Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated health, and subjective life expectancy in survey instruments

Abstract

Purpose

This study examines the effect of question context created by order in questionnaires on three subjective well-being measures: life satisfaction, self-rated health, and subjective life expectancy.

Methods

We conducted two Web survey experiments. The first experiment (n = 648) altered the order of life satisfaction and self-rated health: (1) life satisfaction asked immediately after self-rated health; (2) self-rated health immediately after life satisfaction; and (3) two items placed apart. We examined their correlation coefficient by experimental condition and further examined its interaction with objective health. The second experiment (n = 479) asked life expectancy before and after parental mortality questions. Responses to life expectancy were compared by order using ANOVA, and we examined interaction with parental mortality status using ANCOVA. Additionally, response time and probes were examined.

Results

Correlation coefficients between self-rated health and life satisfaction differed significantly by order: 0.313 (life satisfaction first), 0.508 (apart), and 0.643 (self-rated health first). Differences were larger among respondents with chronic conditions. Response times were the shortest when self-rated health was asked first. When life expectancy asked after parental mortality questions, respondents reported considering parents more for answering life expectancy; and respondents with deceased parents reported significantly lower expectancy, but not those whose parents were alive.

Conclusion

Question context effects exist. Findings suggest placing life satisfaction and self-rated health apart to avoid artificial attenuation or inflation in their association. Asking about parental mortality prior to life expectancy appears advantageous as this leads respondents to consider parental longevity more, an important factor for true longevity.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. 1.

    Diener, E., Oishi, S., & Lucas, R. E. (2003). Personality, culture, and subjective well-being: Emotional and cognitive evaluations of life. Annual Review of Psychology, 54(1), 403–425.

    PubMed  Article  Google Scholar 

  2. 2.

    Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The query of American-life. New York, NY: Russell Sage.

    Google Scholar 

  3. 3.

    Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Kahneman, D., Diener, E., & Schwarz, N. (1999). Well-being: The foundations of hedonic psychology. New York, NY: Russell Sage Foundation.

    Google Scholar 

  5. 5.

    Schwarz, N., & Strack, F. (1999). Report of subjective well-being: Judgmental processes and their methodological implications. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: The foundations of hedonic psychology (pp. 61–84). New York, NY: Russell Sage Foundation.

    Google Scholar 

  6. 6.

    Schuman, H., & Presser, S. (1981). Questions and answers in attitude surveys. London: Academic Press.

    Google Scholar 

  7. 7.

    Schwarz, N. (1996). Cognition and communication: judgmental biases, research methods, and the logic of conversation. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  8. 8.

    Schuman, H. (1992). Context effects: State of the past/state of the art. In N. Schwarz & S. Sudman (Eds.), Context effects in social and psychological research (pp. 5–20). New York, NY: Springer.

    Google Scholar 

  9. 9.

    Schwarz, N. (1999). Self-reports: how the questions shape the answers. American Psychologist, 54(2), 93.

    Article  Google Scholar 

  10. 10.

    Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of survey response. Cambridge: Cambridge University Press.

    Google Scholar 

  11. 11.

    Strack, F., Schwarz, N., & Wänke, M. (1991). Semantic and pragmatic aspects of context effects in social and psychological research. Social Cognition, 9(1), 111–125.

    Article  Google Scholar 

  12. 12.

    Lee, S., & Schwarz, N. (2014). Question context and priming meaning of health: Effect on differences in self-rated health between Hispanics and non-Hispanic whites. American Journal of Public Health, 104(1), 179–185.

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Garbarski, D., Schaeffer, N. C., & Dykema, J. (2015). The effects of response option order and question order on self-rated health. Quality of Life Research, 24, 1443–1453.

    PubMed  Article  Google Scholar 

  14. 14.

    Sudman, S., Bradburn, N. M., & Schwarz, N. (1996). Thinking about answers: The application of cognitive processes to survey methodology. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  15. 15.

    Zaller, J. (1992). The nature and origins of mass opinion. Cambridge: Cambridge University Press.

    Google Scholar 

  16. 16.

    Higgins, E. T. (1989). Knowledge accessibility and activation: Subjectivity and suffering from unconscious sources. In J. S. Uleman & J. A. Barghs (Eds.), Unintended thought (pp. 75–123). New York, NY: Guilford Press.

    Google Scholar 

  17. 17.

    Tourangeau, R., & Rasinski, K. A. (1988). Cognitive processes underlying context effects in attitude measurement. Psychological Bulletin, 103(3), 299–314.

