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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding 3 Conducting Research on the Health Status of LGBT Populations As background for the review of existing research on sexual- and gender-minority health in Chapters 4, 5, and 6, the present chapter reviews research challenges associated with the study of LGBT populations, the research methods and data sources used in studying these populations, and best-practice principles for conducting research on the health of LGBT people. The final section presents a summary of key findings and research opportunities. RESEARCH CHALLENGES Three important challenges confront researchers attempting to gather valid and reliable data for describing LGBT populations and assessing their health: (1) operationally defining and measuring sexual orientation and gender identity, (2) overcoming the reluctance of some LGBT individuals to identify themselves to researchers, and (3) obtaining high-quality samples of relatively small populations. In addition, as emphasized in Chapter 1, although the acronym “LGBT” is applied to lesbians, gay men, bisexual men and women, and transgender people, these groups are distinct, and they also comprise subgroups based on race, ethnicity, geographic location, socioeconomic status, age, and other factors. These variations have implications for health research, including the need to obtain sample sizes that are large enough to understand differences among subgroups.
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding Operationally Defining and Measuring Sexual Orientation and Gender Identity Many social, cultural, and behavioral phenomena pose measurement challenges to researchers. For example, multiple operational definitions have been used to assess education (Smith, 1995), political ideology (Knight, 1999), religiosity and religious fundamentalism (Hall et al., 2008; Kellstedt and Smidt, 1996), and race and ethnicity (NRC, 2004; Stephan and Stephan, 2000). Similarly, researchers who study LGBT populations face the challenges of defining sexual orientation and gender identity and developing procedures for operationalizing these constructs. As explained in Chapter 2, sexual orientation is typically defined and measured in terms of three dimensions—behavior, attraction, and identity. Ideally, which of these dimensions is used in research is informed by a particular study’s research goals. For example, a study of HIV risk in gay men would appropriately focus on sexual behavior, whereas a study of experiences with hate crimes or housing discrimination might focus on sexual orientation identity (Herek et al., 2010). Although most adults exhibit consistency across the three dimensions (e.g., they are exclusively heterosexual or homosexual in their sexual behavior, attractions, and self-labeled identity), some do not. Whether a particular study categorizes the latter individuals as lesbian, gay, homosexual, bisexual, heterosexual, or something else will depend on which specific dimension of sexual orientation is measured in that study. In a study that measures sexual orientation in terms of same-sex attraction or sexual behavior with a same-sex partner, for example, the sample may include some participants who do not label themselves as lesbian, gay, or bisexual. Not only do studies vary in which facet of sexual orientation they measure, but they also can differ in how they define each of the three dimensions operationally. The current lack of standardized measures contributes to the variability of population estimates and can make comparisons across studies difficult. For example, if two studies defined sexual orientation operationally in terms of sexual behavior but used different time frames for screening participants (e.g., if one study used the criterion of any same-sex sexual behavior during the past 12 months, whereas the other used any same-sex sexual behavior since age 18), they might reach different conclusions about the target population. Moreover, the samples obtained for both studies would exclude individuals who were not sexually active during the specified time period even if they experienced same-sex attractions or self-identified as lesbian, gay, or bisexual. This variability in the criteria for operationally defining sexual orientation may produce what appear to be inconsistent findings across studies. Although it may appear obvious, it is important to make the point that researchers should carefully evaluate the
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding appropriateness of their operational definition(s) of sexual orientation in light of the research question their study addresses and clearly explain their measurement procedures when reporting their results. Similar definitional and measurement variability can be observed across studies of transgender populations. No uniformly accepted best measures of gender variance and gender nonconformity currently exist. One common approach is simply to ask participants whether they are transgender (e.g., Almeida et al., 2009), and, in some studies, whether they further self-identify as female-to-male or male-to-female. This question often follows immediately a question about sexual orientation. However, Buchting and colleagues (2008) have proposed combining the two questions by asking respondents: “Do you consider yourself to be one or more of the following: (a) Straight, (b) Gay or Lesbian, (c) Bisexual, (d) Transgender.” Because some