Purpose: Sampling lesbian, gay, and bisexual (LGB) people to recruit a national probability sample is challenging for many reasons, including the low base rate of LGB people in the population. To address this challenge, researchers have relied on diverse approaches to sampling LGB people. We aimed to test an innovative method to assemble a U.S. national probability sample of non-transgender sexual minority adults. Methods: Our approach used two phases. In Phase 1, we identified LGBT respondents in a probability general population sample. These respondents were then queried about their sexual orientation and gender identity using short screening questions to identify non-transgender sexual minority respondents. In Phase 2, the identified sexual minority respondents completed the targeted survey online or on a mailed questionnaire. Results: In Phase 1, using random-digit dialing, a nationally representative sample of 366,644 respondents were screened in a brief telephone interview. Of them, 3.5% (n = 12,837) identified as LGB or transgender. In Phase 2, eligible respondents were asked to participate in a self-administered survey questionnaire. Eligibility was based on gender identity, age, race and ethnicity, and educational restrictions. Of the 3525 who were eligible, 81% (n = 2840) agreed to participate in the study (78% agreed to use the web version and 22% the mailed questionnaire), and 49% of web surveys and 46% of mailed surveys were completed. The final sample included 1331 respondents. Conclusion: The benefits of this approach include the ability to assess sexual minority-specific content in a national probability sample; challenges include high cost and low base rates for Asian and American Indian or Alaska Native individuals in the United States. *We use the generic term sexual minority to refer to people who are not heterosexual, including lesbian women, gay men, and bisexual (LGB) individuals, and those who identify by other terms, such as queer. We use the term LGBT community to refer to the community as a whole when not specifying a particular subgroup, such as LGB or transgender people.

Telephone surveys have been a ubiquitous method of collecting survey data, but the environment for telephone surveys is changing. Many surveys are transitioning from telephone to self-administration or combinations of modes for both recruitment and survey administration. Survey organizations are conducting these transitions from telephone to mixed modes with only limited guidance from existing empirical literature and best practices. This article summarizes findings by an AAPOR Task Force on how these transitions have occurred for surveys and research organizations in general. We find that transitions from a telephone to a self-administered or mixed-mode survey are motivated by a desire to control costs, to maintain or improve data quality, or both. The most common mode to recruit respondents when transitioning is mail, but recent mixed-mode studies use only web or mail and web together as survey administration modes. Although early studies found that telephone response rates met or exceeded response rates to the self-administered or mixed modes, after about 2013, response rates to the self-administered or mixed modes tended to exceed those for the telephone mode, largely because of a decline in the telephone mode response rates. Transitioning offers opportunities related to improved frame coverage and geographic targeting, delivery of incentives, visual design of an instrument, and cost savings, but challenges exist related to selecting a respondent within a household, length of a questionnaire, differences across modes in use of computerization to facilitate skip patterns and other questionnaire design features, and lack of an interviewer for respondent motivation and clarification. Other challenges related to surveying youth, conducting surveys in multiple languages, collecting nonsurvey data such as biomeasures or consent to link to administrative data, and estimation with multiple modes are also prominent.

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

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Numerous within-household selection methods have been tested in general population surveys since the advent of telephone interviewing. However, very few selection studies, if any, have been conducted with a dual frame (landline and cell phone) sample. Landline and cell phone frames are known to represent demographically different groups of respondents, and selection methods that may result in more representative demographics in a landline frame may actually skew the results when combined with the cell phone frame. This study tested 11 different within-household selection methods with approximately 11,000 landline respondents. A parallel cell phone sample was also collected with 1,000 respondents, and the frames were combined for analysis. The selection methods tested included one probability-based method, four quasi-probability methods and six nonprobability methods. The methods were evaluated on four criteria: response rates, accuracy, demographic representation and substantive results. The demographic representativeness of each method was examined for the landline frame only and for the dual (landline and cell phone) frame combination. The probability method had the lowest response rate, while the nonprobability at-home methods had the highest. Accuracy rates were lowest for the quasi-probability birthday methods. There were few demographic differences between selection methods, and no substantive differences, when combined with the cell phone sample.