be understood in terms of Immanuel Kant's distinction between perfect and imperfect duties. In Experiment 1, subjects simply rated the number of contrary behaviors they would have to observe in order to change a prior expectancy that a target person had particular morality traits. Their responses indicated that positive partially restrictive traits were more difficult to disconfirm than positive hierarchically restrictive traits. In Experiment 2, subjects were presented with scenarios where the type of person, the type of situation, the type of behavior, and the trait of concern were manipulated. Behavior had a greater, and the situation had a lesser, effect on trait attributions for hierarchically than partially restrictive morality dimensions. Experiment 3 demonstrated that differences in trait attributions for the two types of trait dimensions were not due to the implications of contrary behaviors for the frequency with which these behaviors could be expected to be repeated in the future. Finally, Experiment 4 further supported the prediction that situational factors affect trait attributions less for hierarchically than partially restrictive trait dimensions pertaining to morality.

South African scholars, like most scholars in the developing world, have sold the idea that social constructivism is the gold standard of qualitative management research. In this chapter, we caution against this subordination to unquestioned conventions and offer a process relational ontology as an alternative to social constructivism that is often punted by most qualitative research programmes and textbooks. We also debunk the idea that ‘grounded theory’ exists by delving into epistemology and showing how science is ‘self-correcting’ rather than ‘tabula rasa’. Instead of boxing business ethics knowledge, as has been done by the case study gurus, we encourage business and organisational ethicists to own their indigenous heritage through storytelling science based on the self-correcting method underpinned by Popperian and Peircian epistemological thought. This chapter encourages business management researchers to move towards more profound ethical knowledge by refuting and falsifying false assumptions in each phase of the study, in a sequence of self-correcting storytelling phases. This is what Karl Popper called trial and error, and what C.S. Peirce called self-correcting by the triadic of Abduction–Induction–Deduction. We offer a novel method for accomplishing this aim that we call ‘Conversational Interviews’ that are based on antenarrative storytelling sciences. Our chapter aims to evoking the transformative power of indigenous ontological antenarratives in authentic conversation in order to solve immediate local problems ad fill the many institutional voids that plague the South(ern)-/African context.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

This paper seeks to further develop a model of natural hazard preparedness by examining the role of attitudes to natural hazards and their mitigation and social norms. It aims to examine whether social‐cultural factors influence the decisions people make regarding their relationship with natural hazards.

Survey data were collected from 156 residents in Napier, New Zealand. A cross‐sectional design was used. Data were analysed using the AMOS 5 structural equation modelling program.

Positive attitudes to hazard mitigation, existing in a social context that advocates adopting protective behaviours, belief in the effectiveness of personal mitigation (outcome expectancy) and good problem solving (action coping) skills increase the likelihood of adopting protective measures for earthquakes. The research identified how attitudes and social norms influence the perception of hazards and how people make preparedness decisions.

Further research is needed to examine how hazard attitudes are formed, sustained and organized, as well as how they can be changed to facilitate the sustained adoption of protective measures. Work also needs to be directed to identifying those with whom normative comparisons are made and the relative influence of different referents.

The findings argue for a move away from reliance on the passive presentation of information to people and communities that dominates risk communication. Rather, strategies for encouraging and sustaining positive discourse about hazards and their mitigation within a community should be prioritized in future risk communication work.

Provides new insights into the relationship between people and natural hazards and provides empirical support for the inclusion of attitudes and social norms in risk communication work. It provides additional support for accommodating the social and cultural context in the development and delivery of risk communication strategies.

In this chapter, we argue that state and trait mindfulness and mindfulness-based practices in the workplace should enhance employee outcomes. First, we review the existing literature on mindfulness, provide a brief history and definition of the construct, and discuss its beneficial effects on physical and psychological health. Second, we delineate a model of the mental and neurobiological processes by which mindfulness and mindfulness-based practices improve self-regulation of thoughts, emotions, and behaviors, linking them to both performance and employee well-being in the workplace. We especially focus on the power of mindfulness, via improved self-regulation, to enhance social relationships in the workplace, make employees more resilient in the face of challenges, and increase task performance. Third, we outline controversies, questions, and challenges that surround the study of mindfulness, paying special attention to the implications of unresolved issues for understanding the effects of mindfulness at work. We conclude with a discussion of the implications of our propositions for organizations and employees and offer some recommendations for future research on mindfulness in the workplace.