Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

数据生产要素的结合机制——互补性资产视角

数据作为生产要素创造的价值备受关注,但目前对数据与其他生产要素的结合机制缺乏明确的解释。本文从互补性资产理论出发,分别探讨数据与技术、资本、劳动生产要素的结合机制。研究表明:在与技术要素的结合上,数据要素的加入能够帮助创新企业更有效地使用互补性资产,从而产生技术创新;在与资本要素的结合上,数据要素的加入降低了企业获取作为生产资源和营销资源的互补性资产的成本,从而产生成本创新;在与劳动要素的结合上,数据要素能够从产品设计和生产方面提升互补性资产的生成效率,从而产生流程创新。最终,数据要素在与核心生产要素的有机结合下,极大促进了数字经济的创新与发展。     

基于Hawkes因子模型的股价共同跳跃研究

本文首先比较了三种目前主流的共跳检验方法：基于LM检验的共跳检验、BLT共跳检验和FHLL共跳检验，结果表明，三种方法在识别共跳数量上差距明显，但三者结果的重合部分基本属于市场暴涨暴跌行情，说明共跳识别对市场剧烈波动的聚集性较为敏感。基于跳跃、共跳存在的聚集性问题，本文将Hawkes过程引入跳跃和共跳的研究，构建了基于Hawkes过程的因子模型，结果显示，基于Hawkes因子模型的MJ统计量、CJ统计量和实证数据的拟合程度较好，表明因子模型能够更好地描述跳跃和共跳的聚集性。     

京津冀文化消费的影响因素与结构特征

在京津冀协同发展战略中,文化创意产业在取得初步发展成果的同时,还依旧面临着不少亟待解决的问题。采用问卷调查法实地走访采集区域居民文化消费数据,并进一步通过决策树算法进行数据挖掘,发现京津冀文化消费的影响因素及结构特征,据此提出打造区域共同文化品牌、保障居民收入持续平稳增长、针对青少年文化消费市场创新营销模式、培养其正确的文化价值观及文化创新能力等改进措施,从而优化推动京津冀文化消费市场协同发展的路径,打造互联互通、开放统一的区域文化消费市场,使区域居民文化消费需求成为促进文化产品供给的市场新动力。     

ECM-IS视角下移动政务APP用户持续使用意愿影响因素研究

随着“互联网+”时代的到来，移动政务APP快速发展，但也存在功能单一、满意度不高、用户持续使用意愿较低的问题，要促使其健康发展，必须重视用户的使用体验和使用意愿。本研究以ECM-IS模型理论为基础，构建了移动政务APP用户持续使用意愿影响因素模型，据此设计调查问卷并进行实证研究。研究结果显示，移动政务APP的系统质量是满足用户期望的重要因素，而用户感知对于提升用户的满意度以及促使持续使用具有重要的意义，其中，感知便利性和感知有用性的积极作用尤为明显。最后，从促使用户持续使用的角度，提出了完善移动政务APP系统功能、突出实用优势，兼顾趣味功能，以及重视用户感知、强调人文关怀等对策和建议。     

地震后家庭幸福指数研究

2008年5月12日，举国震动的汶川大地震打破了平静，灾区人民的生命财产遭受巨大损失。灾后重建，八方支援，灾区人民找回失去的幸福。文章采用问卷调查地震灾区重建后家庭幸福感状况，从物质生活幸福感、环境幸福感和精神生活幸福感三因子反映地震后家庭幸福指数，构建因子模型，以期向爱心人士提供灾后重建的家庭幸福感的可靠信息。     

A statistical procedure for representing state fragility and transition paths

State fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks.     

Choice of the ridge factor from the correlation matrix determinant

Ridge regression is the alternative method to ordinary least squares, which is mostly applied when a multiple linear regression model presents a worrying degree of collinearity. A relevant topic in ridge regression is the selection of the ridge parameter, and different proposals have been presented in the scientific literature. Since the ridge estimator is biased, its estimation is normally based on the calculation of the mean square error (MSE) without considering (to the best of our knowledge) whether the proposed value for the ridge parameter really mitigates the collinearity. With this goal and different simulations, this paper proposes to estimate the ridge parameter from the determinant of the matrix of correlation of the data, which verifies that the variance inflation factor (VIF) is lower than the traditionally established threshold. The possible relation between the VIF and the determinant of the matrix of correlation is also analysed. Finally, the contribution is illustrated with three real examples.     

城镇化、工业化对农业全要素生产率增长的影响研究——来自重庆37个县(区)面板数据的证据

文章基于2000—2016年重庆37个县（区）面板数据，使用序列DEA方法测算了重庆农业全要素生产率变动，在此基础上探讨了重庆农业全要素生产率增长的时间演变，并从理论和实证层面系统考察了城镇化和工业化对农业全要素生产率增长的影响。结果表明：首先，2000—2016年间重庆市农业全要素生产率呈上升趋势，2007年后重庆市农业全要素生产率增长更为明显，并且重庆市各地区呈现出明显的空间不平衡性。其次，动态面板模型的实证结果表明城镇化对农业全要素生产率增长具有显著正向影响，工业化对农业全要素生产率增长也具有显著正向影响。最后，研究还发现基础设施、教育水平以及金融发展程度对农业全要素生产率增长均具有显著正向影响，而产业结构对农业全要素生产率增长具有显著负向影响。