Results
Our qualitative analysis led to the identification and classification of various subdimensions of the following analytic categories, which we present in the Methods section: types of health factors mentioned, valence of health factors, temporality of health factors, conditional health statements, and general descriptions and definitions of health. We report the results for each of these analytic categories below.
Types of health factors mentioned
Table
2 shows the percentage of participants who explicitly mentioned the listed types of health factors at least once while reporting what they were thinking about when rating their health. The most common health factor mentioned was health conditions: 70% of participants made reference to at least one health condition (e.g., “diabetes,” “no illness”). Examining subdivisions of health conditions, 41% of participants reported at least one specific health condition, 36% reported at least one nonspecific health condition (e.g., “my illnesses”), and 17% made reference to the absence of health conditions. Compared to younger participants, older participants were more likely to mention specific health conditions (
p < .10) and less likely to mention an absence of health conditions (
p < .05). Participants with at least some college were more likely to report the absence of health conditions compared to those with a high school education or less (
p < .05).
Table 2
Percentage of Participants Mentioning Various Types of Health Factors at Least One Time
Health conditions (overall) | 70% | | – | – | – | – |
Specific | 41% | “I’m diabetic” | – | – | + | – |
Nonspecific | 36% | “my illnesses” | – | – | – | – |
Absence | 17% | “I have no medical conditions at all” | – | – | * | * |
Health behavior | 41% | “eating,” “I don’t exercise,” “trying to lose weight” | – | – | – | * |
Health care practitioner or setting | 25% | “I went to the doctor,” “I don’t go…” | – | – | – | – |
Physical state | 22% | “in good shape,” “overweight” | – | – | – | – |
Comparative statements (overall) | 19% | | – | – | – | – |
Compared present self to others | 11% | “I was thinking about my husband…compared to him” | – | – | – | – |
Compared present self to self at another time | 11% | “I’m not in as good shape as I was 3 years ago” | – | – | – | – |
Physical functioning | 14% | “body working,” “ability to work,” “lazy” | * | – | – | – |
Feel | 14% | “how/what I feel” “I feel fine/good/great/sick” | – | – | – | – |
Mental health | 8% | “depression,” “I don’t have mental health issues” | – | – | + | – |
Age | 6% | “at the age of 51,” “I’m at an age…” | – | – | – | – |
External factors | 5% | “I’m a parent,” “family background,” “money for food” | – | – | – | – |
Forty-one percent of participants made at least one reference to a health behavior (e.g., “smoking,” “I don’t exercise”), and these mentions were more likely to occur for participants in the higher education group (p < .05). One quarter of participants made at least one reference to a health care practitioner or setting when rating their health. Twenty-two percent of participants mentioned their overall physical state, and 14% mentioned their physical functioning. Whites were more likely to discuss physical functioning compared to the other racial/ethnic groups (p < .05). Fourteen percent of participants used the word “feeling” to describe an internal state of their health (rather than a description of how they were thinking of something).
Comparative statements are defined in terms of explicit (e.g., “compared to others”) or implicit (e.g., “I’m not the healthiest”) comparisons that participants make between 1) their own health and the health of others (like the preceding examples) or 2) their health in the present and their health at other points in time (e.g., “I’m not in as good shape as I was 3 years ago,” “since I’ve hit my thirties I’ve started having health problems”). Overall, 19% of participants made some sort of comparative statement while describing how they rated their health. Among the specific types of comparative references, 11% of participants made comparisons to others, and 11% of participants compared their present self to themselves at other points in time (the past for all but one participant, who compared their present self to a hypothetical self, e.g., “I’m not as healthy as I could be”). It is interesting to note that no Latinos made any sort of comparative reference, although the difference across racial/ethnic groups did not reach conventional statistical significance levels.
In contrast to the several types of physical health factors participants mentioned, only 8% of participants explicitly mentioned mental health as figuring into their assessment of their health status. All five of these mentions of mental health occurred for older compared to younger participants (p < .10). Furthermore, only 6% of participants mentioned age as informing their rating. All four of these mentions of age were provided by male participants, although this difference across gender did not reach conventional statistical significance levels. Finally, only 3 participants (5%) mentioned external factors outside their immediate control as informing their health rating.
