This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of non-random sampling on results of association tests in a setting with data on one variable of interest for all observations and frequently-missing data on another variable of interest. We develop and validate a resampling approach that uses only observations from the data-restricted sample to construct distribution-matched samples that approximate randomly-drawn samples from the reference sample. Our simulation tests provide evidence that distribution-matched samples yield generalizable results. We demonstrate the effects of non-random sampling in tests of the association between realized returns and five implied cost of equity metrics. In this setting, the reference sample has full information on realized returns, while on average only 16% of reference sample observations have data on cost of equity metrics. Consistent with prior research (e.g., Easton and Monahan The Accounting Review 80, 501–538, 2005), analysis using the unadjusted (non-random) cost of equity sample reveals weak or negative associations between realized returns and cost of equity metrics. In contrast, using distribution-matched samples, we find reliable evidence of the theoretically-predicted positive association. We also conceptually and empirically compare distribution-matching with multiple imputation and selection models, two other approaches to dealing with non-random samples.

本文研究了档案会计研究中经常遇到的数据需求如何产生数据受限的样本，这是从参考样本中随机抽取的观察值，研究人员希望将这些结果归纳为总结。我们举例说明了一个环境中非随机抽样对关联测试结果的影响，该环境中的所有观察值都涉及一个关注变量的数据，而另一个关注变量则经常丢失数据。我们开发并验证了一种重采样方法，该方法仅使用数据受限的样本中的观察值来构建分布匹配的样本，该样本近似于从参考样本中随机抽取的样本。我们的模拟测试提供了证据，证明分布匹配的样本可产生可推广的结果。我们在已实现的回报与五个隐含的权益成本度量之间的关联性测试中证明了非随机抽样的影响。在这种情况下，参考样本具有有关已实现回报的完整信息，而平均而言，参考样本观测值中只有16％包含有关权益成本度量的数据。与先前的研究一致（例如Easton和Monahan《会计评论》 80，501–538，2005），使用未经调整的（非随机）股权成本样本进行的分析显示，已实现收益与股权成本指标之间存在弱关联或负关联。相反，使用分布匹配的样本，我们找到了理论上预测的正相关的可靠证据。我们还在概念上和经验上将分布匹配与多个插补和选择模型进行比较，这是处理非随机样本的另外两种方法。