factors associated with participation in the ... - Oxford Academic

Public Opinion Quarterly, Vol. 81, No. 1, Spring 2017, pp. 57–85

FACTORS ASSOCIATED WITH PARTICIPATION IN THE COLLECTION OF SALIVA SAMPLES BY MAIL IN A SURVEY OF OLDER ADULTS JENNIFER DYKEMA* KERRYANN DiLORETO KENNETH D. CROES DANA GARBARSKI JEREMY BEACH

Abstract While biomeasures are increasingly collected along with survey responses in household surveys, more research is needed to determine respondents’ willingness to provide these measures, what factors influence participation, and what barriers impede successful collection. In this paper, we examine factors associated with compliance to a request to provide a salivary DNA sample as part of a separate data-collection effort of the Wisconsin Longitudinal Study, in which respondents have been periodically surveyed since they were high school seniors in 1957. Respondents were contacted in 2007, and over 50 percent provided a Jennifer Dykema is a senior scientist and survey methodologist at the University of Wisconsin Survey Center, Madison, WI, USA. Kerryann DiLoreto and Kenneth D. Croes are senior project directors at the University of Wisconsin Survey Center, Madison, WI, USA. Dana Garbarski is an assistant professor in the Department of Sociology at Loyola University, Chicago, IL, USA. Jeremy Beach is a survey methodologist at USDA-NASS, Washington, DC, USA. Authorship is shared equally. This research uses data from the Wisconsin Longitudinal Study (WLS) of the University of Wisconsin–Madison. Since 1991, the WLS has been supported principally by the National Institute on Aging [R01 AG-009775, P01 AG-021079, and R01 AG-033285 to Pamela Herd], with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundation, and the Graduate School of the University of Wisconsin–Madison. Since 1992, data have been collected by the University of Wisconsin Survey Center. A public-use file of data from the Wisconsin Longitudinal Study is available from the Wisconsin Longitudinal Study, University of Wisconsin–Madison, 1180 Observatory Drive, Madison, WI 53706. USA, and at http://www.ssc.wisc.edu/wlsresearch/data/. Research support was provided by the University of Wisconsin Survey Center (UWSC), which receives support from the College of Letters and Science at the University of Wisconsin–Madison. The authors thank Nora Cate Schaeffer, John Stevenson, Steven Blixt, and Jon Miller for helpful comments and suggestions on earlier drafts, and Nadia Assad, Augie Salick, and Eric White for help with data processing. The opinions expressed herein are those of the authors. *Address correspondence to Jennifer Dykema, University of Wisconsin Survey Center, University of Wisconsin–Madison, 475 N. Charter Street, Room 4308, Madison, WI 53706, USA; e-mail: [email protected]. doi:10.1093/poq/nfw045 Advance Access publication October 31, 2016 © The Author 2016. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please e-mail: [email protected]

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Dykema et al. saliva sample, which was self-administered and returned through the mail. We examine factors associated with returning the saliva sample in this panel survey, including respondents’ socio-demographic characteristics, cognitive ability, health, religiosity, and survey process variables such as amount of prior participation in the WLS. Bivariate results indicate that the odds of participation are lower among females, those with less education, those who are more socially isolated, those with lower cognitive ability, those in poorer health and with less contact with the healthcare system, those who are more religious, and those whose past behavior indicated resistance to participation. Many of these effects, however, are attenuated when controlling for all the variables simultaneously. Our analysis adds to the small but growing body of research on factors influencing the collection of biological measures in household surveys, particularly household panel surveys. Our conclusions include recommendations for maximizing participation rates and reducing costs.

Introduction Biomeasures, such as saliva and blood, are increasingly collected alongside survey responses in household surveys in order to gather information on the genetic and biological determinants of health (Sakshaug et al. 2015). Although the promise of collecting these measures in surveys is rich (Hauser and Weir 2010), there are barriers to obtaining representative and high-quality data, including nonresponse bias that may result if different groups, such as those in poorer health, participate at lower levels than those in better health; ensuring that respondents follow directions and understand the nature of the task; technological hurdles; and increased costs (Beebe 2007; Weinstein, Vaupel, and Wachter 2007). In order to develop methods to increase participation, and to understand the potential for nonresponse bias, we need to determine which characteristics of respondents are associated with successful participation and which reduce response. A critical problem in identifying variables that are correlated with successful participation in surveys is that information on nonparticipants is typically unknown (Groves and Couper 1998). Longitudinal survey designs, however, offer the ability to use data from previous waves of data collection to predict response to later requests (Lepkowski and Couper 2002; Radler and Ryff 2010). Using data from the highly successful Wisconsin Longitudinal Study (WLS) (Sewell et al. 2003), for which data were first collected from a sample of high school seniors in 1957, we examine variables associated with a request to provide saliva in 2007, approximately fifty years later. TASK CHARACTERISTICS OF COLLECTING BIOMEASURES

Figure 1 presents a conceptual model illustrating factors that influence respondents’ decisions to provide biological measures, including characteristics of

Correlates of Providing Saliva Samples

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Figure 1. Conceptual Model of Factors Influencing a Respondent’s Decision to Provide a Biomeasure. the task, characteristics of respondents, and participation in the survey process. Task-based characteristics are particularly important to understanding participation and encompass such varied dimensions as 1) respondents’ understanding of data-collection procedures, 2) methods for recruitment, 3) informed-consent language and processes (e.g., how measures will be used, such as for genetic testing), 4) the kind of biomeasure sought (e.g., saliva, physical measurements), 5) the invasiveness of the method of measurement, 6) the setting for measurement (e.g., respondent’s home, clinic), 7) who is responsible for taking the measurement (e.g., respondents themselves, respondents with assistance, another person), 8) the person’s qualifications if the measurement is to be completed by another person (e.g., medical doctor, nurse, field interviewer), 9) and other survey-based characteristics (e.g., use of incentives). Sakshaug and colleagues (2015) provide an overview of the methodology employed by several studies that vary with regard to their task-based characteristics. For example, studies with strict protocols, such as for processing specimens, tend to occur at centralized facilities (Harris, Gruenewald, and Seeman 2007). Midlife in the United States (MIDUS), for instance, employed medically trained personnel working in clinical settings to collect biological measures and conduct physical examinations (Love et al. 2010). Biomeasures collected in clinics are then combined with self-reported responses gathered during in-person interviews, or by phone or mail. Studies that use centralized

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collection sites are limited in their ability to recruit participants from a wide geographical area and place the burden of travel on study participants, a barrier that studies may offset with incentives. When geographical dispersion is central to the researcher’s objectives, the clinic may be made mobile. The National Health and Nutrition Examination Survey (NHANES) is perhaps the foremost example of this method. In recent years, several prominent US data-collection efforts have forgone the clinic and opted to collect biomeasures in person and in study participants’ homes. While some use medically trained staff (e.g., the National Long-Term Care Survey, Research Triangle International 2002); other in-home efforts rely on specially trained interviewing staff. The Health and Retirement Study (HRS) employs interviewers who collect saliva, blood, and anthropometric measures (e.g., height), and conduct physical performance assessments (Weir 2007) (see too the National Social Life, Health, and Aging Project; Jaszczak, Lundeen, and Smith [2009]). The cost of in-home collection, however, is out of reach for many data-collection efforts (Dykema, Basson, and Schaeffer 2007). A more cost-effective alternative for collecting DNA is to have respondents provide a saliva sample through the mail (e.g., Etter et al. 2005; Hansen et al. 2007; see review in Gatny, Couper, and Axinn [2013]). For example, 42 percent of respondents from a large-scale probability sample returned a saliva sample by mail when requested at the conclusion of a telephone survey (Kilpatrick et al. 2007). Although there have been some larger-scale epidemiologic efforts to collect salivary DNA via the mail (e.g., Ness et al. 2010; Rylander-Rudqvist et al. 2006; Uusküla, Kals, and McNutt 2011), many studies that employ this methodology have used small samples with specialized populations (Amstadter et al. 2010; Fix et al. 2010; Le Marchand et al. 2001). More research is needed to determine if this methodology can yield high response rates from diverse populations. RESPONDENT CHARACTERISTICS ASSOCIATED WITH PROVIDING BIOMEASURES