gender-variant people do not use “transgender” to identify themselves, and some nontransgender individuals may not fully understand the term, simply asking individuals whether they are transgender may lead to underreporting and false positives (SMART, 2009). To address these concerns, some studies have provided respondents with a definition of “transgender” to increase the validity of responses (e.g., Massachusetts Department of Public Health, 2007). Conron and colleagues (2008) report the results of cognitive interviewing with a small nonprobability sample (n = 30) that included transgender youth. Using a question that combined biological sex and gender—asking respondents whether they were “female,” “male,” “transgender, female-to-male,” “transgender, male-to-female,” or “transgender (not exclusively male or female)”—they found that most transgender youth were able to choose a response option they felt was appropriate. However, the authors recommend further testing with slight modifications to the question (Conron et al., 2008). In addition, questions about gender transitioning have been included in several studies (Grant et al., 2010; Nemoto et al., 2005; Xavier et al., 2007). Measuring the sexual orientation of transgender people poses special challenges because some respondents may answer questions about sexual orientation in terms of birth sex (their own or their partner’s), whereas others may respond in terms of gender identity, and still others may find it difficult to answer in terms of a male–female dichotomy (e.g., Austin et al., 2007; Garofalo et al., 2006). Some HIV studies have included questions about the respondent’s sexual behavior with males, females, transgender men, and transgender women. While a number of effective measures of sexual orientation and gender identity have been developed, there remains a need for methodological research to determine the best ways to identify lesbian, gay, bisexual, and transgender people in health research. And while the most appropriate measures of sexual orientation and gender identity vary according to a
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding particular study’s research goals, standardization of measures in federally funded surveys would help improve knowledge about LGBT health because it would allow for the comparison and combination of data across studies. Overcoming the Reluctance to Identify as LGBT to Researchers Researchers studying sensitive topics must deal routinely with the reluctance of some participants to disclose accurate information about themselves. A topic may be sensitive because respondents perceive it as intruding on their privacy, because it raises concerns about the possible repercussions of disclosure to others, or because it triggers social desirability concerns (i.e., the desire to “look good” to others). Examples of sensitive topics include income, illegal activities, sexual practices, and membership in a stigmatized group. When confronted with a question about a sensitive topic, respondents may decline to answer or may intentionally give an inaccurate response. In some cases, respondents may decide not to participate in the study at all, thereby reducing the overall response rate and possibly making the sample less representative of the larger population. All of these outcomes have important implications for data quality (Lee, 1993; Tourangeau and Yan, 2007; Tourangeau et al., 2000). Because they wish to avoid stigma and discrimination and are concerned about their privacy, some individuals are reluctant to disclose their membership in a sexual- or gender-minority group. McFarland and Caceres (2001), for example, describing the factors that lead to underestimation of HIV infection and risk among men who have sex with men, note that stigma and discrimination result in marginalization of these men, which in turn engenders suspicion toward government institutions, researchers, and service providers. Consequently, they argue, many men who have sex with men are unwilling or reluctant to participate in research studies. As with research on other sensitive topics, challenges include nonparticipation and item nonresponse (which occurs when a respondent provides some of the requested information, but certain questions are left unanswered, or certain responses are inadequate for use). Nonparticipation and nonresponse threaten the generalizability of research data to the extent that those who do not disclose their sexual orientation or transgender identity accurately, or decline to participate altogether, differ in relevant ways from those who do disclose and participate. A primary strategy to foster disclosure and reduce nonresponse is for researchers to establish a bond of trust with members of the target population. As with other populations, sexual and gender minorities are more likely to entrust researchers with sensitive information about themselves to the extent that they perceive the researchers to be professional, competent, and sensitive to their concerns about privacy (see, generally, Dillman et
