2.3 Challenge 3: Recognizing the limitations of survey data
Survey research and self-reported health are common elements of human
health and aging studies addressing the impact of social inequalities
(Black et al., 2017). Surveys have proven to be a reliable general tool
for quickly producing empirical data from large representative samples,
but these data also have significant disadvantages. For example,
response rates are hard to control, and the data produced can lack many
important details about the topic under investigation (Kelley et al.,
2003). When used longitudinally (e.g., cohort studies), survey research
suffers from other limitations due to the potential for significant
changes in individual’s responsiveness over time, as well as in the
consistency of respondents in their replies to public health-relevant
questions when asked again at a later time (ethnic origin: Johnson 1974;
patterns of abuse: Abramsky et al., 2022; Loxton et al., 2019; smoking:
Kaestle, 2015; substance use: Broman et al., 2022; suicidal attempts:
Hart et al., 2013). In general, there may be systematic differences in
the ability of individuals to self-evaluate their own health across time
(Black et al., 2017; Vuolo et al., 2014). Associations between
self-reported health and objective health, as measured by biological
risk factors, may also differ across socially stratified groups (Dowd
and Zajacova, 2010; Layes et al., 2012). Conversely, interviewer effects
also contribute significantly to biases in survey research, as
respondents may be predisposed to provide socially desirable responses
to the interviewer (Davis et al., 2010; Salazar, 1990). Interviewers
also contribute to misclassification of race and ethnic groups (Massey
1980; Williams and Chiquita, 1995); biased assumptions of health among
old persons (Thorslund and Wärneryd, 1990); biases in age, race, and
gender perceptions (as reviewed by Davis et al., 2010); and can be
affected by their level of experience interviewing people (Salazar,
1990). Taken together, these data limitations may contribute to the
census miscount of socially stratified groups that ultimately affects
reported rates of health conditions (Williams and Chiquita, 1995). Thus,
both survey and interviewer data can result in biases in our ability to
evaluate the accuracy of models quantifying the effects of the social
environment on human health and consequent life courses.