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The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option. [ABSTRACT FROM AUTHOR]

This qualitative study explores the experiences of men who self-report victimization from a female intimate partner in four English-speaking countries. Forty-one men who reported any type of intimate partner abuse (IPA) from a female partner were recruited via targeted advertising in Australia, Canada, the United Kingdom, and the United States. Twelve online focus groups were conducted across countries using a phenomenologically informed design. Thematic analysis was carried out from an inductive and realist epistemological position and themes identified at a semantic level. This approach was taken to directly reflect the men's experiences and perspectives, ensuring the voices of this hard-to-reach and overlooked population were heard. Three themes were identified across the countries: an imbalanced experience of harm; living with sustained abuse ; and knowledge is power for men experiencing IPA. It was found that most participants underwent physical harm in the context of coercive control and experienced abuse over long periods of time. They were slow to recognize the magnitude of their partners' behavior and act upon it for a range of reasons that are described in detail. In addition, promoting knowledge about the victimization of men by women, using appropriate language and active learning, was found to be important in helping the men gain autonomy and agency to break the pattern of abuse and aid their recovery. The implications of the findings for developing male-friendly IPA policy, practice, and services are discussed, in addition to the need for innovative research methodology to access hard-to-reach populations. [ABSTRACT FROM AUTHOR]

Purpose: Opioid use disorder among women of childbearing age has reached epidemic proportions. In rural regions of the United States, recruiting perinatal women who use nonmedical opioids to participate in research is wrought with challenges, including barriers such as community stigma, lack of transportation, and time constraints. The current study describes our process and challenges of recruiting pregnant and postpartum women in rural Indiana consisting of women who misuse opioids and those who do not. Description: We employed multiple strategies to recruit participants. Methods included (1) sampling from healthcare facilities based on referrals from front-desk staff and frontline healthcare workers; (2) dissemination of flyers and brochures within healthcare facilities and the community, supported with onsite research assistant presence; (3) digital methods coupled with snowball sampling; and (4) local community talks that provided information about the study. Assessment: Our multiple recruitment efforts revealed that building relationships with community stakeholders was key in recruiting women who use nonmedical opioids, but that digital methods were more effective in recruiting a larger sample of pregnant and postpartum women in a short amount of time. Conclusion: We conclude by making several recommendations to enhance academic-community partnerships in order to bolster sample sizes for prolonged research studies. Furthermore, we highlight the need to destigmatize addiction in order to better serve hard-to-reach populations through research and practice. [ABSTRACT FROM AUTHOR]

Although research with hard‐to‐reach populations is necessary to deepen the social work knowledge base and improve services to these groups, recruiting members of hard‐to‐reach populations for research projects is often a challenging process. Frequently, non‐probability sampling is used to obtain participants. However, the difficulties and limitations associated with this process in quantitative research are rarely discussed in depth in the literature. Sampling issues can significantly impact a research project, delaying it, extending it or even causing the premature termination of a project. Challenges to recruitment can limit the type of research that is completed, impact the knowledge base or introduce threats to validity through sampling bias. Using a preliminary quantitative study on parental engagement with child protective services as an illustration for the discussion, the challenges of non‐probability sampling with a hard‐to‐reach population and the implications for research practice are explored and discussed. Implications for future research practice are considered. [ABSTRACT FROM AUTHOR]

Objective. To evaluate the impact of a client-centered behavioral intervention (Brief Negotiated Interviewing) on mothers’ human papillomavirus (HPV) vaccine knowledge and vaccination initiation for their adolescent daughters. Methods. We randomized mothers to intervention (n = 100) and control (n = 100) groups, and followed them over 12 months. Electronic medical records were reviewed to determine vaccination status. The primary outcome was receipt of the first vaccine. The secondary outcome was HPV vaccine knowledge among mothers. Results. Brief Negotiated Interviewing intervention mothers demonstrated increased knowledge about HPV (pre/post mean score of 5 to 10 out of a possible 11; P < .001) and significantly higher mean knowledge scores (10 vs 6, P < .001) than control mothers. However, initiation and completion rates of the vaccine were not significantly different between groups. Conclusions. Increasing HPV vaccine knowledge did not translate into increased vaccine uptake or completion of vaccination series. Future intervention must explore vaccine reminders to increase HPV vaccination rates. [ABSTRACT FROM AUTHOR]