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding al., 2009). In addition, sexual- and gender-minority participants are more likely to trust researchers who evidence knowledge and sensitivity about their community and culture, characteristics commonly understood to be components of cultural competence. As an adjunct to cultural competence, a number of techniques have been used to improve response rates to questions relating to sensitive topics. Modes of data collection that foster participants’ sense of confidentiality or anonymity may yield higher rates of disclosure. For example, research participants may be more willing to disclose same-sex behavior or attractions when they provide their responses via computer rather than in a face-to-face interview (Villarroel et al., 2006; for a review, see Gribble et al., 1999). Collecting data in a private setting and taking steps to establish rapport before asking questions about sensitive topics may also increase respondents’ willingness to disclose sensitive information. Variations in the wording and format of questions, as well as use of terminology that is familiar to the participant, have shown some success in eliciting responses (Catania et al., 1996). Respondents may be more willing to disclose sensitive information about themselves when their participation is anonymous. If anonymity is not possible, understanding that their responses are confidential may increase the extent of participants’ self-disclosure. Although it would not be required, a certificate of confidentiality from the National Institutes of Health (NIH) could be helpful in this regard (NIH, 2011). Obtaining High-Quality Samples of Relatively Small Populations As documented below and in subsequent chapters, numerous studies of sexual and gender minorities that have relied on nonprobability samples have yielded important information about and insights into LGBT life and health. If the goal of a study is to provide estimates that can be generalized with confidence to the entire LGBT population, however, the use of probability-based methods is necessary. Obtaining a probability sample of a relatively small population, such as a racial, ethnic, religious, sexual, or gender minority, requires considerably more resources than are required for sampling the population as a whole. This is the case because a large number of potential participants must be screened to obtain a sample of minority group members large enough for statistical analysis. Still more resources are required to collect samples that permit study of subpopulations within these groups, such as socioeconomic, age, and geographic groupings, and comparisons of respondents according to health-related characteristics. Lacking such resources, relatively few studies designed specifically to examine LGBT individuals have been able to utilize large probability samples. There are, however, some exceptions. In the Urban Men’s Health
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding Study, Catania and colleagues (2001) used a complex, two-stage sampling procedure in New York, Los Angeles, San Francisco, and Chicago to obtain a probability sample of men who have sex with men (n = 2,881) (see also Blair, 1999). Herek and colleagues used the Knowledge Networks panel to obtain a national probability sample of self-identified lesbian, gay, and bisexual adults (n = 662) (Herek, 2009; Herek et al., 2010). Knowledge Networks creates a panel using random-digit dialing to generate a national probability sample and administers an online survey to the panel. Internet access and the appropriate equipment are provided for those panel members who lack them. Other researchers have conducted secondary analyses of health data collected from surveys of large national samples that included at least one question about respondents’ sexual behavior (e.g., Cochran and Mays, 2000), sexual attraction (e.g., Consolacion et al., 2004), or sexual orientation identity (e.g., Cochran et al., 2003, 2007; Hatzenbuehler et al., 2009, 2010; Mays and Cochran, 2001; McLaughlin et al., 2010). The findings from many of these studies are discussed in later chapters of this report. In addition to the data sets used in these secondary analyses, numerous other government and academic surveys routinely use large national probability samples to collect extensive data on the health of Americans. However, relatively few of these surveys have included measures of variables related to sexual orientation or gender identity. Consequently, many of the data sources widely used by health researchers do not yield insights into LGBT populations. As discussed later in this chapter, this situation can be remedied by routinely including measures of sexual orientation and gender identity in these surveys. U.S. census data have also been used to obtain information about the LGBT population (Black et al., 2000; Gates, 2007; Rosenfeld, 2010), but the available information is limited. Since 1990, the census has reported data for same-sex partners who live in the same household, provided that one of them is designated the householder and both report their gender and relationship status on the household roster. However, an unknown number of same-sex partners who do not meet these conditions are not identified. Moreover, because census respondents’ sexual orientation is not ascertained, lesbians, gay men, and bisexual adults who are not cohabiting in a same-sex relationship remain invisible in the data. Nor can transgender people be identified in census data. It should be noted that adding content to the census requires the approval of the U.S. Office of Management and Budget and, ultimately, the Congress. A third approach to obtaining a national probability sample with a sufficient number of sexual- and gender-minority respondents involves combining data across studies. For ongoing studies that recruit new probability samples on a regular basis, it can be possible to combine sexual- and