To isolate respondent and survey characteristics with the potential to predict when respondents might be more likely to provide a biomeasure like saliva through the mail, we reviewed the literature on patterns of participation in household surveys that use probability sampling (e.g., Groves and Couper 1998), including the few (published) studies in which biological measures have been collected along with survey responses (e.g., Jaszczak, Lundeen, and Smith 2009; Sakshaug, Couper, and Ofstedal 2010). We also examined the literature on factors associated with collecting biological measures in clinical settings using volunteer or quota samples (e.g., Henderson et al. 2008). Our review highlighted the importance of variables that describe characteristics of the respondent—such as socio-demographic characteristics, cognitive ability,


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health status, and religiosity—and variables related to the respondent’s prior and current participation in the survey. Socio-demographic characteristics: With regard to respondents’ sociodemographic characteristics, we identified several potentially important covariates, including gender, education, household composition, and social participation (we do not examine age or race due to insufficient variation in the WLS sample; income is excluded because of high levels of missing data). Based on the literature on nonresponse in household surveys, which shows a positive relationship between education and participation, we predicted a higher rate of return of saliva samples from better-educated respondents. We predicted a lower rate of return among respondents who live alone, and who are hypothesized to be more socially isolated and less likely to engage in a social exchange like participating in a survey. Similarly, we expected a lower rate of return among respondents with less involvement in social organizations. We were less certain about how women and men would differ in their levels of participation. While household surveys and the WLS in particular show a trend for women to participate at higher levels (Hauser 2005), findings from probability surveys in which biological samples have been collected are mixed. No gender differences were reported in the collection of saliva samples during personal interviews with elderly respondents in NSHAP (Gavrilova and Lindau 2009) or in HRS (Sakshaug, Couper, and Ofstedal 2010), or in clinical studies that have examined participation in the collection of biological samples for genetic research (Chen et al. 2005; Malone et al. 2002). Similarly, no significant differences by gender were found in a population-based survey of attitudes toward the collection and storage of biological specimens for future genetic research (Wang et al. 2001). However, McQuillan, Pan, and Porter (2006) reported a trend, for NHANES, in which women were less likely to consent when they were told the specimen would be used for later genetic research. Health status: Of major concern to researchers collecting biological measures is the potential for nonresponse bias based on respondents’ health. If respondents in poorer health—who are less likely to respond to surveys than those in better health—are similarly less likely to provide biomeasures, results from studies that combine self-reports with biological data could underestimate poor health. In their analysis of factors associated with panel respondents consenting to provide physical measurements, saliva, and blood, Sakshaug, Couper, and Ofstedal (2010) found that only a few of the indicators related to health status were significantly associated with consent after controlling for characteristics of the interview (e.g., number of contact attempts). Respondents with diabetes and those who visited the doctor one or more times in the past two years were more likely to consent, while respondents who were less active and had more

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limitations were less likely to consent; self-rated health status, high blood pressure, cancer, lung disease, heart condition, stroke, arthritis, and BMI were not associated with the propensity to consent. The likelihood of consenting for respondents with diabetes was thought to be due to their familiarity with testing and an interest in knowing the results; similar tendencies may apply to respondents who recently visited a doctor. Self-rated health also was not associated with participation for saliva (Gavrilova and Lindau 2009) or vaginal self-swabs (Lindau et al. 2009). In their analysis of the likelihood of cancer survivors and their siblings to return biological samples through the mail, Ness et al. (2010) found that those who had never smoked and who had had recent contact with a healthcare provider were more likely to return a sample than smokers and those who had not had recent contact with a provider. Survivors who perceived their health as “excellent” or “good” compared to “fair” or “poor” were only slightly more likely to return a sample, with no effect for siblings. However, measures of nicotine dependence (Fix et al. 2010) and smoking status (Le Marchand et al. 2001) were found to have no effect on the propensity to return saliva samples by mail in two epidemiological cohort studies. In our examination of whether the provision of saliva is associated with health characteristics of respondents, we examine several pertinent measures of health status, including self-rated health, health-related quality of life, presence of chronic conditions, measures of healthcare utilization, smoking status, and health insurance coverage. We predicted that respondents in poorer health and with less contact with the healthcare system would be less likely to participate. Cognitive ability: While the role of cognitive ability has been of interest to survey researchers in understanding how respondents process survey questions (e.g., Krosnick 1991; Tourangeau, Rips, and Rasinski 2000), few studies have examined the relationship between respondents’ cognitive abilities and participation in surveys or survey-related tasks; instead, research has used education as a proxy for ability. However, there is reason to believe that cognitive ability interacts with the nature of the task such that for more complicated survey requests, participation will be lower. For example, in his analysis of differential response rates over time in the WLS, Hauser (2005) examined the association between respondents’ IQ scores (measured in high school) and their participation in the 1975 and 1992 waves of the longitudinal survey (conducted roughly nineteen and thirty-six years later, respectively). He found a gradient in which response rates were substantially lower among respondents with the lowest IQ scores. This differential was more pronounced for data collected by mail than by phone, the former being arguably a more difficult cognitive task. Coupling findings from the WLS with Beebe’s (2007) speculation that respondents’ motivation and ability to follow instructions are


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likely barriers to participation in the collection of biospecimens, we predicted lower rates of participation among respondents with lower IQ scores. Religiosity: Studies conducted on specialized populations in medical and epidemiological contexts indicate that the relationship between religiosity and participation in genetic and biological research warrants examination. Advani et al.’s (2003) survey of patients diagnosed with cancer found that those who believed that “God would determine whether or not they would be cured or die from their cancer” (1503) had lower odds of being willing to participate in an oncological clinical trial. Schwartz et al. (2000) reported that those who rated their spirituality as “very strong” had lower odds of using genetic testing than those who said their spirituality was less strong. Similarly, Singer, Corning, and Lamias (1998), in a review of population-based studies on attitudes about genetic testing and engineering, cite a NORC survey that reported that “67 percent of those who never attend church would want a prenatal test for themselves or their spouse, compared with 51 percent of those who attend church once a week or more often” (638). Finally, a survey of participants in a genetic epidemiological study that attempted to assess participants’ attitudes toward genetic research in general and the likelihood that they would participate in genetic research again (Henderson et al. 2008) found that those who called themselves “not religious” were significantly more likely than those who called themselves “somewhat” and “very” religious to be very positive about genetic research and very likely to participate again. Based on this prior research, we expected that those whose religiosity measures are high would be less likely to participate in the salivary DNA collection. SURVEY PROCESS CHARACTERISTICS ASSOCIATED WITH PROVIDING BIOMEASURES

Increasingly, researchers use paradata—information about the process of data collection (Couper 2005)—to understand the response process and to make adjustments for potential nonresponse bias. This is of particular importance within panel surveys when factors such as the frequency of data collection and the time between data collections, as well as the number of waves and the type of respondent being followed, may influence nonresponse (Lepkowski and Couper 2002). By assessing prior wave experiences and the corresponding likelihood of a respondent’s willingness to cooperate, researchers can evaluate the propensity of cooperation in subsequent waves. Past predictors of nonresponse have included the respondent’s level of effort, reluctance, and busyness (Fricker and Tourangeau 2010), and willingness to cooperate and residential moves (Lepkowski and Couper 2002). Lepkowski and Couper (2002) found that levels of survey experience (i.e., understanding of questions and cooperation with and “enjoyment” of the interview) in a prior wave increased cooperation propensity in a future wave, while “reluctant behavior” and comments