Time-location sampling (TLS), also called time-space sampling or venue-based sampling is a sampling technique widely used in populations at high risk of infectious diseases. The principle is to reach individuals in places and at times where they gather. For example, men who have sex with men meet in gay venues at certain times of the day, and homeless people or drug users come together to take advantage of services provided to them (accommodation, care, meals). The statistical analysis of data coming from TLS surveys has been comprehensively discussed in the literature. Two issues of particular importance are the inclusion or not of sampling weights and how to deal with the frequency of venue attendance (FVA) of individuals during the course of the survey. The objective of this article is to present TLS in the context of sampling theory, to calculate sampling weights and to propose design-based inference taking into account the FVA. The properties of an estimator ignoring the FVA and of the design-based estimator are assessed and contrasted both through a simulation study and using real data from a recent cross-sectional survey conducted in France among drug users. We show that the estimators of prevalence or a total can be strongly biased if the FVA is ignored, while the design-based estimator taking FVA into account is unbiased even when declarative errors occur in the FVA. [ABSTRACT FROM AUTHOR]

The use of personal names for screening is an increasingly popular sampling technique for migrant populations. Although this is often an effective sampling procedure, very little is known about the properties of this method. Based on a large German survey, this article compares characteristics of respondents whose names have been correctly classified as belonging to a migrant population with respondentswho aremigrants and whose names have not been classified as belonging to a migrant population. Although significant differences were found for some variables even with some large effect sizes, the overall bias introduced by name-based sampling (NBS) is small as long as procedures with small false-negative rates are employed. [ABSTRACT FROM AUTHOR]

As part of a large sexual health study, we used two different approaches to target Sexual Minority Individuals (SMIs). Firstly, we drew on a probability sample (1,832 respondents aged 14-80) of the Flemish population in Belgium. Secondly, we set up a targeted sampling design followed by an Internet survey. Our focus was to explore how two different sampling procedures and survey designs could lead to differences in sample characteristics. Results showed that for female SMIs (we excluded male SMIs from the analyses due to their low numbers) the population sample differed from the Internet sample in terms of sociodemographic characteristics (the latter included younger and more highly educated respondents) and scores on sexual orientation dimensions (the population sample included more respondents who didn't identify as lesbian or bisexual but reported same-sex sexual experiences and desire). Respondents' scores on sexual health indicators differed between the samples for two of the seven variables. We discuss implications for improving the quality and validity of nonrandom samples. [ABSTRACT FROM AUTHOR]

Human trafficking has long-lasting implications for the well-being of trafficked people, families, and affected communities. Prevention and intervention efforts, however, have been stymied by a lack of information on the scale and scope of the problem. Because trafficked people are mostly hidden from view, traditional methods of establishing prevalence can be prohibitively expensive in the recruitment, participation, and retention of survey participants. Also, trafficked people are not randomly distributed in the general population. Researchers have therefore begun to apply methods previously used in public health research and other fields on hard-to-reach populations to measure the prevalence of human trafficking. In this topical review, we examine how these prevalence methods used for hard-to-reach populations can be used to measure the prevalence of human trafficking. These methods include network-based approaches, such as respondent-driven sampling and the network scale-up method, and venue-based methods. Respondent-driven sampling is useful, for example, when little information about the trafficked population has been produced and when an adequate sampling frame does not exist. The network scale-up method is unique in that it does not target the hidden population directly. The implications of our work internationally include the need for documenting and validating the various prevalence estimation methods in the United States in a more robust way than was done in existing efforts. In providing this roadmap for estimating the prevalence of human trafficking, our overarching goal is to promote the equitable treatment and overall well-being of the socially disadvantaged populations who disproportionately experience human trafficking. [ABSTRACT FROM AUTHOR]

