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In view of the challenges that may arise from using a single test statistic from a single regression equation to test a hypothesis, it is important that authors assess the evidence comprehensively. In particular, by conducting a variety of robustness tests, authors can show that a significant finding is not due to an idiosyncrasy of the chosen empirical model and/or estimation strategy. Evidence from empirical tests becomes more convincing when it is supported by appropriate robustness tests.

The discussion section of a paper provides opportunities for a comprehensive assessment of the evidence, beyond the statistical properties of the specific tests used in the focal regression analysis. What tests are appropriate varies with the design of the study and the nature of the data. It is a normal part of the reviewing process that reviewers suggest some additional robustness tests, and authors are expected to seriously engage with such suggestions. ${ }^{7}$ If this additional work were to result in an excessive number of tables, an additional file with these tables and short explanation of them can be included in a supplement to the paper that will be made available on the JIBS website. Robustness tests may include, for example, additional analyses with

• alternative proxies of focal constructs (i.e., variables mentioned in the hypotheses as independent or explanatory variables), especially for those that involve abstract concepts that cannot be measured directly;
• alternative sets of control variables, especially when correlation is present in the dataset between a focal explanatory variable and a control variable; and/or
• alternative functional forms of the regression models, especially for the hypotheses that suggest nonlinear effects (Haans et al. 2016; Meyer 2009), or moderating or mediating effects (Andersson et al. 2014; Cortina et al. 2015).

## 会计代写|国际商贸代考International Business代写|From HARKing to Developing Theory

Hypothesizing After the Results are Known (HARKing) in search of hypotheses for already known positive results is causing great harm to scientific progress (Bosco et al. 2016). We would like to note that HARKing is not the same as “playing with your data” to explore the nature of relationships and get better feeling for possibly interesting patterns in a dataset. HARKing refers to the practice of datamining and, after significant results are established, developing or adjusting theoretical arguments ex post, but presenting the theory as if already in place ex ante. The issue with HARKing is that we have no knowledge of the many nulls and negatives that were found but not reported along the way, and therefore readers cannot be sure as to the true power of the statistical evidence. While papers in business studies journals appear to confirm groundbreaking hypotheses, we rarely see reports about falsification outcomes. ${ }^{8}$ As indicated in our opening paragraph, about $89 \%$ of all hypoth- eses in JIBS $(82 \%), S M J(90 \%)$ and Organization Science $(92 \%)$ were confirmed in the 2016 volumes. ${ }^{9}$ Yet no information is provided about the many “interventions” applied to produce this abundance of positive results.

To tackle this problem, no journal can operate a policing force to monitor and sanction what is happening behind the closed doors of our authors’ offices. Eliminating HARKing requires an orchestrated effort to seriously change deeply embedded practices in the scholarly community (Ioannidis 2005, 2012). What we can do, for now, is firstly to reduce the focus on single test statistics when assessing results in favour of comprehensive assessments, and thereby to reduce the incentives to engage in HARKing (hence our guidelines $1-9)$, and secondly to mentor and train a new generation of scholars to intrinsically dislike HARKing practices. Here, key is that established scholars lead by example. Of course, this also requires broader institutional change to remove some of the incentives that disproportionately reward scholars finding statistically significant results (Ioannidis 2012; van Witteloostuijn 2016). What journals can do boils down to, basically, two alternatives (or a combination of both).

## 国际商贸代写

• 焦点结构的替代代理（即假设中提到的作为独立或解释变量的变量），特别是那些涉及无法直接测量的抽象概念的变量;
• 控制变量的替代集合，特别是当数据集中存在焦点解释变量和控制变量之间的相关性时;和/或
• 回归模型的替代功能形式，特别是对于建议非线性效应的假设（Haans等人，2016;Meyer 2009），或调节或中介效应（Andersson et al. 2014;Cortina et al. 2015）。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。