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Social processes that change quickly are difficult to study, because they require frequent survey measurement. Weekly, daily, or even hourly measurement may be needed depending on the topic. With more frequent measurement comes the prospect of more complex patterns of missing data. The mechanisms creating the missing data may be varied, ranging from technical issues such as lack of an Internet connection to refusal to complete a requested survey. We examine one approach to mitigating the damage of these missing data - a follow-up or closeout interview that is completed after the frequent measurement. The Relationship Dynamics and Social Life (RDSL) study used this approach. The study asked women weekly about their attitudes and behaviors related to sexual relationships and pregnancy. The surveys were carried out for 130 weeks and concluded with a closeout interview. We explore the patterns of missing data in the RDSL study and then examine associations between the data collected in the closeout survey and key variables collected in the weekly survey. We then assess the extent to which data from the closeout survey are useful in repairing the damage caused by missing data.

Challenges to survey data collection have increased the costs of social research via face-to-face surveys so much that it may become extremely difficult for social scientists to continue using these methods. A key drawback to less expensive Internet-based alternatives is the threat of biased results from coverage errors in survey data. The rise of Internet-enabled smartphones presents an opportunity to re-examine the issue of Internet coverage for surveys and its implications for coverage bias. Two questions (on Internet access and smartphone ownership) were added to the National Survey of Family Growth (NSFG), a U.S. national probability survey of women and men age 15-44, using a continuous sample design. We examine 16 quarters (4 years) of data, from September 2012 to August 2016. Overall, we estimate that 82.9% of the target NSFG population has Internet access, and 81.6% has a smartphone. Combined, this means that about 90.7% of U.S. residents age 15-44 have Internet access, via either traditional devices or a smartphone. We find some evidence of compensatory coverage when looking at key race/ethnicity and age subgroups. For instance, while Black teens (15-18) have the lowest estimated rate of Internet access (81.9%) and the lowest rate of smartphone usage (72.6%), an estimated 88.0% of this subgroup has some form of Internet access. We also examine the socio-demographic correlates of Internet and smartphone coverage, separately and combined, as indicators of technology access in this population. In addition, we look at the effect of differential coverage on key estimates produced by the NSFG, related to fertility, family formation, and sexual activity. While this does not address nonresponse or measurement biases that may differ for alternative modes, our paper has implications for possible coverage biases that may arise when switching to a Web-based mode of data collection, either for follow-up surveys or to replace the main face-to-face data collection.

We explore the correlates of noncoverage and nonresponse in an Internet survey conducted as part of the Health and Retirement Study (HRS), a panel study of persons 50 years old and older in the US. About 30% of HRS respondents indicated they used the Internet. Of these, 73% expressed willingness to do a Web survey. A subset of this group was subsequently sent a mailed invitation to participate in a Web survey and 78% completed the survey. Using multivariate models, we find significant demographic, financial, and health-related differences in access, consistent with other research. There are fewer differences in willingness (given access) and response (given willingness). However, disparities in health and socio-economic status persist after controlling for demographic differences in coverage and response. Weighting on demographics alone is thus unlikely to yield a representative sample in such surveys. Noncoverage (lack of access to the Internet) appears to be of greater concern than nonresponse (unwillingness to participate given access) for representation in Internet surveys of this age group.

We study the influence of frequent survey measurement on behavior. Widespread access to the Internet has made important breakthroughs in frequent measurement possible-potentially revolutionizing social science measurement of processes that change quickly over time. One key concern about using such frequent measurement is that it may influence the behavior being studied. We investigate this possibility using both a population-based experiment with random assignment to participation in a weekly journal for twelve months (versus no journal) and a large-scale, population-based, journal-keeping study with weekly measurement for 30 months. Results reveal few of the measured behaviors are correlated with assignment to frequent measurement. Theoretical reasoning regarding the likely behavioral response to frequent measurement correctly predicts domains most vulnerable to this possibility. Overall, however, we found little evidence of behavioral response to frequent measurement.

This paper aims to increase understanding of the methodological issues involved in adding biomeasures to social research by investigating the potential of an event-triggered, self-collection technique for monitoring biological response to social events. We use data from the Relationship Dynamics and Social Life (RDSL) study, which collected saliva samples triggered by a life event important to the aims of the study - the end of a romantic relationship. Our investigation found little evidence that those who complied in the biosample collection were different from those who did not comply in terms of key study measures and sociodemographic characteristics. We also found no evidence that the biosample collection had adverse consequences for subsequent panel participation. We did find that prior cooperation in the study was an important predictor of biosample cooperation, which is important information in developing biosample collection strategies. As demand for biological samples directly linked to social data continues to grow, effective low-cost collection methods will become increasingly valuable. The evidence here indicates that self-collected biosamples may offer tremendous potential to meet this demand.