For studies using respondent driven sampling (RDS), the current practice of collecting a sample twice as large as that used in simple random sampling (SRS) (i.e. design effect of 2.00) may not be sufficient. This paper provides empirical evidence of sample-to-sample variability in design effects using data from nine studies in six countries among injecting drug users, female sex workers, men who have sex with men and male-to-female transgender (MTF) persons. We computed the design effect as the variance under RDS divided by the variance under SRS for a broad range of demographic and behavioral variables in each study. We also estimated several measures for each variable in each study that we hypothesized might be related to design effect: the number of waves needed for equilibrium, homophily, and mean network size. Design effects for all studies ranged from 1.20 to 5.90. Mean design effects among all studies ranged from 1.50 to 3.70. A particularly high design effect was found for employment status (design effect of 5.90) of MTF in Peru. This may be explained by a 'bottleneck'-defined as the occurrence of a relatively small number of recruitment ties between two groups in the population. A design effect of two for RDS studies may not be sufficient. Since the mean design effect across all studies was 2.33, an effect slightly above 2.00 may be adequate; however, an effect closer to 3.00 or 4.00 might be more appropriate. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to overcome the challenges in accessing hired farm workers with the use of the Mexican Immigration to California: Agricultural Safety and Acculturation (MICASA) study sample. It describes the MICASA study which investigates the occupational and environmental health risks in the households of farm workers in Mendota, California. It compares the results of the MICASA study with the findings of the California Health Interview Survey (CHIS).

Obtaining representative samples from populations of emerging adults who do not attend college is challenging for researchers. This article introduces respondent-driven sampling (RDS), a method for obtaining representative samples of hard-to-reach but socially interconnected populations. RDS combines a prescribed method for chain referral with a mathematical model to adjust for potential sampling bias. A case study is presented of the use of RDS to obtain a sample of rural African American emerging adults and to document the feasibility, effectiveness, and efficiency of the method with this population. RDS-related findings indicate that rural African American emerging adults form interconnected social networks that are appropriate for using RDS. RDS procedures are an acceptable and relatively efficient strategy for rural African Americans that is likely to yield more representative samples than traditional snowball sampling and provides data on potential sampling biases for planning multivariate analyses. [ABSTRACT FROM AUTHOR]

The article discusses the application of time-space sampling (TSS) and respondent-driven sampling (RDS) on hard-to-reach people. TSS is used for tallying respondents at particular places and times while RDS involves social networks of respondents for expanded data gathering. Both data collection through TSS and RDS in human immuno virus-associated projects have proven that both techniques are effective in gathering information when applied with actions including correct planning, observation, and evaluation of the sampling strategy.

The article explains various research methods for hard-to-reach people, which are time-location sampling (TLS), respondent driven sampling (RDS), and capture-recapture technique. Hard-to-reach people are being tallied for social issues including homelessness, acquired immuno deficiency syndrome epidemic, and challenges of children of immigrants. TLS is used for counting drug users, while RDS is applied for weighing statistical data, and capture-recapture technique is utilized for tallying homeless people.

Background: Web-based respondent-driven sampling is a novel sampling method for the recruitment of participants for generating population estimates, studying social network characteristics, and delivering health interventions. However, the application, barriers and facilitators, and recruitment performance of web-based respondent-driven sampling have not yet been systematically investigated.Objective: Our objectives were to provide an overview of published research using web-based respondent-driven sampling and to investigate factors related to the recruitment performance of web-based respondent-driven sampling.Methods: We conducted a scoping review on web-based respondent-driven sampling studies published between 2000 and 2019. We used the process evaluation of complex interventions framework to gain insights into how web-based respondent-driven sampling was implemented, what mechanisms of impact drove recruitment, what the role of context was in the study, and how these components together influenced the recruitment performance of web-based respondent-driven sampling.Results: We included 18 studies from 8 countries (high- and low-middle income countries), in which web-based respondent-driven sampling was used for making population estimates (n=12), studying social network characteristics (n=3), and delivering health-related interventions (n=3). Studies used web-based respondent-driven sampling to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the number of seeds recruited, the proportion of successfully recruiting participants, the number of recruitment waves, the type of incentives offered to participants, and the duration of data collection. Studies that recruited relatively more seeds, through online platforms, and with less rigorous selection procedures reported relatively low percentages of successfully recruiting seeds. Studies that did not offer at least one guaranteed material incentive reported relatively fewer waves and lower percentages of successfully recruiting participants. The time of data collection was shortest in studies with university students.Conclusions: Web-based respondent-driven sampling can be successfully applied to recruit individuals for making population estimates, studying social network characteristics, and delivering health interventions. In general, seed and peer recruitment may be enhanced by rigorously selecting and motivating seeds, offering at least one guaranteed material incentive, and facilitating adequate recruitment options regarding the target population's online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing web-based respondent-driven sampling, such as having less opportunities to implement rigorous seed selection procedures when recruiting many seeds, as well as issues around online rather than physical participation, such as the risk of cheaters participating repeatedly. [ABSTRACT FROM AUTHOR]