Random digit dialing (RDD) telephone sampling, although experiencing declining response rates, remains one of the most accurate and cost-effective data collection methods for generating national population-based estimates. Such methods, however, are inefficient when sampling hard-to-reach populations because the costs of recruiting sufficient sample sizes to produce reliable estimates tend to be cost prohibitive. The authors implemented a novel respondent-driven sampling (RDS) approach to oversample cigarette smokers and lesbian, gay, bisexual, and transgender (LGBT) people. The new methodology selects RDS referrals or seeds from a probability-based RDD sampling frame and treats the social networks as clusters in the weighting and analysis, thus eliminating the intricate assumptions of RDS. The authors refer to this approach as RDD^+RDS. In 2016 and 2017, a telephone survey was conducted on tobacco-related topics with a national sample of 4,208 U.S. adults, as well as 756 referral-based respondents. The RDD^+RDS estimates were comparable with stand-alone RDD estimates, suggesting that the addition of RDS responses from social networks improved the precision of the estimates without introducing significant bias. The authors also conducted an experiment to determine whether the number of recruits would vary on the basis of how the RDS recruitment question specified the recruitment population (closeness of relationship, time since last contact, and LGBT vs. tobacco user), and significant differences were found in the number of referrals provided on the basis of question wording. The RDD^+RDS sampling approach, as an adaptation of standard RDD methodology, is a practical tool for survey methodologists that provides an efficient strategy for oversampling rare or elusive populations.

随机数字拨号（RDD）电话采样虽然响应率不断下降，但仍然是用于生成基于全国人口的估算值的最准确，最具成本效益的数据收集方法之一。但是，这种方法在对难以到达的人群进行抽样时效率低下，因为招募足够多的样本量以产生可靠的估计值的成本往往过于昂贵。作者实施了一种新颖的响应者驱动采样（RDS）方法，以对吸烟者和女同性恋，男同性恋，双性恋和变性者（LGBT）进行过度采样。新方法从基于概率的RDD抽样框架中选择RDS推荐人或种子，并在加权和分析中将社交网络视为群集，从而消除了RDS的复杂假设。作者将此方法称为RDD^{+ RDS}。在2016年和2017年，针对与烟草有关的主题进行了电话调查，在全国范围内对4,208名美国成年人以及756名基于推荐的受访者进行了抽样调查。RDD ^{+ RDS}估计值与独立的RDD估计值可比，表明添加来自社交网络的RDS响应可提高估计值的精度，而不会引入明显的偏差。作者还进行了一项实验，以根据RDS招聘问题如何指定招聘人口（关系的亲密性，自上次接触以来的时间以及LGBT与烟草使用者之间的关系）来确定招聘人数是否会有所不同，并且存在显着差异可以在根据问题措辞提供的推荐数量中找到。RDD ^{+ RDS} 抽样方法是对标准RDD方法的一种改编，是调查方法学家的实用工具，可为稀有或难以捉摸的种群过度抽样提供有效策略。