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suggesting the respondent was “too busy” during a prior wave decreased subsequent cooperation (see also Campanelli, Sturgis, and Purdon [1997]; Groves and Couper [1996]). Fricker and Tourangeau (2010) also identified that unit nonresponse in a current wave is more likely for respondents who reported having worked more hours in a previous wave, when compared to those who reported less, as a proxy measure for busyness. Sakshaug, Couper, and Ofstedal (2010) found several significant associations between measures of resistance toward the survey interview and biomeasure consent, with decreased odds of consenting for increased number of contact attempts in previous waves, among respondents who “often” asked how long the interview would last or “seldom” or “often” asked about confidentiality, and among respondents with lower ratings on their cooperativeness and enjoyment of the interview. Gatny, Couper, and Axinn (2013) reported that women in a mixed-mode Internet/phone panel with below-average levels of prior participation were less likely to comply with a request for saliva. We predicted that measures indicating incomplete involvement in previous waves of the WLS and other indicators of resistance to participating within a given wave of data collection would be negatively associated with returning a saliva sample.

Methods DATA

Data were provided by the Wisconsin Longitudinal Study (WLS), a longitudinal study of 10,317 randomly selected respondents who graduated from Wisconsin high schools in 1957 (Herd, Carr, and Roan 2014; Sewell et al. 2003). Since 1957, follow-up interviews have been conducted with graduates or their parents in 1964, 1975, 1992, 2003–2005 (phone with mail follow-up), and 2011. The study covers a variety of topics, with a focus on educational plans, occupational aspirations, social influences, and, more recently, physical and mental health. A high response rate characterizes all waves of survey data collection. Methodological interest and technological capability motivated the collection of salivary DNA. Because most WLS sample members had reached retirement (~68 years of age), the study provided a unique opportunity to examine the relationships among genetic factors, personality traits, health, and the myriad socio-demographic variables collected since 1957 (Hauser and Weir 2010). Technologically, DNA could be collected in a cost-effective and high-quality manner by mailing sample members inexpensive saliva self-collection kits. Procedures for data collection were informed by a pilot study that manipulated the number and type of contacts (e.g., by including an advance letter and advance phone call) with a subsample of potential participants (DiLoreto and Croes 2007). The procedures reported here and summarized in table 1


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mirror the experimental condition that yielded the highest participation rate in the pilot. In 2007, 8,081 eligible sample members, including those who were nonparticipants from earlier waves of data collection, were selected to participate in the collection of salivary DNA (members were excluded from saliva collection if they were deceased prior to or during the field period [n = 1,521], included in the pilot study [n = 400], not able to be located during tracing [n = 292], or had requested no further contact prior to the saliva collection effort [n = 23]). Between January and March, a DNA Genotek Inc.’s OrageneTM DNA self-collection kit (OGR-250) was mailed to each sample member who did not refuse during the advance calling and for whom an address was available. Kits consisted of a cover letter explaining that salivary DNA was being collected to “study the relationship of genes to health and well-being, including Alzheimer’s disease, cancer, and depression”; two identical consent forms (one to be signed and returned, the other for the sample member’s records) (see online appendix for copies of the cover letter and consent form); a sealable plastic bag with cloth to absorb leakage; a saliva self-collection kit; a selfaddressed, first-class-stamped return envelope; and a sheet with written and illustrated instructions on how to use the kit. The consent form did not specify how the DNA sample would be used, but left open the possibility of testing for other conditions or purposes. Participants were not provided with a termination date for their data, but were assured that their data would be securely stored and de-identified. Participants were explicitly told that they would not be privy to their individual results. Saliva kits and signed consent forms were returned by 4,365 of the 8,081 graduates included in the production phase of the saliva-collection effort, for an overall participation rate of 54.0 percent. Nonparticipation resulted if sample members (or other householders) refused during the advance calling or reminder calling (n = 1,182), failed to return kits sent to them (n = 1,869), mailed back unopened kits (n = 648), returned a kit without a signed consent form (n = 14), or (one respondent) returned a kit and later asked for the materials to be destroyed. Including health and religiosity variables restricted the analytic sample to cases that were among the 80 percent randomly selected to receive the module on religion in the telephone survey and completed both the 2003–2005 WLS telephone and mail surveys (n = 4,586). Saliva kits were returned by 66.7 percent of these respondents (n = 3,060), a higher rate than for the full sample described above. Because the participation rate for our final analytic sample was higher than for the full sample that includes all 8,081 graduates, we ran three sets of multivariate logistic regression models to determine whether the association between providing the saliva sample and the independent variables differed for the final analytic sample compared to other WLS sample members. Model A included the variables available for all 8,081 graduates (i.e., gender, educational attainment, cognitive ability, prior participation, call attempts

All eligible sample members

Sample members successfully contacted by phone

Sample members sent saliva kit

Nonresponding sample members

01/22/2007–02/27/2007

01/25/2007–03/02/2007

01/29/2007–03/05/2007

02/04/2007–03/23/2007

Telephone

Postal mail

Postal mail

Telephone

Method of contact

• Phone call to remind nonresponders, respond to questions/concerns/ reluctance, and inquire about the need to resend a saliva-collection kit

• Reminder postcard

• Saliva-collection kit with instruction • Consent form • $5 incentive

• Phone calls (maximum of 7 made over 35 days) notifying sample members of the upcoming mailing of saliva-donation kits (see online appendix for the script used by the telephone interviewers) • Sample members prioritized based on levels of prior participation in the WLS such that those with higher levels were called first • If the sample member was not reached during the initial call, callers left a message identifying the sponsoring organization; if the sample member was not reached during the final call, callers left a message stating the purpose of the call and providing a callback number.

Nature of contact

Note.—Sample members were considered ineligible if they were included in a pilot study to test the methodology, were deceased prior to or died during the field period, were not able to be located during tracing, or had requested no further contact prior to the saliva-collection effort.

Subsample

Approximate date

Table 1. Overview of Saliva Collection Methodology, Wisconsin Longitudinal Study

66 Dykema et al.


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in 2003–2005, refused in a 2003–2005 call), and the results were compared across the sample of 8,081 graduates versus the final analytic sample. Model B included the variables in model A and the additional variables available for cases participating in the 2003–2005 phone survey (i.e., household composition, self-rated health, HUI, chronic conditions, any private health insurance); the results were compared for graduates participating in the phone survey versus the final analytic sample. Model C included the variables from models A and B and the additional variables available for cases completing both the 2003–2005 phone and mail survey (i.e., physical exam, dental exam, current smoker); results were compared for graduates participating in the phone and mail survey versus the final analytic sample. Results (available upon request) demonstrated that in terms of the direction, size, and significance of the coefficients, the associations reported here for the analytic sample are stable. MEASURES