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding gender-minority respondents across years to produce a sample that is sufficiently large for analysis, provided that the studies all include comparable measures of key variables. Combining data from eight waves of the General Social Survey with data from the National Health and Social Life Survey (NHSLS) and the Chicago Health and Social Life Survey, for example, Wienke and Hill (2009) compared the well-being of partnered gay men and lesbians (n = 282) with that of single gay men and lesbians (n = 59) and married, cohabiting, dating, and single heterosexuals (sample sizes ranged from 614 to 6,734). Combining data from multiple samples can be helpful in researching groups (like sexual and gender minorities) that represent a small domain in part of a larger survey. Because the numbers of these small groups often are not sufficiently large for analysis, combining data from multiple samples allows researchers to generate more accurate estimates. However, this method poses a variety of analytical challenges, and statistical methods for improving the estimation and analysis of small domains continue to be developed (Rao, 2003). These methods usually require assumptions about the statistical models employed and additional information related to the estimates the researcher wants to produce. For application to LGBT health research, these measures require the implementation and use of consistent measures to identify LGBT populations. Raghunathan and colleagues (2007) provide an example that, although not involving LGBT populations, combines information from two data sets to improve the efficiency of county-level estimates. The authors use a statistical modeling approach—combining data from the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey conducted by state agencies, and the National Health Interview Survey (NHIS), an area probability sample surveyed through face-to-face interviews—to improve county-level prevalence rates of cancer risk factors that were developed from one survey alone. In a case study using data from the NHIS and the National Nursing Home Survey, Schenker and colleagues (2002) provide an example that illustrates the benefits of combining estimates from complementary surveys and discuss the analytic issues involved in doing so. Schenker and Raghunathan (2007) review four studies conducted by the National Center for Health Statistics that combine information from multiple surveys to improve various measures of health. In another example, Elliott and colleagues (2009) recognized that estimates of health care disparities in small racial/ethnic groups are often lacking in precision because of the small sample sizes involved. They developed an application of the Kalman filter (a recursive algorithm originally used in engineering applications; see Kalman, 1960) to use the available data more efficiently. By applying the Kalman filter to 8 years of data from the NHIS, they demonstrated how estimates for small populations could be improved by combining estimates from
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding multiple years. In many cases, this method improved precision to an extent that would be similar to what would be achieved by doubling the sample size of the yearly data. When this method is used, the LGBT populations in the data sets that are statistically combined must be identified. RESEARCH METHODS In all empirical research, each component of the study design must be based on consideration of specific characteristics of the population being studied if effective methods for data gathering are to be developed. For LGBT studies, researchers must identify and select the most effective methods to compensate for the unique research challenges discussed above. This section reviews sampling issues, including the utility of probability and nonprobability sampling for generating study populations for LGBT health research, and describes quantitative and qualitative analytic methods used in LGBT research. Research studies are designed to describe population characteristics, explore unanswered questions, or test hypotheses in order to validate previous findings or investigate areas that have not been fully explored. The applicability of research findings is directly related to the study design and the ability of the research team to identify an adequate sample for analysis. The manner in which the data collection methodology, the measurement design, and sample selection methods and subject recruitment are assembled into a coherent study design determines the relevance and generalizability of the findings. Internal and external validity are important considerations for evaluating the relevance of LGBT research findings. Internal validity means that the measures of all variables are reliable, there is justification for linkages of relationships between independent and dependent variables, and other extraneous variables that are not logically associated are ruled out. External validity denotes the generalizability of study results beyond the specific study setting. These issues are discussed throughout the chapter. Sampling Challenges Careful sampling requires a precise definition of the target population of the study. The target population is the set of elements about which information is wanted and parameter estimates are required (OMB, 2001). For example, the target population could be all LGBT persons in the United States or in a state, community, or other geographic area. If members of the target population are selected into the sample by a random, unbiased mechanism such that every person in the target population has a known chance of being selected into the study, the resultant study sample can be