Valence of health factors mentioned
We define valence as the participants’ affective orientation to the health factor they mentioned based on what it implies about the quality of their current health, and provide examples of each in the Analytic Categories subsection of the Methods section. Overall, 80% of participants mentioned at least one negatively valenced health factor, and a majority (59%) also mentioned at least one positively valenced health factor, indicating immediately that some participants are integrating disparate health information in forming their health assessment (Table
3). A majority (52%) of participants also mentioned at least one health factor with a “not discernible” valence, meaning that the health factor was mentioned in the abstract rather than indicating its impact on the participant’s health. Thirty percent of participants expressed at least one ambivalent or neutrally valenced health factor.
Table 3
Percentage of Participants with at Least One Health Factor Coded as Indicating a Negative, Ambivalent/Neutral, Positive, or Not Discernible Valence
Negative | 80% | – | – | – | – |
Ambivalent or Neutral | 30% | * | + | – | – |
Positive | 59% | – | – | – | – |
Not discernible | 52% | – | – | – | – |
Men were more likely than women to mention at least one ambivalently or neutrally valenced health factor (p < .10). In addition, a difference by race/ethnicity was present in the data (p < .05): American Indians were less likely to mention ambivalently or neutrally valenced health factors and blacks and Latinos were more likely to mention these health factors, with whites in an intermediate position.
Table
4 shows the percentage of mentions of each health factor that were coded with a negative, ambivalent/neutral, positive, or not discernible valence, showing the diversity in valence within particular kinds of health factors. The majority of mentions of (overall, specific, and nonspecific) health conditions, (overall and self) comparative statements, mental health, age, and external factors were negatively valenced, with pluralities of negatively valenced mentions for health behaviors, health care practitioners or settings, physical state, and feeling. The majority of mentions of absence of health conditions and comparisons to others were positively valenced, with large minorities or pluralities of positively valenced mentions of health behaviors, health care practitioners or settings, physical state, overall comparative statements, physical functioning, and feeling. Furthermore, some types of health factors were more likely to be formulated with a level of abstraction given the large proportions of “not discernible” mentions, including health behaviors, physical state, and physical functioning.
Table 4
Percentage of Health Factors Coded as Indicating a Negative, Ambivalent/Neutral, Positive, or Not Discernible Valence by Type of Health Factor
Health conditions (overall) | 74% | 6% | 17% | 3% | 124 |
Specific | 86% | 6% | 5% | 3% | 65 |
Nonspecific | 90% | 8% | 0% | 3% | 40 |
Absence | 0% | 0% | 95% | 5% | 19 |
Health behavior | 40% | 3% | 32% | 25% | 63 |
Health care practitioner or setting | 42% | 8% | 35% | 15% | 26 |
Physical state | 42% | 0% | 29% | 29% | 24 |
Comparative statements (overall) | 52% | 13% | 35% | 0% | 23 |
Self | 83% | 8% | 8% | 0% | 12 |
Others | 18% | 18% | 64% | 0% | 11 |
Physical functioning | 11% | 0% | 48% | 41% | 27 |
Feel | 31% | 23% | 31% | 15% | 13 |
Mental health | 56% | 11% | 22% | 11% | 9 |
Age | 50% | 0% | 25% | 25% | 4 |
External | 60% | 0% | 20% | 20% | 5 |
We further examined how the valence of health factors mentioned varied by the SRH response category selected (Table
5). Of the 64 participants, a plurality (44%) selected the middle category “good,” followed by the categories that surround it—“very good” (31%) and “fair” (19%). Very few participants selected “excellent” (6%) and no participants selected “poor.” Somewhat unsurprisingly, the percentage with at least one negatively valenced health factor decreased with better SRH (
p < .05): The percentage of participants with at least one negatively valenced health factor was 100% for those answering “fair,” 86% for those answering “good,” 65% for those answering “very good,” and 50% for those answering “excellent.” Similarly, all participants reporting “excellent” health had at least one positively valenced health factor, and the percentage with at least one positively valenced health factor decreased with worse SRH (
p < .01).
Table 5
Percentage of Participants with at Least One Health Factor Coded as Indicating a Negative, Ambivalent/Neutral, Positive, or Not Discernible Valence within Self-Rated Health Category Chosen
Overall |
Percentage | 19% | 44% | 31% | 6% | |
Number of participants | 12 | 28 | 20 | 4 |
Valencea
|
Negative (vs. none) | 100% | 86% | 65% | 50% | * |
Ambivalent or neutral (vs. none) | 17% | 46% | 20% | 0% | + |
Positive (vs. none) | 25% | 54% | 80% | 100% | ** |
Not discernible (vs. none) | 25% | 57% | 65% | 25% | + |
The results in Table
5 also highlight the integration of disparate aspects of health in answering SRH. For example, 25% of participants who said their health was "fair" still mentioned at least one health factor that was positively valenced with respect to the quality of their current health. The integration of disparate aspects is also evident in the percentage of participants who had at least one ambivalently or neutrally valenced health factor, which varied across the SRH response categories (
p < .10). That these ambivalent/neutral mentions occurred for almost half (46%) of participants who answered “good” provides evidence that participants selecting “good” may be attempting to integrate disparate health information when selecting their response to the survey question. Finally, the results in Table
5 show that mentions of health factors with a “not discernible” valence also varied across SRH category (
p < .10), with the largest percentages for respondents who reported their health to be “very good” or “good.”