Our dependent variable is dichotomous and coded “1” (versus “0”) if the respondent returned a saliva kit. Independent variables used in the analysis were obtained at different phases of data collection for the study as a whole. The online appendix provides the exact wording of the questions used in the analysis and the wave and module in which the question was administered (see too http://www.ssc.wisc.edu/wlsresearch/.) Socio-demographic characteristics include gender, educational attainment (measured in years of schooling and collapsed into three categories for “high school graduate,” which includes high school graduates and those with less than one year of college, “some college,” and “college degree or more”), and from the 2003–2005 wave of data collection, an indicator for whether the respondent lived alone or with more than one person (gathered during the administration of the household roster) and a measure of social participation. This latter variable is categorized in terms of the number of organizations (e.g., “professional groups,” “charity or welfare organizations”) in which the respondent had at least “some” involvement (ranging from zero to three or more out of a list of fifteen). Cognitive ability is indicated by the respondent’s IQ score, assessed while the respondent was in high school. Measures of health are based on self-reports to questions in the 2003–2005 surveys. They assess physical functioning, chronic conditions, healthcare utilization, health behaviors, and access to healthcare. Respondents were asked to provide a global self-rating of their health using the standard five-point scale of “excellent,” “very good,” “good,” “fair,” and “poor,” which has been shown to be an important predictor of mortality (Idler and Benyamini 1997). Summary scores from the Health Utilities Index Mark 3 (HUI3; Horsman et al. 2003) measure health-related quality of life across the domains of vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain, with higher scores indicating better health-related quality of life (we use the WLS’s best

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measure for the HUI3, in which “don’t know” responses to individual questions are imputed as “no” responses). An index of the prevalence of chronic conditions, including high blood pressure, diabetes, cancer or a malignant tumor (excluding minor skin cancers), heart problem (e.g., heart attack, coronary heart disease, angina, congestive heart failure), stroke, and arthritis or rheumatism, is constructed. Questions in the series are phrased to ask, “has a doctor ever told you that you have,” and “don’t know” responses are imputed as “no” responses. The analysis compares respondents with one to two or three or more conditions to those reporting never having any conditions. Two indicators of healthcare utilization—whether the respondent had a physical exam or dental exam in the twelve months preceding the survey—are coded to indicate no use. We include an indicator for whether the respondent was a smoker or had any private health insurance prior to collection of the saliva kit. Frequency of religious attendance was assessed by asking respondents how often they attended religious services during the past year. Responses are collapsed into four categories: “never,” “less than weekly” (those attending 1–44 times per year), “weekly” (those attending 45–78 times per year), and “more than weekly” (those attending 79–730 times per year). Respondents’ agreement with whether the “Bible is God’s word” is indicated by the categories “strongly agree,” “agree,” “neither agree nor disagree,” and “disagree or strongly disagree.” Survey process variables are used to examine whether features of the respondent’s level of participation in prior and current waves of data collection affect returning a saliva kit. The variable for incomplete participation indicates whether the respondent did not participate in one or more prior waves of data collection for the WLS or for a given wave if the respondent failed to complete part of the wave (e.g., a respondent is coded as an incomplete participator if they participated in the telephone portion of a prior wave but did not complete the mail survey follow-up). We also examine two features of the 2003–2005 telephone-calling phase that were associated with lower propensity to participate in that wave of data collection and have the potential to signal resistance with subsequent requests. Our first measure is the number of telephone calls made to the respondent’s household (which range in value from 1 to 137 and are top-coded at 15). A second measure captures “refusal conversions”— whether the respondent or anyone in the respondent’s household refused to participate, but the respondent later agreed to participate.

Results The first three columns in table 2 show the distribution of respondents and nonrespondents over categories of the given survey predictor (for scales


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measured as continuous variables, we collapse values of the predictor into levels and show the mean level of the variable within a given level). The fifth column provides the participation rate, and is useful for survey practitioners in evaluating the potential impact of a variable on participation: because the sample size for this study is large, many of the variables have statistically significant effects, but these effects do not always correspond to large differences in participation rates among groups. Table 3 presents the results from bivariate and multivariate logistic regression models of returning the saliva kit on the various predictors. The models employ complete case analysis; however, we also conducted the analyses with multiply imputed data, and the results (available upon request) were substantively the same. The first column in table 3 presents the results from bivariate logistic regression models to examine the effect of each survey predictor on its own and before controlling for other indicators. With respect to socio-demographic characteristics, being a woman and living alone are each associated with decreased odds of returning a saliva kit, while having a college degree or more (vs. a high school degree) and having at least “some” participation in one or more social organizations (vs. no participation) are associated with increased odds of returning a saliva kit. A one-unit increase in adolescent IQ, our measure for cognitive ability, is associated with a 1 percent increase in the odds of returning a saliva kit, consistent with Hauser’s (2005) analysis of IQ and response rates in the WLS surveys. In the bivariate models, many of the indicators for health status are in the predicted direction of poorer health and low healthcare utilization being associated with decreased odds of providing a saliva sample. Each category of self-rated health (vs. “excellent”) is associated with decreased odds of providing a saliva sample; these are statistically significant for “good” and “poor” health. Having three or more chronic conditions is associated with decreased odds of providing a saliva sample. Smoking did not show statistically significant effects. Low healthcare utilization, as indicated by not having been to the doctor or dentist in the year prior to the 2003–2005 survey, is associated with decreased odds of providing a saliva sample, as is not having private health insurance. Each one-unit increase in the HUI score, a summary measure in which a higher score indicates better health-related quality of life, is associated with increased odds of providing a saliva sample. We examine two sets of indicators for religiosity. In the bivariate logistic regression of saliva sample provision on religious attendance, low and high religious attendance are associated with decreased odds of providing a saliva sample compared with respondents who report “about weekly” religious attendance. Relative to those who disagree or strongly disagree with the statement that the “Bible is God’s word,” strongly agreeing with or displaying neutrality about this statement is associated with decreased odds of providing a saliva sample.

Socio-demographic characteristics Gender Male Female Educational attainment High school graduate Some college College degree or more (Missing N) Household composition Lives with at least one other Lives alone Social participation No organizations 1 organization 2 organizations 3 or more organizations (Missing N)

Survey predictors

45.5 54.5 61.4 13.1 25.5

83.3 16.7 40.1 24.4 17.2 18.3

2,817 599 1,169 (1)

3,821 765

1,800 1,092 773 820 (101)

%

2,085 2,501

N

Total

1,140 762 546 569 (43)

2,598 462

1,800 390 869 (1)

1,443 1,617

N

37.8 25.3 18.1 18.9

84.9 15.1

58.8 12.8 28.4

47.2 52.8

%

Graduates who returned sample (n = 3,060)

660 330 227 251 (58)

1,223 303

1,017 209 300

642 884

N

45.0 22.5 15.5 17.1

80.1 19.9

66.6 13.7 19.7

42.1 57.9

%

Graduates who did not return sample (n = 1,526)

0.000

0.000

0.000

0.001

Chi-square p-value

Continued

63.3 69.8 70.6 69.4

68.0 60.4

63.9 65.1 74.3

69.2 64.7

Participation rate

Table 2. Summary Information for Survey Predictors, WLS Graduate Respondents Who Participated in the 2003–2005 Phone and Mail Surveys and Were in the Religion Module

70 Dykema et al.

Cognitive ability Adolescent IQ in deciles 61–83 84–91 92–94 95–98 99–102 103–106 107–109 110–115 116–121 122–145 Health status Self-rated health Excellent Very good Good Fair

Survey predictors

Table 2. Continued

10.5 12.0 8.5 10.0 10.5 10.3 9.8 10.4 8.2 9.8

26.1 38.8 26.6 6.9

1,195 1,777 1,221 314

%

481 551 391 460 482 472 447 479 376 447

N

Total

842 1,196 776 205

292 354 253 292 295 328 314 335 261 336

N

27.5 39.1 25.4 6.7

9.5 11.6 8.3 9.5 9.6 10.7 10.3 11.0 8.5 11.0

%


353 581 445 109

189 197 138 168 187 144 133 144 115 111

N

23.2 38.1 29.2 7.2

12.4 12.9 9.0 11.0 12.3 9.4 8.7 9.4 7.5 7.3

%


0.000

0.000

Chi-square p-value

Continued

70.5 67.3 63.6 65.3

60.7 64.3 64.7 63.5 61.2 69.5 70.3 69.9 69.4 75.2

Participation rate

Correlates of Providing Saliva Samples 71

1.7

1.0 1.0 1.7 2.4 1.9 2.8 7.8 9.3 14.2 49.1 8.7

25.0 61.2

78 (1)

