Quality of Life Research

, Volume 25, Issue 3, pp 497–506 | Cite as

Ambulatory and diary methods can facilitate the measurement of patient-reported outcomes




Ambulatory and diary methods of self-reported symptoms and well-being have received increasing interest in recent years. These methods are a valuable addition to traditional strategies for the assessment of patient-reported outcomes (PROs) in that they capture patients’ recent symptom experiences repeatedly in their natural environments. In this article, we review ways that incorporating diary methods into PRO measurement can facilitate research on quality of life.


Several diary methods are currently available, and they include “real-time” (Ecological Momentary Assessment) and “near-real-time” (end-of-day assessments, Day Reconstruction Method) formats. We identify the key benefits of these methods for PRO research.


(1) In validity testing, diary assessments can serve as a standard for evaluating the ecological validity and for identifying recall biases of PRO instruments with longer-term recall formats. (2) In research and clinical settings, diaries have the ability to closely capture variations and dynamic changes in quality of life that are difficult or not possible to obtain from traditional PRO assessments. (3) In test construction, repeated diary assessments can expand understanding of the measurement characteristics (e.g., reliability, dimensionality) of PROs in that parameters for differences between people can be compared with those for variation within people.


Diary assessment strategies can enrich the repertoire of PRO assessment tools and enhance the measurement of patients’ quality of life.


Patient-reported outcomes Ambulatory measurement Diaries Ecological Momentary Assessment Day Reconstruction Method Recall 



This work was supported by Grants from the National Institute on Aging and the National Bureau of Economic Research, R01 AG042407-01A1, 5R01AG040629, and P01 AG05842.