Temporality of health factors mentioned
The temporality analytic category captures information about the relative point in time to which a health factor refers. Overall, 97% of participants had at least one health factor that referred to the present, 23% had at least one factor referring to the past, 13% had a factor that spanned from past to present, and 19% had at least one conjecturing health factor (not shown). The temporality of health factors did not vary significantly by race/ethnicity, gender, age, or education, or by the response category chosen when answering the SRH question (not shown).
Conditional health statements
Conditional health statements refer to when the presence or absence of one health factor depends on another; these can either cascade (e.g., “if X then Y”) or contrast (e.g., “X but Y”). Sixty-four percent of participants had at least one conditional health statement; 34% had at least one cascade, and 36% had at least one contrast (Table
6). The only marginally significant difference across groups, however, was that those with some college education or more were more likely to have a cascade than those with a high school education or less (
p < .10).
Table 6
Percentage of Participants with at Least One Conditional Health Statement
Conditional health statement | 64% | – | – | – | – |
Cascade | 34% | – | – | – | + |
Contrast | 36% | – | – | – | – |
We also coded the valence of the entire conditional health statement. There were 52 unique conditional health statements (some participants had more than one); half were cascades and half were contrasts (not shown). Nineteen of the 26 unique cascades were negative. Coding the valence of the conditional health statements also revealed another way in which the integration of different aspects of health is displayed by participants—21 of the 26 unique contrasts were ambivalent, integrating both positive and negative health factors in formulating an assessment of one’s health (not shown).
Descriptions and definitions
In addition to the various types of health factors, participants expressed various descriptions and definitions of health when asked to describe what they were thinking about when rating their health (Table
7). Forty two percent of participants had at least one adjective descriptions (e.g., “I guess I’m pretty good,” “I feel fine”) when asked what they were thinking about when they rated their health. Fourteen percent had at least one global statement as part of their response (e.g., “overall,” “everything,” “in general”). Thirteen percent of participants had at least one health definition, providing some abstract parameters for what they were thinking about when rating their health (e.g., “just my health overall,” “I was thinking about my physical health,” or “how I feel”). Eight percent of participants had at least one evaluative statement that assessed a previous health factor mentioned (e.g., “that was scary,” “it’s really hard”). Finally, 6 % of participants had at least one normative statement in which they implicitly or explicitly identify the commonality of their situation with others (e.g., “there’s always going to be stress,” “[pains] come along with age”). None of descriptions and definitions vary across the sociodemographic groups.
Table 7
Percentage of Participants with at Least One Description or Definition
Adjective description | 42% | – | – | – | – |
Global statement | 14% | – | – | – | – |
Health definition | 13% | – | – | – | – |
Evaluative statement | 8% | – | – | – | – |
Normative statement | 6% | – | – | – | – |
Discussion
In order to provide a more comprehensive understanding of how SRH functions as a measure of health, the two main goals for this study were to provide a qualitative description of how participants rate their health when asked to do so and to examine whether features of this response process vary across sociodemographic groups. We used cognitive interviewing to elicit descriptions of what participants consider when rating their health and qualitative analysis to identify both which health factors they take into account as well as how they take these health factors and components of the SRH question into account when rating their health. Participants do not simply list the health factors they consider, but often cast their answers in a way that reveals how those health factors and components of the question are experienced, conceptualized, interpreted, and integrated to formulate answers to SRH.
Our qualitative analysis led to the identification and classification of various subdimensions of the following analytic categories: types of health factors mentioned, valence of health factors, temporality of health factors, conditional health statements, and general descriptions and definitions of health. We consider the findings from each of these analytic categories below.