44 44 77 112 89 130 359 428 652 2,253 397 (1)

1,147 2,806

Poor (Missing N) Health utilities index (HUI) less than .1 .1–less than .2 .2–less than .3 .3–less than .4 .4–less than .5 .5–less than .6 .6–less than .7 .7–less than .8 .8–less than .9 .9–less than 1.0 1.0 (Missing N) Chronic conditions index 0 conditions 1 to 2 conditions

%

N

Total

Survey predictors

Table 2. Continued

754 1,924

31 22 44 65 57 79 230 279 450 1,533 269 (1)

41

N

24.6 62.9

1.0 0.7 1.4 2.1 1.9 2.6 7.5 9.1 14.7 50.1 8.8

1.3

%


393 882

13 22 33 47 32 51 129 149 202 720 128

37 (1)

N

25.8 58.0

0.9 1.4 2.2 3.1 2.1 3.3 8.5 9.8 13.2 47.2 8.4

2.4

%


0.001

0.027

Chi-square p-value

Continued

65.7 68.6

70.5 50.0 57.1 58.0 64.0 60.8 64.1 65.2 69.0 68.0 67.8

52.6

Participation rate

72 Dykema et al.

13.7

76.5 23.5

79.8 20.2

88.3 11.7

85.9 14.1

629 (4)

3,450 1,058 (78)

3,602 910 (74)

3,993 530 (63)

3,875 637 (74)

3 conditions or more (Missing N) Physical exam Yes No (Missing N) Dental exam Yes No (Missing N) Current smoker No Yes (Missing N) Any private health insurance Yes No (Missing N)

%

N

Total

Survey predictors

Table 2. Continued

2,645 391 (24)

2,689 343 (28)

2,470 545 (45)

2,355 659 (46)

382

N

87.1 12.9

88.7 11.3

81.9 18.1

78.1 21.9

12.5

%


1,230 246 (50)

1,304 187 (35)

1,132 365 (29)

1,095 399 (32)

247 (4)

N

83.3 16.7

87.5 12.5

75.6 24.4

73.3 26.7

16.2

%


0.001

0.227

0.000

0.000

Chi-square p-value

Continued

68.3 61.4

67.3 64.7

68.6 59.9

68.3 62.3

60.7

Participation rate


Religiosity Religious attendance Never Less than weekly About weekly More than weekly (Missing N) “Bible is God’s word” Strongly agree Agree Neither agree nor disagree Disagree or strongly disagree (Missing N) Survey process variables Prior participation Complete Incomplete

Survey predictors

Table 2. Continued

14.6 35.2 42.1 8.1

20.0 22.2 33.6 24.3

73.4 26.6

888 988 1,493 1,081 (136)

3,368 1,218

%

652 1,579 1,886 365 (104)

N

Total

2,396 664

564 677 980 774 (65)

411 1,050 1,333 229 (37)

N

78.3 21.7

18.8 22.6 32.7 25.8

13.6 34.7 44.1 7.6

%


972 554

324 311 513 307 (71)

241 529 553 136 (67)

N

63.7 36.3

22.3 21.4 35.3 21.1

16.5 36.3 37.9 9.3

%


0.000

0.001

0.000

Chi-square p-value

Continued

71.1 54.5

63.5 68.5 65.6 71.6

63.0 66.5 70.7 62.7

Participation rate

74 Dykema et al.

Call attempts in 2003–2005 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 or more Refused in a 2003–2005 call No Yes

Survey predictors

Table 2. Continued

13.4 16.6 12.3 9.0 8.3 5.3 4.5 3.8 3.0 2.5 1.8 1.9 1.6 1.1 14.9 93.3 6.7

4,278 302

%

616 761 559 415 380 243 204 176 136 118 82 89 74 50 683

N

Total

2,932 128

461 552 424 278 266 156 125 103 85 75 51 57 39 30 358

N

95.8 4.2

15.1 18.0 13.9 9.1 8.7 5.1 4.1 3.4 2.8 2.5 1.7 1.9 1.3 1.0 11.7

%


1,346 180

155 209 135 137 114 87 79 63 51 43 31 32 35 20 325

N

88.2 11.8

10.2 13.7 8.9 9.0 7.5 5.7 5.2 4.8 3.3 2.8 2.0 2.1 2.3 1.3 21.3

%


0.000

0.000

Chi-square p-value

68.5 41.6

74.8 72.5 75.9 70.0 70.0 64.2 61.3 58.5 62.5 63.6 62.2 64.0 52.7 60.0 52.4

Participation rate


Socio-demographic characteristics Female [vs. male] Educational attainment [High school graduate] Some college College degree or more Lives alone [vs. with at least one other] Social participation [No organizations] 1 organization 2 organizations 3 or more organizations Cognitive ability Adolescent IQ Health status Self-rated health [Excellent] Very good Good

Survey predictors 0.72 – 0.92

0.88 – 1.27 1.41 – 1.91 0.61 – 0.84

1.14 – 1.57 1.16 – 1.67 1.10 – 1.57 1.01 – 1.02

0.74 – 1.01 0.62 – 0.87

1.00 1.05 1.64 *** 0.72 *** 1.00 1.34 *** 1.39 *** 1.31 ** 1.01 ***

1.00 0.86 0.73 ***

95% CI

0.81 ***

Odds ratio

Bivariate models

1.00 0.90 0.86

1.01 *

1.00 1.26 ** 1.27 * 1.13

1.00 0.94 1.18 0.84

0.77 ***

Odds ratio

Continued

0.76 – 1.08 0.70 – 1.05

1.00 – 1.01

1.06 – 1.50 1.04 – 1.55 0.93 – 1.37

0.76 – 1.16 0.97 – 1.43 0.70 – 1.01

0.67 – 0.89

95% CI

Multivariate model

Table 3. Bivariate and Multivariate Models for WLS Survey Variables as Predictors of Participation in Saliva Collection, WLS Graduate Respondents Who Participated in the 2003–2005 Phone and Mail Surveys and Were in the Religion Module

76 Dykema et al.

Fair Poor HUI Chronic conditions index [0 conditions] 1 to 2 conditions 3 conditions or more No physical exam [vs. had one] No dental exam [vs. had one] Current smoker [vs. not] No private insurance [vs. any] Religiosity Religious attendance Never Less than weekly [About weekly] More than weekly “Bible is God’s word” Strongly agree Agree Neither agree nor disagree [Disagree or strongly disagree]