    Article  Google Scholar 

  18. 18.

    Wyer, R. S., & Srull, T. K. (1986). Human cognition in its social context. Psychological Review, 93(3), 322–359.

    PubMed  Article  Google Scholar 

  19. 19.

    Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103(3), 582–591.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Organisation for Economic Cooperation and Development (OECD). (2013). OECD guidelines on measuring subjective well-being. OECD Publishing. Retrieved from http://dx.doi.org/10.1787/9789264191655-en.

  22. 22.

    Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment, 5(2), 164–172.

    Article  Google Scholar 

  23. 23.

    Diener, E. (1994). Assessing subjective well-being: Progress and opportunities. Social Indicators Research, 31, 103–157.

    Article  Google Scholar 

  24. 24.

    Barbato, A., Monzani, E., & Schiavi, T. (2004). Life satisfaction in a sample of outpatients with severe mental disorders: A survey in northern Italy. Quality of Life Research, 13, 969–973.

    PubMed  Article  Google Scholar 

  25. 25.

    Bjørnskov, C. (2010). How comparable are the Gallup World Poll life satisfaction data? Journal of Happiness Studies, 11(1), 41–60.

    Article  Google Scholar 

  26. 26.

    Organisation for Economic Cooperation and Development (OECD). (2015). Life satisfaction. Retrieved from http://www.oecdbetterlifeindex.org/topics/life-satisfaction/.

  27. 27.

    Idler, E. L., & Kasl, S. (1991). Health perceptions and survival: Do global evaluations of health status really predict mortality? Journal of Gerontology, 46(2), S55–S65.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Ferraro, K. F., Farmer, M. M., & Wybraniec, J. A. (1997). Health trajectories: Long-term dynamics among black and white adults. Journal of Health and Social Behavior, 38(1), 38–54.

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Kuusio, H., Heponiemi, T., Aalto, A. M., Sinervo, T., & Elovainio, M. (2012). Differences in well-being between GPs, medical specialists, and private physicians: The role of psychosocial factors. Health Services Research, 47(1), 68–85.

    PubMed  Article  Google Scholar 

  30. 30.

    Riise, H. K. R., Riise, T., Natvig, G. K., & Daltveit, A. K. (2014). Poor self-rated health associated with an increased risk of subsequent development of lung cancer. Quality of Life Research, 23, 145–153.

    PubMed  Article  Google Scholar 

  31. 31.

    Shi, L., Starfield, B., Politzer, R., & Regan, J. (2002). Primary care, self-rated health, and reductions in social disparities in health. Health Services Research, 37(3), 529–550.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    DeSalvo, K. B., Fan, V. S., McDonell, M. B., & Fihn, S. D. (2005). Predicting mortality and healthcare utilization with a single question. Health Services Research, 40(4), 1234–1246.

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Veenstra, M., Moum, T., & Garratt, A. M. (2006). Patient experiences with information in a hospital setting: Associations with coping and self-rated health in chronic illness. Quality of Life Research, 15, 967–978.

    PubMed  Article  Google Scholar 

  34. 34.

    Andersen, F. K., Christensen, K., & Frederiksen, H. (2007). Self-rated health and age: A cross-sectional and longitudinal study of 11,000 Danes aged 45–102. Scandinavian Journal of Public Health, 35(2), 164–171.

    PubMed  Article  Google Scholar 

  35. 35.

    Fleishman, J. A., & Cohen, J. W. (2010). Using information on clinical conditions to predict high-cost patients. Health Services Research, 45(2), 532–552.

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Mossey, J. M., & Shapiro, E. (1982). Self-rated health: A predictor of mortality among the elderly. American Journal of Public Health, 72(8), 800–808.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior, 38(1), 21–37.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Mohan, R., Beydoun, H. A., Beydoun, M. A., Barnes-Eley, M., Davis, J., Lance, R., & Schellhammer, P. (2011). Self-rated health as a tool for estimating health-adjusted life expectancy among patients newly diagnosed with localized prostate cancer: A preliminary study. Quality of Life Research, 20(5), 713–721.

    PubMed  Article  Google Scholar 

  39. 39.

    Kleinman, A. (1980). Patients and healers in the context of cultures. Berkeley, CA: University of California Press.

    Google Scholar 

  40. 40.

    Bury, M. (1982). Chronic illness as biological disruption. Sociology of Health and Illness, 12, 451–468.