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding used to draw inferences and generalize about the target population, and the sample thus generated is “representative” of the target population. After the desired target population for a study has been specified, selection of a sample requires identifying or developing a sampling frame or list of elements in the target population. The completeness of the sampling frame relative to the target population and the methods by which individual units are selected or identified for the study sample determine the limits of statistical inference and generalizability for the study results. Typically, researchers obtain study samples by selecting participants from a geographically defined population or a list of individuals who share a common characteristic, such as inclusion in a membership list of professionals. As discussed above, a variety of factors create challenges for generating samples that are representative of LGBT populations. Recently, alternative models have been developed to identify a target population by starting with the community of interest and identifying samples that mirror characteristics of that community. A probability-based mechanism may or may not be used for selecting the study sample. For LGBT studies, both probability and nonprobability sampling methods have been used. Probability Sampling Probability sampling identifies a well-defined target population and sampling frame and uses a probabilistic method of selection to obtain a sample that is representative of the target population (Kalton, 2009). Although probability sampling can be expensive and the statistical methods employed can be complicated, the ensuing data lead to findings that can be generalized to the target population. If the target population were the nation’s LGBT populations, the sampling frame had characteristics such that it was possible to identify all LGBT people, and a probability mechanism were defined that gave everyone in the sampling frame an equal chance of being selected, then the findings could be generalized to LGBT populations in the United States—within the scope of the study measures and subject to limitations of sampling and nonsampling error. Probability-based sampling methods rely on the assumption that a list of all eligible units of the target population can be constructed and that all units will have a known probability of selection. Many approaches to obtaining a probability-based sample of a population ensure that valid inferences can be drawn. Kalton (2009) describes a number of such approaches for obtaining valid samples for subpopulations. When an existing sampling frame can identify whether an individual is a member of a subpopulation, drawing a sample of a specified size can be accomplished in a straightforward way. On the other hand, in many
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding applications, individuals cannot be identified prior to selection of the sample. In such cases, major challenges exist within the probability-based framework. The approaches Kalton describes can be costly, as several require extensive screening to identify the subpopulation(s) of interest or can rely on a number of assumptions to permit valid inferences. A common practice is to draw a large sample of the general population and then screen potential participants for inclusion in the study based on criteria that define the study’s target population. With populations such as LGBT individuals, ineligible participants must be identified and eliminated from the study during the data collection process. This process is often implemented with a series of screening questions administered at the time the interviewer first contacts the household person. For example, the previously mentioned Urban Men’s Health Study used telephone screening, along with other techniques, to obtain a probability sample of men who were gay or bisexual or reported having sex with men and who resided in New York, Chicago, Los Angeles, and San Francisco (Blair, 1999; Catania et al., 2001). To compare the yield of population-based methods for health needs assessments, Meyer and colleagues (2002) and Bowen and colleagues (2004) conducted paired surveys in Jamaica Plain, Massachusetts, using random-digit dialing and household area probability sampling in the same census tracts. Percentages of women who identified as sexual minorities were similar across the two sampling methods. Another method, known as disproportionate stratification, can be effective for identifying small study populations. This method identifies areas where the target population is more highly concentrated and then samples a higher fraction of units within those areas. Disproportionate sampling may be an effective screening strategy for LGBT populations while ensuring that population estimates are possible. For example: Boehmer and colleagues (2010) used disproportionate sampling to select geographic units in census areas with a higher prevalence of lesbians and bisexual women. The 2003 California LGBT Tobacco Survey used disproportionate stratification in its random-digit dialing sampling design. The survey used areas identified by the 2000 decennial census as having a high proportion of unmarried same-sex partners and applied a weighting scheme to make the sample representative of the lesbian and gay population of California (Carpenter and Gates, 2008). Sampling using multiple sampling frames takes advantage of more than one partial listing of the target population to create a probability sample; care must be taken to remove duplicate listings of individuals when using this method. Aaron and colleagues (2003) used capture recapture methods with multiple lists and elimina-