Survey predictors

Table 3. Continued

0.69 *** 0.86 0.76 *** 1.00

0.86 1.07 0.94

0.74 *

0.55 – 0.88 0.57 – 0.84 0.72 – 1.04 0.64 – 0.90

0.77 * 0.83 *

1.00 1.12 0.87 0.81 * 0.85 1.01 0.90

1.06 0.62 1.21

Odds ratio

Continued

0.69 – 1.07 0.86 – 1.33 0.78 – 1.14

0.57 – 0.96

0.62 – 0.96 0.71 – 0.98

0.95 – 1.33 0.68 – 1.11 0.69 – 0.96 0.72 – 1.02 0.82 – 1.25 0.75 – 1.10

0.77 – 1.46 0.35 – 1.09 0.83 – 1.76

95% CI

Multivariate model

0.59 – 0.85 0.71 – 0.95

0.98 – 1.32 0.66 – 0.99 0.67 – 0.89 0.59 – 0.80 0.74 – 1.08 0.62 – 0.88

1.00 1.14 0.81 * 0.77 *** 0.68 *** 0.89 0.74 ***

0.71 *** 0.82 ** 1.00 0.70 **

0.61 – 1.03 0.29 – 0.74 1.19 – 2.15

95% CI

0.79 0.47 *** 1.60 **

Odds ratio

Bivariate models


*p < .05; **p < .01; ***p < .001

Likelihood ratio chi-square Log likelihood Degrees of freedom N

Survey process variables Incomplete prior participation [vs. complete] Call attempts in 2003–2005 Refused in a 2003–2005 call

Survey predictors

Table 3. Continued

0.49 *** 0.93 *** 0.33 ***

Odds ratio 0.43 – 0.56 0.92 – 0.95 0.26 – 0.41

95% CI

Bivariate models

268.92 –2,481.583 28 4,196

0.62 *** 0.95 *** 0.53 ***

Odds ratio

0.53 – 0.72 0.94 – 0.97 0.40 – 0.71

95% CI

Multivariate model

78 Dykema et al.


79

Finally, as expected, each of the measures of survey process that may signal reluctance on the part of the respondent is associated with decreased odds of providing a saliva sample. The odds of participating are lowered by 51 percent among those with incomplete prior participation; each additional call attempt (top-coded at 15) lowers the odds of participating by 7 percent, and a refusal in the 2003–2005 phone survey is associated with a 67 percent decrease in the odds of providing saliva in 2007. The second column in table 3 presents a full model in which participation is regressed on all variables simultaneously. While being female is still associated with a significant decrease in the odds of providing a saliva sample net of the other variables in the model, the effects of college education or more (vs. high school education), living alone, and participating “some” in three or more organizations (vs. no participation) are attenuated and no longer statistically significant when all other variables are controlled. The effects of participating “some” in one and two organizations relative to no participation are each attenuated but still statistically significant when all other variables are controlled. The association between adolescent IQ and providing a saliva sample is the same size, although controlling for all other predictors decreases its p-value. Overall, the effects of the various health-status measures are attenuated and no longer statistically significant when all other variables are controlled. The exception is having no physical exam in the last year, which is still associated with decreased odds of providing a saliva sample. The effect of religious attendance on providing a saliva sample is slightly attenuated but still statistically significant for never, less than weekly, and more than weekly (vs. weekly) attendance, while one’s attitude about the Bible being God’s word is no longer statistically significant. Finally, the effects of the survey process variables are attenuated but still statistically significant, controlling for the other predictors.

Discussion Researchers are still at the beginning stages of understanding how respondents make decisions about participating in survey research in which biological measures are gathered and what factors influence when that decision-making process will lead to consent and eventual compliance. This study uses a longitudinal dataset to examine factors associated with providing a saliva sample among all sample members—those that participated, and those that did not. We find that female sample members are less likely to provide saliva samples through the mail. This contrasts with findings about participation from several sources, including household surveys for which some evidence indicates that women are more likely to participate; in-person surveys that use field interviews to collect saliva to extract DNA that show no differences by

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gender; and randomly sampled respondents in a telephone interview asked to provide a saliva sample via the mail that show no differences by gender (Galea et al. 2006). However, our result is consistent with findings from NHANES that suggest women might be less likely to yield a sample for the purposes of genetic testing. Our finding could be due to women being less likely to want to provide DNA or women being less willing or able to spit, and future research should continue to examine possible mechanisms through which this occurs. Although it is only significant in the bivariate model, our finding for household composition is compatible with Sakshaug, Couper, and Ofstedal (2010), who reported that respondents in the HRS who lived with another eligible respondent were more likely to consent to provide biological measures. These relationships are consistent with studies of response rates in household surveys more generally (Couper and Groves 1996); respondents who live alone are hypothesized to be more socially isolated and less likely to participate in a survey. Related to this, we find that participating at least “some” in one or two social organizations is associated with increased odds of providing a saliva sample; the nonsignificant findings for three or more social organizations in the full model may speak to limits on participating in surveys based on time constraints. Respondents with lower cognitive abilities, indicated by their IQ scores assessed during high school, are much less likely to provide a saliva sample. As shown in table 2, there is a difference of 14.5 percentage points between respondents in the lowest versus highest decile of cognitive ability. These results underscore the importance of developing materials that respondents with a range of abilities are able to understand and execute. The use of monetary incentives may also offset declines in response rates, particularly among those of lower cognitive ability. In addition, while evidence indicates that IQ is relatively stable across the life course (Deary et al. 2000), an avenue for future investigation would be to explore the relationship between more current measures of cognitive functioning and respondents’ willingness to provide biomeasures like saliva. We find that many of the indicators for health status are in the predicted direction of poorer health and low healthcare utilization being associated with decreased odds of providing a saliva sample, although several results are nonsignificant when controlling for other predictors. Results indicate that religiosity is associated with providing a saliva sample in both predicted and unexpected ways. Consistent with other studies, frequent religious attendance and belief in the Bible as the word of God are associated with decreased odds of providing a saliva sample, although the latter is not significant in the full model. However, we find that those with less than weekly religious attendance were less likely to provide a saliva sample compared to weekly attendance. The curvilinear association between religious attendance and providing a saliva sample was not documented in prior research and is important to


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examine in future research. For example, it is plausible that religious attendance is capturing an additional facet of social participation such that those with lower participation in one of the most common types of social participation in the United States (organized religious services) are less likely to participate in other opportunities such as research studies. Consistent with past research (e.g., Gatny, Couper, and Axinn 2013; Sakshaug, Couper, and Ofstedal 2010), the prior survey experience variables we examine strongly indicate that respondents who express reluctance to participate in a prior wave of data collection—such as by being harder to reach or refusing at some point during the previous wave of surveying—participate in lower levels to a subsequent request to provide a biological sample. An avenue for future research would be to use this paradata to create an index to predict the sample member’s propensity to participate and then adapt the study design and materials to maximize response in a cost-effective manner. For example, recruiting materials and consent forms could be tailored to include vignettes to address questions respondents might raise and be written at a level appropriate for segments of the population with lower cognitive ability. Studies could experiment with different structures for implementing incentives, such as offering larger amounts to more reluctant respondents during recruitment and using differential amounts during conversion. For studies that use interviewers to recruit or collect biomeasures, paradata on past participation could be leveraged to enhance interviewer training. Cases that are predicted to be harder to secure could be delayed to let interviewers—who inevitably vary in their experience interviewing but all of whom will be new to the study—gain experience on the easier cases (Schaeffer, Dykema, and Maynard 2010). In addition, interviewers who are identified as more adept at securing participation could be assigned to cases that are anticipated to be more difficult to field. There are obvious limitations to generalizing from the WLS sample to a larger population (Hauser 2005). In particular, the WLS is very homogeneous with regard to age, education (but not necessarily cognitive ability), and race (the sample is almost exclusively white), and some previously conducted studies of biosamples collected by mail find differences between responders in their propensity to participate by age and race (Gatny, Couper, and Axinn 2013). Further, because of its historically high response rate, WLS participants may have responded to a request to provide saliva at a higher rate than would be found in other populations. Nevertheless, to the extent that sample characteristics of other populations mirror those reported here, we believe the WLS findings provide an important baseline for comparison in terms of the strength of association among the predictors and provision of a saliva sample. Furthermore, while our selection of variables to include in the models presented here was informed by prior research, other variables not included may be equally important. For example, we do not include a measure for household income—which has been shown to be related to nonresponse in some

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studies—because of missing-data issues, but future analyses may include some measure of the household’s poverty level. Further, our analysis of the effects of respondents’ attitudes is limited to religiosity, but other variables, such as personality traits (McClain et al. 2015), political attitudes (Lawrence and Cobb 2013), and attitudes about the collection and use of biomeasures, may help further elucidate the dimensions that differentiate nonparticipants from participants. Finally, the overall design of the data-collection process included prompting phone calls to notify sample members of the upcoming mailing of the saliva kits and follow-up calls to encourage them to return the kits. While we are not able to determine the impact of these calls on final rates of participation, they add complexity to our design, in contrast to a mail-out/ mail-back effort without these added contacts.