We argue for the merit in adding to the body of studies that examine the health factors respondents consider when rating their health, as doing so across time, place, and groups bolsters previous findings and documents health factors previously undescribed. In addition to replicating some of the types of health factors mentioned in prior qualitative studies, such as mentions of health conditions and physical functioning [
8], one new health factor emerged from our study that was not identified previously. Comparisons to relevant others have been documented as one of the factors participants consider when formulating their response to SRH [
7,
8], and the current study highlights another relevant comparison that some participants make—a comparison to themselves in the past, which has been proposed as contributing to health ratings [
3] but was not reported in prior qualitative studies as something respondents state as a consideration when rating their health.
1 An additional interesting finding from our analysis is the health factors participants did
not mention taking into account when formulating their SRH answer. In particular, stress, spirituality, mental health, age, and external factors like family background or socioeconomic circumstances were rarely or not mentioned by participants.
Our contribution of the characterizations of
how participants formulate SRH is an important extension of prior research. To the best of our knowledge, this is the first study to systematically describe and analyze the analytic categories of valence, temporality, conditional health statements, and descriptions and definitions of health as important components of participants’ ratings of health. Importantly, our coding of valence relies on our perception of the participant’s orientation to the health factor mentioned as displayed in the cognitive interview, rather than researchers’ a priori evaluation of what constitutes a positive or negative component of health [
8]. We note that future research can use valence in order to make distinctions among participants who mention the same type of health factor yet use information in different ways when formulating their rating of their health; we could not examine group differences in this process due to sample size. In addition, we argue that in order to understand what a health factor means for a participant’s health, probing needs to focus on this when the valence of the health factor is not discernible (“how much I exercise” vs. “I exercise a lot/not at all”). This extends beyond SRH to other studies that seek to map out the dimensions of complex concepts with cognitive interviewing.
In our characterization of how participants formulate SRH, we also documented conditional health statements, which took the form of cascading (sequentially linked) health factors or contrasts that largely integrated disparately valenced health factors, both of which were previously undescribed in the literature. Overall, we observed various ways in which seemingly disparate health factors are integrated to formulate an answer: negatively valenced health factors reported with positive self-rated health (and vice versa), ambiguous or neutrally valenced health factors, and two types of conditional health statements: cascades and contrasts.
In addition, our study highlighted the temporal dimensions that underlie participants’ health ratings, mainly considering the present, but also the past, trajectories from past to present [
3], and conjecturing about a hypothetical future self. The descriptions and definitions of health that occur when participants are asked to describe what they are thinking about when they rate their health were previously undescribed in the qualitative studies in which respondents report what they think about when rating their health. In our inductive coding we observed four categories of descriptions or definitions that participants use: adjective descriptions, global statements, health definitions, evaluative statements, and normative statements.
We argue that it is important to understand all the components—the
which and the
how—underlying SRH in order to understand its validity as a measure of health for research and monitoring purposes. As we see in this study, two participants who report the same SRH and the same health condition may have different valences or temporality for that health factor, and different conditional relationships with other health factors; treating the SRH answers reported in the survey (and the reported health condition) as the same for these two hypothetical respondents ignores this heterogeneity. In addition, participants across the range of answer categories appear to integrate disparate aspects of their health through the use of contrasting conditional health statements, ambiguously valenced health factors, and health factors with valences that do not align with their health rating (e.g., reporting “excellent” health and a negatively valenced health factor). Thus, underlying ratings of health is a web of interrelated and sometimes conflicting components of health. In terms of implications for researchers and analysts that use SRH, this tells us that 1) SRH is doing what researchers assume it is doing in terms of prompting participants to summarize their health [
8]—although what is considered and by whom varies across sociodemographic groups—and 2) prior research is incomplete with respect to representing what underlies ratings of health, as how these health factors are perceived and integrated to formulate an answer is not captured in prior research. Thus, the results of prior and future studies that use SRH as a measure of health should be interpreted with this complexity and heterogeneity in mind. More specifically, it indicates that treating SRH as proxy for more objective health without adjusting in some way for this complex and heterogeneous response process can lead to errors in measurement and interpretation, as the measure then conflates both the more objective health factors that inform the rating and participants’ evaluative response processes [
4].
Although the characterizations of “how” participants formulate their SRH answers are an important contribution, we sought to supplement these with examination of differences across race, ethnicity, gender, education, and age. Because of the small convenience quota sample, however, the results are clearly exploratory and meant to highlight new avenues for future research, particularly since 1) we do not have the statistical power to confidently detect group differences and 2) we do not have controls for “more objective” health that would help to delineate whether the differences across groups in the SRH response process we observe here are due to group differences in more objective health or differences in evaluative frameworks. Furthermore, a focus on between-group differences should be supplemented with a focus on intersecting systems of identity and oppression which cannot be examined in this small convenience quota sample with two respondents in each “cell” that intersects race/ethnicity, gender, age, and education [
20,
21].