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding tion of duplicates to estimate the lesbian population in Allegheny County, Pennsylvania. Network or multiplicity sampling uses sampled persons as proxy respondents for persons who are “linked” to them in a specific way, for example, as a family member (Sirken, 2004). An assumption required for this method is that all members of the linkage must know or be willing to report the rare population status of those linked to them (Kalton, 2009). Probability sampling has seen limited use in the study of LGBT health. As explained above, the relatively small size of LGBT populations, the lack of research funding, and the sensitivity of questions relating to sexual behavior and gender expression have been barriers to effective probability sampling. Despite these challenges, some researchers have used probability samples for LGBT research. In addition to the examples cited earlier (Catania et al., 2001; Herek et al., 2010), the NHSLS, described in the previous chapter (Laumann et al., 1994), used multistage sampling to create a probability sample of U.S. households. Although sexual and gender minorities were not specifically targeted for the study, questions about sexual orientation were included in the survey instrument. Similarly, the federally sponsored National Survey of Family Growth (NSFG) does not specifically target LGBT people but does include questions about sexual orientation identity, behavior, and attraction (Mosher et al., 2005). A further example is the National Survey of Sexual Health and Behavior (Herbenick et al., 2010), which was based on data from an online survey using a cross-sectional sample of U.S. adolescents and adults participating in a Knowledge Networks panel and reported data on the sexual orientation and behavior of participants. Another study using a probability sample of self-identified lesbian, gay, and bisexual participants in the Knowledge Networks panel reported extensive data on demographic, psychological, and social commonalities and differences across sexual orientation subgroups (Herek et al., 2010). Illustrative examples of the study designs and sexual orientation measures used in some of these studies are shown in Box 3-1. Sexual orientation and gender identity measures have also been included in state-level health surveys of probability-based samples, allowing some comparisons with heterosexual counterparts. The Massachusetts Department of Public Health has incorporated these measures into its Behavioral Risk Factor Surveillance System surveys since 2001 (transgender identity question added in 2010). Conron and colleagues (2010) aggregated 2001–2008 data from the Massachusetts Behavioral Risk Factor Surveillance System surveys to examine patterns in self-reported health by sexual orientation identity. The California Health Interview Survey (CHIS), conducted every 2 years, is a population-based random-digit dialing telephone
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding the state of knowledge regarding LGBT health across the life course in the following chapters. Key findings presented in this chapter are listed below. Research Challenges A number of challenges are associated with conducting research on the health status of LGBT populations: The lack of standardized measures in federally funded surveys— Sexual orientation and gender nonconformity are multifaceted concepts, and a variety of methods have been used to identify them for research purposes. Small populations—Since LGBT populations represent a relatively small proportion of the U.S. population, creating a sufficiently large sample to provide reliable estimates of these populations requires considerable resources. A further challenge arises in obtaining a probability sample of LGBT participants that includes sufficient numbers of representatives of population subgroups, such as racial-and ethnic-minority individuals, to permit meaningful analyses. Barriers to identification as LGBT—Because of concerns about stigma and privacy, individuals may be reluctant to answer research questions about their same-sex sexual behavior or gender nonconformity. Sampling Probability sampling allows findings based on the data to be generalized to the study’s target population with a known margin of error. Some methods make it possible to improve the precision of estimates for small populations by combining two or more data sets. Although probability sampling is not used frequently in the study of LGBT health, some studies have obtained probability samples of LGBT participants, while others (such as federal health surveys and the U.S. census) have examined subsets of sexual and gender minorities using probability samples not designed specifically to study those individuals. The majority of studies addressing topics relevant to LGBT health have been conducted using nonprobability samples. Even though the extent to which their findings accurately characterize the entirety of LGBT populations is unknown, studies based on nonprobability samples have yielded valuable information. In addition to providing general descriptive data for LGBT populations and subgroups, they have served to demonstrate the existence of certain phenomena, to test experimentally the effectiveness of various be-