Conclusion Taking advantage of a longitudinal survey design in which a wealth of information exists on respondents, our research considers a number of respondent characteristics—socio-demographic characteristics, cognitive ability, healthstatus measures, religiosity, and prior participation—and examines their effect on a request to provide a salivary DNA sample. This study contributes to a fuller understanding of how the characteristics of respondents affect the provision of a saliva sample, one of the many increasingly common requests made across a variety of survey endeavors. Future research should continue to evaluate characteristics of respondents and characteristics of the tasks being requested of them in order to describe how respondents ultimately make a decision to participate. This research will need to isolate not only which respondent characteristics are important but how they interact with task characteristics in order to leverage responses to optimize participation.

Supplementary Data Supplementary data are freely available online at http://poq.oxfordjournals. org/.

References Advani, Anjall S., Benjamin Atkeson, Carrie L. Brown, Bercedis L. Peterson, Laura Fish, Jeffrey L. Johnson, Jon P. Gockerman, and Marc Gautler. 2003. “Barriers to the Participation of African-American Patients with Cancer to Clinical Trials: A Pilot Study.” Cancer 97:1499–506. Amstadter, Ananda B., Karestan C. Koenen, Kenneth J. Ruggiero, Ron Acierno, Sandro Galea, Dean G. Kilpatrick, and Joel Gelernter. 2010. “NPY Moderates the Relation between Hurricane Exposure and Generalized Anxiety Disorder in an Epidemiologic Sample of Hurricane-Exposed Adults.” Depression and Anxiety 27:270–75.


83

Beebe, Timothy J. 2007. “Challenges to Collecting Survey-Based Biomarker and Genetic Data.” Survey Research 38:1–4. Campanelli, Pamela, Patrick Sturgis, and Susan Purdon. 1997. “Can You Hear Me Knocking: An Investigation into the Impact of Interviewers on Survey Response Rates.” London: Survey Methods Centre at SCPR, Social and Community Planning Research. Chen, Donna T., Donald L. Rosenstein, Palaniappan Muthappan, Susan G. Hilsenbeck, Franklin G. Miller, Ezekiel J. Emanuel, and David Wendler. 2005. “Research with Stored Biological Samples: What Do Research Participants Want?” Archives of Internal Medicine 165:652–55. Couper, Mick P. 2005. “Technology Trends in Survey Data Collection.” Social Science Computer Review 23:486–501. Couper, Mick P., and Robert M. Groves. 1996. “Household-Level Determinants of Survey Nonresponse.” In Advances in Survey Research, edited by M. T. Braverman and J. Kay Slater, 63–79. San Francisco: Jossey-Bass. Deary, Ian J., Lawrence J. Whalley, Helen Lemmon, J. R. Crawford, and John M. Starr. 2000. “The Stability of Individual Differences in Mental Ability from Childhood to Old Age: Follow-Up of the 1932 Scottish Mental Survey.” Intelligence 28:49–55. DiLoreto, Kerryann, and Kenneth D. Croes. 2007. “DNA Collection Via Saliva Donations with a Large, Longitudinal Sample: A Comparison of Different Treatments and their Outcomes.” Paper presented at the Annual International Field Directors & Technologies Conference, Santa Monica, CA, USA. Dykema, Jennifer, Danna Basson, and Nora Cate Schaeffer. 2007. “Face-to-Face Surveys.” In The Sage Handbook of Public Opinion Research, edited by W. Donsbach and M. W. Traugott, 240–48. London: Sage Publications. Etter, Jean-Francois, Elisabeth Neidhart, Sonia Bertrand, Alain Malafosse, and Daniel Bertrand. 2005. “Collecting Saliva by Mail for Genetic and Cotinine Analyses in Participants Recruited through the Internet.” European Journal of Epidemiology 20:833–38. Fix, Brian V., Richard O’Connor, David Hammond, Bill King, Ann McNeill, James Thrasher, Marcelo Boado, K. Michael Cummings, Hua-Hie Yong, Mary E. Thompson, and Andrew Hyland. 2010. “ITC ‘Spit and Butts’ Pilot Study: The Feasibility of Collecting Saliva and Cigarette Butt Samples from Smokers to Evaluate Policy.” Nicotine & Tobacco Research 12:185–90. Fricker, Scott, and Roger Tourangeau. 2010. “Examining the Relationship between Nonresponse Propensity and Data Quality in Two National Household Surveys.” Public Opinion Quarterly 74:934–55. Galea, Sandro, Ron Acierno, Kenneth Ruggiero, Heidi Resnick, Melissa Tracy, and Dean Kilpatrick. 2006. “Social Context and the Psychobiology of Posttraumatic Stress.” Annals of the New York Academy of Sciences 1071:231–41. Gatny, Heather H., Mick P. Couper, and William G. Axinn. 2013. “New Strategies for Biosample Collection in Population-Based Social Research.” Social Science Research 42:1402–9. Gavrilova, Natalia, and Stacy Tessler Lindau. 2009. “Salivary Sex Hormone Measurement in a National, Population-Based Study of Older Adults.” Journals of Gerontology Series B: Psychological Sciences and Social Sciences 64B (suppl. 1):i94–i105. Groves, Robert M., and Mick P. Couper. 1996. “Contact-Level Influences on Cooperation in Faceto-Face Surveys.” Journal of Official Statistics 12:63–83. ———. 1998. Nonresponse in Household Interview Surveys. New York: John Wiley and Sons. Hansen, Thomas V. O., Mette K. Simonsen, Finn C. Nielsen, and Yrsa Andersen Hundrup. 2007. “Collection of Blood, Saliva, and Buccal Cell Samples in a Pilot Study on the Danish Nurse Cohort: Comparison of the Response Rate and Quality of Genomic DNA.” Cancer Epidemiology, Biomarkers & Prevention 16:2072–76. Harris, Jennifer R., Tara L. Gruenewald, and Teresa Seeman. 2007. “An Overview of Biomarker Research from Community and Population-Based Studies on Aging.” In Biosocial Surveys,

84

Dykema et al.