    Google Scholar 

  41. 41.

    Zullig, K. J., & White, R. J. (2011). Physical activity, life satisfaction, and self-rated health of middle school students. Applied Research in Quality of Life, 6(3), 277–289.

    Article  Google Scholar 

  42. 42.

    Herman, K. M., Hopman, W. M., & Rosenberg, M. W. (2013). Self-rated health and life satisfaction among Canadian adults: associations of perceived weight status versus BMI. Quality of Life Research, 22, 2693–2705.

    PubMed  Article  Google Scholar 

  43. 43.

    Okun, M. A., & George, L. K. (1984). Physician-and self-ratings of health, neuroticism and subjective well-being among men and women. Personality and Individual Differences, 5(5), 533–539.

    Article  Google Scholar 

  44. 44.

    Lawton, M. P. (1977). Morale: What are we measuring? In C. N. Nydeggar (Ed.), Measuring morale: A guide to effective measurement. Washington, DC: The Gerontological Society.

    Google Scholar 

  45. 45.

    Elder, T. E. (2013). The predictive validity of subjective mortality expectations: evidence from the health and retirement study. Demography, 50(2), 569–589.

    PubMed  Article  Google Scholar 

  46. 46.

    Hurd, M. D., & McGarry, K. (1995). Evaluation of the subjective probabilities of survival in the health and retirement study. Journal of Human Resources, 30, S268–S292.

  47. 47.

    Perozek, M. (2008). Using subjective expectations to forecast longevity: Do survey respondents know something we don’t know? Demography, 45(1), 95–113.

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Van Doorn, C., & Kasl, S. V. (1998). Can parental longevity and self-rated life expectancy predict mortality among older persons? Results from an Australian cohort. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 53(1), S28–S34.

    Article  Google Scholar 

  49. 49.

    Lee, S., & Smith, J. (2016). Methodological aspects of subjective life expectancy: Effects of culture-specific reporting heterogeneity among older adults in the U.S. The Journals of Gerontology: Series B Psychological Sciences and Social Sciences, 71(3), 558–568.

  50. 50.

    Smith, T. W. (1982). Conditional order effect. GSS technical report. no. 33. Chicago, IL: NORC.

  51. 51.

    Strack, F., Martin, L. L., & Schwarz, N. (1988). Priming and communication: Social determinants of information use in judgments of life satisfaction. European Journal of Social Psychology, 18, 429–442.

    Article  Google Scholar 

  52. 52.

    Schwarz, N., Strack, F., & Mai, H. P. (1991). Assimilation and contrast effects in part–whole question sequences: A conversational logic analysis. Public Opinion Quarterly, 55(1), 3–23.

    Article  Google Scholar 

  53. 53.

    Grice, H. P. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and semantics 3: Speech acts (pp. 41–58). New York, NY: Academic Press.

    Google Scholar 

  54. 54.

    Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370.

    Article  Google Scholar 

  55. 55.

    Griffin, B., Loh, V., & Hesketh, B. (2013). A mental model of factors associated with subjective life expectancy. Social Science and Medicine, 82, 79–86.

    PubMed  Article  Google Scholar 

  56. 56.

    Pyszczynski, T., Greenberg, J., & Solomon, S. (1999). A dual-process model of defense against conscious and unconscious death-related thoughts: An extension of terror management theory. Psychological Review, 106(4), 835.

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Jonas, E., Schimel, J., Greenberg, J., & Pyszczynski, T. (2002). The Scrooge effect: Evidence that mortality salience increases prosocial attitudes and behavior. Personality and Social Psychology Bulletin, 28(10), 1342–1353.

    Article  Google Scholar 

  58. 58.

    Renkema, L. J., Stapel, D. A., & Van Yperen, N. W. (2008). Go with the flow: Conforming to others in the face of existential threat. European Journal of Social Psychology, 38(4), 747–756.

    Article  Google Scholar 

  59. 59.

    Han, S., Qin, J., & Ma, Y. (2010). Neurocognitive processes of linguistic cues related to death. Neuropsychologia, 48(12), 3436–3442.

    PubMed  Article  Google Scholar 

  60. 60.

    Klackl, J., Jonas, E., & Kronbichler, M. (2013). Existential neuroscience: Neurophysiological correlates of proximal defenses against death-related thoughts. Social Cognitive and Affective Neuroscience, 8(3), 333–340.