Limitations noted, however, some of the findings of group differences in this study are consistent with patterns from prior qualitative and quantitative studies. For example, differences by age and education in the types of health factors mentioned correspond to patterns in health disparities by age and education, with younger and more educated persons having better health outcomes (in this case, reporting an absence of health conditions or not reporting the presence of health conditions) [
22]; this finding for age is consistent with prior qualitative research [
8,
11]. White participants being more likely than other groups to mention physical functioning as part of how they rate their health is also consistent with prior qualitative research by Krause and Jay [
8].
In addition, some of the findings of group differences were counter to expectation or highlight avenues for future research. Mental health was more likely to be mentioned for older compared to younger participants, underscoring an interesting juxtaposition with prior research that finds no differences in age in considering mental factors to rate one’s health [
7,
11]. Similarly, participants with more education were more likely to mention health behaviors when rating their health, counter to the findings of Krause and Jay [
8], who found that respondents with lower levels of education were more likely to mention health behaviors. This inconsistency could be sorted out in a larger study in which the valence of health behaviors is considered. Further, those with some college education or more were more likely to have a cascade than those with less education. In a synthesis of research on cross-cultural cognitive interviewing, Willis [
23] discusses a few studies in which respondents with lower educational attainment have more indicators of difficulty in answering cognitive interviewing probes. Although speculative, we find it plausible that those with less education are more likely to simply list health information when asked what they were thinking of when rating their health given the metacognitive burden of performing this task, while those with more education are more likely to make linkages among health factors in the form of a cascade.
Regarding race/ethnicity, no Latinos made any sort of comparative reference, aligning with previous research which shows that Latinos have a more collectivist orientation (prioritizing group over individual goals) compared to, for example, non-Hispanic whites [
24]. We posit that a collectivist orientation may prevent comparisons to others when rating one’s health—or explicitly mentioning such comparisons—if such comparisons are perceived to be a source of discord by invoking hierarchy or individuation. In addition, future work should examine why American Indians, a population previously undescribed with respect to what underlies their SRH, may be less likely to reference ambivalently or neutrally valenced health factors, as well as why blacks and Latinos may be more likely to do so.
All four mentions of age as a health factor were provided by male participants. Although the number is too small to draw conclusions about group differences, it is plausible that women may be less inclined to explicitly mention age in this and other contexts given the gendered nature of ageism in the US [
25]. It is also interesting to note that men were more likely to mention ambivalently or neutrally valenced health factors. This finding highlights one pathway through which the apparent “health optimism” of men in the US relative to women (at least prior to older ages) might occur [
26]. We posit that with poorer objective health, men may rate their health better than do women because men interpret the health factors through a lens of ambivalence or neutrality as opposed to purely negative.
This study builds upon prior work by characterizing how participants formulate their health ratings more holistically, identifying several components of rating health that researchers should attend to beyond the types of health factors participants consider. Our analytic approach itself is an important contribution to the analysis of cognitive interviews and transcribed talk more generally. We coded participants’ cognitive interviews in an inductive, iterative, and systematic process, and include in our analysis all parts of their answers to the probes following SRH. Our group consensus approach was particularly useful in examining words and phrases with multiple meanings, and allowed us to vet assumptions and inferences about what participants might have meant by something they said. We recommend that the process we used to code utterances about SRH for this study be used in other studies of SRH with larger samples.
Some limitations to note include that the interviewing logistics—in which participants traveled to be interviewed—precluded recruiting the very ill. We had very few participants reporting “excellent” health and no participants reporting “poor” health, so we are missing a comprehensive description of what underlies SRH at the extremes of the rating scale. In addition, differences observed across race/ethnicity, gender, age, and educational attainment might be due to confounding factors such as occupation, household income, and access to health care. Furthermore, the results from a Wisconsin convenience sample may not be generalizable to other regions and sociodemographic groups. Finally, the cognitive interviewing process itself may influence the descriptions participants provide, as the answers obtained are dependent on what the probes ask participants to do and which parts of the probes participants attend to. Some participants may be more adept than others in verbally delineating their SRH response process in a follow-up—retrospective—probe, and it is likely that participants attend to different facets of the follow-up probe in the same way they attend to different facets of SRH. Thus, the analysis presented here is a more direct observation of the SRH response process than previously described, but it is not complete.