edited by M. Weinstein, J. W. Vaupel, and K. W. Wachter, 96–135. Washington, DC: National Academies Press. Hauser, Robert M. 2005. “Survey Response in the Long Run: The Wisconsin Longitudinal Study.” Field Methods 17:3–29. Hauser, Robert M., and David Weir. 2010. “Recent Developments in Longitudinal Studies of Aging in the United States.” Demography 47:S111–S130. Henderson, Gail, Joanne Garrett, Jada Bussey-Jones, Mairead Eastin Moloney, Connie Blumenthal, and Giselle Corbie-Smith. 2008. “Great Expectations: Views of Genetic Research Participants Regarding Current and Future Genetic Studies.” Genetics in Medicine 10:193–200. Herd, Pamela, Deborah Carr, and Carol Roan. 2014. “Cohort Profile: Wisconsin Longitudinal Study (WLS).” International Journal of Epidemiology 43:34–41. Horsman, John, William Furlong, David Feeny, and George Torrance. 2003. “The Health Utilities Index (HUI®): Concepts, Measurement Properties and Applications.” Health and Quality of Life Outcomes 1:54. Idler, Ellen L., and Yael Benyamini. 1997. “Self-Rated Health and Mortality: A Review of TwentySeven Community Studies.” Journal of Health and Social Behavior 38:21–37. Jaszczak, Angela, Katie Lundeen, and Stephen Smith. 2009. “Using Nonmedically Trained Interviewers to Collect Biomeasures in a National In-Home Survey.” Field Methods 21:26–48. Kilpatrick, Dean G., Karestan C. Koenen, Kenneth J. Ruggiero, Ron Acierno, Sandro Galea, Heidi S. Resnick, John Roitzsch, John Boyle, and Joel Gelernter. 2007. “The Serotonin Transporter Genotype and Social Support and Moderation of Posttraumatic Stress Disorder and Depression in Hurricane-Exposed Adults.” American Journal of Psychiatry 164:1693–99. Krosnick, Jon A. 1991. “Response Strategies for Coping with the Cognitive Demands of Attitude Measures in Surveys.” Applied Cognitive Psychology 5:213–36. Lawrence, Michael, and Curtiss L. Cobb III. 2013. “Examination of Item- and Unit- Nonresponse in Population-Based Social Surveys that Seek to Collect Biological Marker Samples from Respondents.” Paper presented at the Annual Meeting of the American Association for Public Opinion Research, Boston, MA, USA. Le Marchand, Loïc, Annette Lum-Jones, Barbara Saltzman, Vanessa Visaya, Abraham M. Y. Nomura, and Laurence N. Kolonel. 2001. “Feasibility of Collecting Buccal Cell DNA by Mail in a Cohort Study.” Cancer Epidemiology, Biomarkers & Prevention 10:701–3. Lepkowski, James M., and Mick P. Couper. 2002. “Nonresponse in the Second Wave of Longitudinal Household Surveys.” In Survey Nonresponse, edited by R. M. Groves, D. A. Dillman, J. L. Eltinge, and R. J. A. Little, 259–72. New York: John Wiley & Sons. Lindau, Stacy T., Joscelyn N. Hoffmann, Katie Lundeen, Angela Jaszczak, Martha K. McClintock, and Jeanne A. Jordan. 2009. “Vaginal Self-Swab Specimen Collection in a Home-Based Survey of Older Women: Methods and Applications.” Journals of Gerontology Series B: Psychological Sciences and Social Sciences 64B(suppl. 1):i106–i118. Love, Gayle Dienberg, Teresa E. Seeman, Maxine Weinstein, and Carol D. Ryff. 2010. “Bioindicators in the MIDUS National Study: Protocol, Measures, Sample, and Comparative Context.” Journal of Aging and Health 22:1059–80. Malone, Thomas, Paul J. Catalano, Peter J. O’Dwyer, and Bruce Giantonio. 2002. “High Rate of Consent to Bank Biologic Samples for Future Research: The Eastern Cooperative Oncology Group Experience.” Journal of the National Cancer Institute 94:769–71. McClain, Colleen A., Sunghee Lee, Jessica Faul, and Stefany Barba. 2015. “Understanding Dynamics of Consent Requests in Surveys: Consent to Biomarker Data Collection and Administrative Data Linkage in the Health and Retirement Study.” Paper presented at the Annual Meeting of the American Association for Public Opinion Research, Hollywood, FL, USA. McQuillan, Geraldine M., Qiyuan Pan, and Kathryn S. Porter. 2006. “Consent for Genetic Research in a General Population: An Update on the National Health and Nutrition Examination Survey Experience.” Genetics in Medicine June 8:354–60.


85

Ness, Kirsten K., Chenghong Li, Pauline A. Mitby, Gretchen A. Radloff, Ann C. Mertens, Stella M. Davies, Sue Hammond, Gregory T. Armstrong, and Leslie L. Robison. 2010. “Characteristics of Responders to a Request for a Buccal Cell Specimen among Survivors of Childhood Cancer and Their Siblings.” Pediatric Blood & Cancer 55:165–70. Radler, Barry T., and Carol D. Ryff. 2010. “Who Participates? Accounting for Longitudinal Retention in the MIDUS National Study of Health and Well-Being.” Journal of Aging and Health 22:307–31. Research Triangle International. 2002. “1999 National Long Term Care Survey: Functional and Health Changes of the Elderly.” Final Report: RTI Project 7798. Rylander-Rudqvist, Tove, Niclas Hakansson, Gunnel Tybring, and Alicja Wolk. 2006. “Quality and Quantity of Saliva DNA Obtained from the Self-Administrated Oragene Method: A Pilot Study on the Cohort of Swedish Men.” Cancer Epidemiology, Biomarkers & Prevention 15:1742–45. Sakshaug, Joseph W., Mick P. Couper, and Mary Beth Ofstedal. 2010. “Characteristics of Physical Measurement Consent in a Population-Based Survey of Older Adults.” Medical Care 48:64–71. Sakshaug, Joseph W., Mary Beth Ofstedal, Heidi Guyer, and Timothy J. Beebe. 2015. “The Collection of Biospecimens in Health Surveys.” In Handbook of Health Survey Methods, edited by T. P. Johnson, 383–420. Hoboken, NJ: John Wiley & Sons. Schaeffer, Nora Cate, Jennifer Dykema, and Douglas W. Maynard. 2010. “Interviewers and Interviewing.” In Handbook of Survey Research, 2nd ed., edited by P. V. Marsden and J. D. Wright, 437–70. Bingley, UK: Emerald Group Publishing Limited. Schwartz, Marc D., Chanita Hughes, Joan Roth, David Main, Beth N. Peshkin, Claudine Isaacs, Carol Kavanagh, and Caryn Lerman. 2000. “Spiritual Faith and Genetic Testing Decisions among High-Risk Breast Cancer Probands.” Cancer Epidemiology, Biomarkers & Prevention 9:381–85. Sewell, William H., Robert M. Hauser, Kristen W. Springer, and Taissa S. Hauser. 2003. “As We Age: The Wisconsin Longitudinal Study, 1957–2001.” In Research in Social Mobility, edited by K. T. Leicht, 3–111. London: Elsevier. Singer, Eleanor, Amy Corning, and Mark Lamias. 1998. “Trends: Genetic Testing, Engineering, and Therapy: Awareness and Attitudes.” Public Opinion Quarterly 62:633–64. Tourangeau, Roger, Lance J. Rips, and Kenneth Rasinski. 2000. The Psychology of Survey Response. Cambridge: Cambridge University Press. Uusküla, Anneli, Mart Kals, and Louise-Anne McNutt. 2011. “Assessing Non-Response to a Mailed Health Survey Including Self-Collection of Biological Material.” European Journal of Public Health 21:538–42. Wang, Sophia S., Fred Fridinger, Kris M. Sheedy, and Muin J. Khoury. 2001. “Public Attitudes Regarding the Donation and Storage of Blood Specimens for Genetic Research.” Community Genetics 4:18–26. Weinstein, Maxine, James Vaupel, and Kenneth Wachter. 2007. Biosocial Surveys. Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys. Washington, DC: National Academies Press. Weir, David. 2007. “Elastic Powers: The Integration of Biomarkers into the Health and Retirement Study.” In Biosocial Surveys, edited by M. Weinstein, J. W. Vaupel, and K. W. Wachter, 78–95. Washington, DC: National Academies Press.