    PubMed  Article  Google Scholar 

  61. 61.

    Luo, S., Shi, Z., Yang, X., Wang, X., & Han, S. (2014). Reminders of mortality decrease midcingulate activity in response to others’ suffering. Social Cognitive and Affective Neuroscience, 9(4), 477–486.

    PubMed  Article  Google Scholar 

  62. 62.

    Mathews, P., & Sear, R. (2008). Life after death: An investigation into how mortality perceptions influence fertility preferences using evidence from an internet-based experiment. Journal of Evolutionary Psychology, 6(3), 155–172.

    Article  Google Scholar 

  63. 63.

    Murphy, J., Link, M. W., Childs, J. H., Tesfaye, C. L., Dean, E., Stern, M., & Schober, M. F. (2014). Social media in public opinion research: Report of the AAPOR task force on emerging technologies in public opinion research. American Association for Public Opinion Research. Retrieved from https://www.aapor.org/AAPORKentico/AAPOR_Main/media/MainSiteFiles/AAPOR_Social_Media_Report_FNL.pdf.

  64. 64.

    Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.

    PubMed  Article  Google Scholar 

  65. 65.

    Antoun, C., Zhang, C., Conrad, F. G., & Schober, M. F. (2015). Comparisons of online recruitment strategies for convenience samples: Craigslist, Google AdWords, Facebook, and Amazon Mechanical Turk. Field Methods,. doi:10.1177/1525822X15603149.

    Google Scholar 

  66. 66.

    Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Analysis, 20(3), 351–368.

    Article  Google Scholar 

  67. 67.

    Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411–419.

    Google Scholar 

  68. 68.

    Horton, J. J., & Chilton, L. B. (2010). The labor economics of paid crowdsourcing. In Proceedings of the 11th ACM Conference on Electronic Commerce (pp. 209–218). Association for Computing Machinery. Retrieved from http://arxiv.org/pdf/1001.0627.pdf.

  69. 69.

    Mason, W., & Suri, S. (2012). Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 44(1), 1–23.

    PubMed  Article  Google Scholar 

  70. 70.

    Shapiro, D. N., Chandler, J., & Mueller, P. A. (2013). Using Mechanical Turk to study clinical populations. Clinical Psychological Science, 1(2), 213–220.

    Article  Google Scholar 

  71. 71.

    Groves, R. M., Fultz, N. H., & Martin, E. (1992). Direct questioning about comprehension in a survey setting. Questions about questions: Inquiries into the cognitive bases of surveys. New York, NY: Russell Sage, 49–61.

  72. 72.

    Behr, D., Kaczmirek, L., Bandilla, W., & Braun, M. (2012). Asking probing questions in web surveys: Which factors have an impact on the quality of responses? Social Science Computer Review, 30(4), 487–498.

    Article  Google Scholar 

  73. 73.

    Bassili, J. N., & Scott, B. S. (1996). Response latency as a signal to question problems in survey research. Public Opinion Quarterly, 60(3), 390–399.

    Article  Google Scholar 

  74. 74.

    Tourangeau, R., Couper, M. P., & Conrad, F. G. (2004). Spacing, position, and order: Interpretive heuristics for visual features of survey questions. Public Opinion Quarterly, 68, 368–393.

    Article  Google Scholar 

  75. 75.

    Malhotra, N. (2008). Completion time and response order effects in web surveys. Public Opinion Quarterly, 72(5), 914–934.

    Article  Google Scholar 

  76. 76.

    Yan, T., & Tourangeau, R. (2008). Fast times and easy questions: The effects of age, experience and question complexity on web survey response times. Applied Cognitive Psychology, 22(1), 51–68.

    Article  Google Scholar 

  77. 77.

    Shannon, C. E. (1948). A mathematical theory of communication. The Bell Systems Technical Journal, 27, 379–423 and 623–656.

  78. 78.

    Fisher, R. A. (1921). On the probable error of a coefficient of correlation deduced from a small sample. Metron, 1, 3–32.

    Google Scholar 

  79. 79.

    Silver, N. C., & Dunlap, W. P. (1987). Averaging correlation coefficients: Should Fisher’s z transformation be used? Journal of Applied Psychology, 72(1), 146.

    Article  Google Scholar 

  80. 80.

    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

Download references

Funding

The data used in this study were collected with funding from the Regula Herzog Young Investigator Fund awarded to Sunghee Lee.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sunghee Lee.