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding havioral and medical interventions, to assess relationships among variables, to identify differences among groups, and in general, to provide insights into the health-related challenges faced by LGBT people. In addition, in the absence of data from probability samples, researchers often develop approximations of population patterns when the findings from multiple methodologically rigorous studies with different nonprobability samples converge. Methods Quantitative data can be collected through a variety of methods, including survey research, RCTs, longitudinal cohort studies, and patient-level data. Of these methods, survey research is particularly common in LGBT health studies, especially as a way to generate demographic data. There are four main sources of error associated with survey research: coverage, nonresponse, measurement, and processing errors (Table 3-1). RCTs measure an intervention’s effects by randomly assigning individuals (or groups of individuals) to an intervention or control group. While these trials are considered the gold standard for measuring an intervention’s impact, the results may not be generalizable to groups other than those who participated in the trials. Longitudinal cohort studies track individuals over time, allowing researchers to observe changes more accurately than is otherwise possible. The NHS and NHSII are examples of longitudinal cohort studies that have made significant contributions to understanding health. Research on LGBT populations using patient-level data is evolving, with discussion ongoing about how to collect sexual orientation and gender identity data in databases. Qualitative data can be collected through a variety of methods, including one-on-one interviews, focus groups, and cognitive interviews. These methods can be especially useful for generating hypotheses and laying the groundwork for future research. Research Opportunities A number of issues related to studying the health status of LGBT populations would benefit from additional research: Federally funded surveys do not measure sexual orientation or gender expression in a uniform and consistent way, limiting the ability to compare data across these surveys.
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The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding The majority of LGBT literature relies exclusively on LGBT respondents, making it difficult to compare characteristics of LGBT populations with those of the entire U.S. population. Research into better methods for recruiting and retaining participants in longitudinal studies is needed. While valuable research has been conducted despite the limitations of available data sources, more national data must be collected if we are to fully understand the health needs of U.S. LGBT populations. Even if LGBT populations can be identified through national surveys, since these populations represent a relatively small proportion of the U.S. population, estimates will be relatively imprecise unless resources are available with which to collect large oversamples of LGBT individuals. Research is necessary on ways to improve the quality and understand the limitations of estimates obtained by combining independent data sets, or by combining direct sample-based estimates with model-based estimates derived from supplemental but related data. Guidelines need to be developed for maximizing the utility of available data through such mechanisms as aggregating data sets over time, adding supplemental samples or oversampling LGBT individuals for ongoing studies, and developing standards for recoding measures across multiple studies to achieve nationally representative data sets. REFERENCES AAPOR (American Association for Public Opinion Research). 2010. Best practices. https://www.aapor.org/Best_Practices.htm (accessed October 22, 2010). Aaron, D. J., Y. F. Chang, N. Markovic, and R. E. LaPorte. 2003. Estimating the lesbian population: A capture-recapture approach. Journal of Epidemiology & Community Health 57(3):207–209. Almeida, J., R. M. Johnson, H. L. Corliss, B. E. Molnar, and D. Azrael. 2009. Emotional distress among LGBT youth: The influence of perceived discrimination based on sexual orientation. Journal of Youth & Adolescence 38(7):1001–1014. Austin, S. B., K. Conron, A. Patel, and N. Freedner. 2007. Making sense of sexual orientation measures: Findings from a cognitive processing study with adolescents on health survey questions. Journal of LGBT Health Research 3(1):55–65. Balsam, K. F., T. P. Beauchaine, E. D. Rothblum, and S. E. Solomon. 2008. Three-year follow-up of same-sex couples who had civil unions in Vermont, same-sex couples not in civil unions, and heterosexual married couples. Developmental Psychology 44(1):102–116. Barrett, K., J. Bradford, and J. Ellis. 2002. Using mapping to facilitate development of a health care infrastructure. Redlands, CA: ESRI Newsletter. Bates, C., C. Droste, L. Cuba, and J. Swingle. 2008. One-on-one interviews: A qualitative assessment approach. Crawfordsville, IN: Center in Inquiry in the Liberal Arts at Wabash College.
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