Ethics declarations

Conflict of interest

All authors declare no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study protocol was approved by the Institutional Review Board at the University of Michigan.

Appendices

Appendix 1. Description of experimental design

Main experiments

Experiment 1

In the original study, there were six conditions as in Table 4. While there is no clear guidance, some variations of the question order as in Condition 1 appear to be practiced (e.g., Health and Retirement Study). Hence, we randomly assigned 2/7 of the sample to Condition 1 and 1/7 to the remaining conditions. For this study, Condition 2 is not applicable as it was implemented to study the effect of response scales on measuring self-rated health, hence, removed from the analysis. Conditions 3, 4, and 5 collectively asked life satisfaction immediately after self-rated health. These three conditions produced similar correlation coefficients between self-rated health and life satisfaction and, hence, were grouped into one. Condition 6 asked self-rated health immediately after life satisfaction and was retained as a separate condition in the analysis.

Table 4 Comparison of original experimental conditions and presented conditions

Experiment 2

Subjective life expectancy was asked in two different questions in the original study: one using percent chance, and the other using actual years in age (See Appendix 2 for details of the wording). As the one with percent chance is used more popularly, we assign 2/3 of the sample to the percent chance wording (conditions 1 and 2 in Table 4) and the remainder to the actual years wording (conditions 3 and 4). Within each subjective life expectancy wording, we randomly split the sample half and asked subjective life expectancy either before parental mortality questions (conditions 1 and 3) or after them (conditions 2 and 4). Due to the differences in the question type, we dropped conditions 3 and 4 from the analysis.

Ancillary experiments

The following experiments were implemented in the survey and not included in this study as they address aspects other than question contexts.

Experiment 3: response scale for self-rated health

Condition 2 in Table 4.I was implemented to be compared to Condition 1 only on the distribution of self-rated health.

Experiment 4: types of anchoring vignettes for self-rated health

Different types of vignettes questions were asked in order to examine whether vignette types affect the adjustment of self-rated health.

Experiment 5: question types of subjective life expectancy

Although labeled as “subjective life expectancy” in the literature, some use actual age and others use percent chance. This experiment was included to examine the difference in the question type of subjective life expectancy.

Appendix 2. Question wording

Self-rated health with a 5-point response scale

Would you say your health is excellent, very good, good, fair, or poor in general?

□ Excellent

□ Very good

□ Good

□ Fair

□ Poor

Self-rated health with a 100-point response scale

On a scale from 0 to 100, where 0 is worst possible health and 100 is perfect possible health, how would you rate your health in general? Please provide the number (from 0 to 100).

________

Life satisfaction

Please think about your life and situation right now. How satisfied are you with your life as a whole these days?

□ Completely satisfied

□ Very satisfied

□ Somewhat satisfied

□ Not very satisfied

□ Not at all satisfied

Vignette introduction

We are interested in how you would rate the health of other people your age. Now we are going to describe the health of some people your age and then ask you to rate their health in the way you would to rate your own health.

Physical health vignettes

[Randomize: Mary/Steve] has no problem with walking, running, or moving his/her limbs. [She/He] jogs 3 miles twice a week. The last time [she/he] spent time in bed due to illness was more than a year ago. In general, would you say [Mary’s/Steve’s] health is excellent, very good, good, fair, or poor?

□ Excellent

□ Very good

□ Good

□ Fair

□ Poor

[Kate/Mike] has no problem walking 1 mile. Occasionally, [she/he] feels fatigued and has some trouble bending and lifting, but his/her occasional pain does not affect his/her daily activities. [She/He] spent a couple of days in bed due to illness last year. In general, would you say [Kate’s/Mike’s] health is excellent, very good, good, fair, or poor?

[Nancy/John] has no problem walking a couple of blocks or climbing a couple of flights of stairs. However, [she/he] feels tired after walking or climbing stairs. He/she has a great deal of trouble with daily activities. Each week, [she/he] experiences pain that limit some of his/her daily activities. [She/He] spent a week in bed due to illness last year. In general, would you say [Nancy’s/John’s] health is excellent, very good, good, fair, or poor?

Mental health vignettes

[Jennifer/Peter] enjoys his/her work and social activities and is generally satisfied with [her/his] life. [She/He] gets depressed rarely and loses interest in what [she/he] usually enjoys. In general, would you say [Jennifer’s/Peter’s] health is excellent, very good, good, fair, or poor?

[Kelly/David] enjoys social activities, but does not like [her/his] work. Although [she/he] is content with [her/his] life, [she/he] gets depressed once a month or so but is able to carry on with [her/his] day-to-day activities. In general, would you say [Kelly’s/David’s] health is excellent, very good, good, fair, or poor?

[Jane/Jack] feels depressed most of the time. [She/He] weeps frequently and feels hopeless about the future. [She/He] feels that [she/he] has become a burden on others and that [she/he] would be better dead. In general, would you say [Jane’s/Jack’s] health is excellent, very good, good, fair, or poor?

Combined physical and mental health vignettes

[Jennifer/Peter] enjoys [her/his] work and social activities and is generally satisfied with [her/his] life. [She/He] has no problem with walking, running, or moving [her/his] limbs and jogs 3 miles twice a week. In general, would you say [Jennifer’s/Peter’s] health is excellent, very good, good, fair, or poor?

[Kelly/David] feels fatigued occasionally and has some trouble bending and lifting, but [her/his] occasional pain does not affect [her/his] daily activities. Although [she/he] is content with [her/his] life, [she/he] gets depressed once a month or so. In general, would you say [Kelly’s/David’s] health is excellent, very good, good, fair, or poor?

[Jane/Jack] feels depressed most of the time. Although [she/he] has no problem walking a couple of blocks or climbing a couple of flights of stairs, [she/he] feels tired after walking or climbing stairs. In general, would you say [Jane’s/Jack’s] health is excellent, very good, good, fair, or poor?

Chronic conditions

Next, we’d like to learn more about you. Has a doctor ever told you that you have diabetes or high blood sugar?

□ Yes

□ No

Has a doctor ever told you that you have asthma?

□ Yes

□ No

Has a doctor ever told you that you have high blood pressure or hypertension?

□ Yes

□ No

Subjective life expectancy with percent chance response

The next few questions ask about your views of the chances that various events will happen. Your answers can range from zero to one hundred, where zero means there is absolutely no chance, and one hundred means that it is absolutely certain. For example, when weather forecasters report the chance of rain, a number like 20 % means “a small chance,” a number around 50 % means “a pretty even chance,” and a number like 80 % means “a very good chance.”

What is the percent chance that you will live to be [75 or more (IF AGE IS ≤ 64)/80 or more (IF AGE IS 65–69)/

85 or more (IF AGE IS 70–74)/90 or more (IF AGE IS 75–79)/95 or more (IF AGE IS 80–84)/100 or more (IF AGE IS ≥ 85)]? If you can’t think of an answer, you can choose “Don’t know.”

________ percent chance

□ Don’t know

Subjective life expectancy with actual age response

Now, we want to ask you about your expectations that various events will happen.To what age do you expect to live? If you can’t think of an answer, you can choose “Don’t know.”

_______ years old

□ Don’t know

Probing for subjective life expectancy with percent chance response

{SHOW IF SUBJECTIVE LIFE EXPECTANCY IS ANSWERED} You said there is a [FILL ANSWER FROM SUBJECTIVE LIFE EXPECTANCY] % chance that you will live to be [FILL AGE USED IN SUBJECTIVE LIFE EXPECTANCY] or more. How did you arrive at this answer? What did you consider to say that there is a [FILL ANSWER FROM SUBJECTIVE LIFE EXPECTANCY] % chance that you will live to be [FILL AGE USED IN SUBJECTIVE LIFE EXPECTANCY] or more?

______________________________________________________________

{SHOW IF SUBJECTIVE LIFE EXPECTANCY = Don’t know} You said you don’t know the chance that you will live to be [FILL AGE USED IN SUBJECTIVE LIFE EXPECTANCY] or more. How did you arrive at this answer? What made you say “don’t know”?

_____________________________________________________________

Parental mortality

Is your mother alive now?

□ Yes

□ No

{SHOW IF MOTHER NOT ALIVE} How old was your mother when she passed away?

_________ years old

Is your father alive now?

□ Yes

□ No

{SHOW IF FATHER NOT ALIVE} How old was your father when he passed away?

_________ years old

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lee, S., McClain, C., Webster, N. et al. Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated health, and subjective life expectancy in survey instruments. Qual Life Res 25, 2497–2510 (2016). https://doi.org/10.1007/s11136-016-1304-8

Download citation

Keywords

  • Instrument design
  • Question order
  • Self-rated health
  • Global life satisfaction
  • Subjective life expectancy
  • Survey research