Compliance with Ethical Standards

Conflict of interest

S.S. declares that he has no conflict of interest. A.A.S. is a Senior Scientist with the Gallup Organization and a Senior Consultant with ERT, Inc.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Stone, A. A., & Shiffman, S. (1994). Ecological Momentary Assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16, 199–202.Google Scholar
  2. 2.
    Bhattacharyya, M. R., Whitehead, D. L., Rakhit, R., & Steptoe, A. (2008). Depressed mood, positive affect, and heart rate variability in patients with suspected coronary artery disease. Psychosomatic Medicine, 70, 1020–1027.CrossRefPubMedGoogle Scholar
  3. 3.
    Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A. (2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences, 107, 9985–9990.CrossRefGoogle Scholar
  4. 4.
    Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences, 107, 16489–16493.CrossRefGoogle Scholar
  5. 5.
    Steptoe, A., & Wardle, J. (2011). Positive affect measured using ecological momentary assessment and survival in older men and women. Proceedings of the National Academy of Sciences, 108, 18244–18248.CrossRefGoogle Scholar
  6. 6.
    Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The Day Reconstruction Method. Science, 306, 1776–1780.CrossRefPubMedGoogle Scholar
  7. 7.
    Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.CrossRefPubMedGoogle Scholar
  8. 8.
    Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616.CrossRefPubMedGoogle Scholar
  9. 9.
    Stone, A. A., Shiffman, S., Atienza, A., & Nebling, L. (Eds.). (2007). The science of real-time data capture: Self-reports in health research. New Yrok: Oxford University Press.Google Scholar
  10. 10.
    Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21, 463–475.CrossRefPubMedGoogle Scholar
  11. 11.
    aan het Rot, M., Hogenelst, K., & Schoevers, R. A. (2012). Mood disorders in everyday life: A systematic review of experience sampling and ecological momentary assessment studies. Clinical Psychology Review, 32, 510–523.CrossRefPubMedGoogle Scholar
  12. 12.
    Walz, L. C., Nauta, M. H., & aan het Rot, M. (2014). Experience sampling and ecological momentary assessment for studying the daily lives of patients with anxiety disorders: A systematic review. Journal of Anxiety Disorders, 28, 925–937.CrossRefPubMedGoogle Scholar
  13. 13.
    Stone, A. A., Schwartz, J. E., Schkade, D., Schwarz, N., Krueger, A., & Kahneman, D. (2006). A population approach to the study of emotion: Diurnal rhythms of a working day examined with the Day Reconstruction Method. Emotion, 6, 139–149.CrossRefPubMedGoogle Scholar
  14. 14.
    Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., & Ryan, R. M. (2000). Daily well-being: The role of autonomy, competence, and relatedness. Personality and Social Psychology Bulletin, 26, 419–435.CrossRefGoogle Scholar
  15. 15.
    Carstensen, L. L., Pasupathi, M., Mayr, U., & Nesselroade, J. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79, 644–655.CrossRefPubMedGoogle Scholar
  16. 16.
    Ram, N., & Gerstorf, D. (2009). Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24, 778–791.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Burton, C., Weller, D., & Sharpe, M. (2007). Are electronic diaries useful for symptoms research? A systematic review. Journal of Psychosomatic Research, 62, 553–561.CrossRefPubMedGoogle Scholar
  18. 18.
    Conner, T. S., & Barrett, L. F. (2012). Trends in ambulatory self-report: The role of momentary experience in psychosomatic medicine. Psychosomatic Medicine, 74, 327–337.PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Kubiak, T., & Stone, A. A. (2012). Ambulatory monitoring of biobehavioral processes in health and disease. Psychosomatic Medicine, 74, 325–326.CrossRefPubMedGoogle Scholar
  20. 20.
    Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128, 934–960.CrossRefPubMedGoogle Scholar
  21. 21.
    Stull, D. E., Leidy, N. K., Parasuraman, B., & Chassany, O. (2009). Optimal recall periods for patient-reported outcomes: Challenges and potential solutions. Current Medical Research and Opinion, 25, 929–942.CrossRefPubMedGoogle Scholar
  22. 22.
    Dunn, S. M., Bryson, J. M., Hoskins, P. L., Alford, J. B., Handelsman, D. J., & Turtle, J. R. (1984). Development of the diabetes knowledge (DKN) scales: forms DKNA, DKNB, and DKNC. Diabetes Care, 7, 36–41.CrossRefPubMedGoogle Scholar
  23. 23.
    Craig, B. M., Busschbach, J. J., & Salomon, J. A. (2009). Modeling ranking, time trade-off and visual analogue scale values for EQ-5D health states: A review and comparison of methods. Medical Care, 47, 634–641.PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Hays, R. D., Bjorner, J. B., Revicki, D. A., Spritzer, K. L., & Cella, D. (2009). Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Quality of Life Research, 18, 873–880.PubMedCentralCrossRefPubMedGoogle Scholar
  25. 25.
    Bruce, B., & Fries, J. (2005). The health assessment questionnaire (HAQ). Clinical and Experimental Rheumatology, 23, S14–S18.PubMedGoogle Scholar
  26. 26.
    Schwarz, N. (2007). Retrospective and concurrent self-reports: The rationale for real-time data capture. In A. A. Stone, S. S. Shiffman, A. Atienza, & L. Nebeling (Eds.), The science of real-time data capture: Self-reports in health research (pp. 11–26). New York: Oxford University Press.Google Scholar
  27. 27.
    Ware, J. E. (1993). SF-36 health survey: Manual & interpretive guide. Boston: The Health Institute, New England Medical Center.Google Scholar
  28. 28.
    Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63, 1179–1194.PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    McFarland, C., Ross, M., & DeCourville, N. (1989). Women’s theories of menstruation and biases in recall of menstrual symptoms. Journal of Personality and Social Psychology, 57, 522–531.CrossRefPubMedGoogle Scholar
  30. 30.
    Barrett, L. F. (1997). The relationships among momentary emotion experiences, personality descriptions, and retrospective ratings of emotion. Personality and Social Psychology Bulletin, 23, 1100–1110.CrossRefGoogle Scholar
  31. 31.
    U.S. Department of Health and Human Services Food and Drug Administration. (2009). Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims. http://www.fda.gov. Accessed March 22, 2010.
  32. 32.
    Broderick, J. E. (2008). Electronic diaries: Appraisal and current status. Pharmaceutical Medicine, 22, 69–74.PubMedCentralCrossRefPubMedGoogle Scholar
  33. 33.
    Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176.PubMedCentralCrossRefPubMedGoogle Scholar
  34. 34.
    Bussmann, J. B., & Ebner-Priemer, U. W. (2012). Ambulatory assessment of movement behavior. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 235–250). New York, NY: The Guilford Press.Google Scholar
  35. 35.
    Broderick, J. E., Schwartz, J. E., Schneider, S., & Stone, A. A. (2009). Can end-of-day reports replace momentary assessment of pain and fatigue? Journal of Pain, 10, 274–281.PubMedCentralCrossRefPubMedGoogle Scholar
  36. 36.
    Broderick, J. E., Schneider, S., Schwartz, J. E., & Stone, A. A. (2010). Interference with activities due to pain and fatigue: Accuracy of ratings across different reporting periods. Quality of Life Research, 19, 1163–1170.PubMedCentralCrossRefPubMedGoogle Scholar
  37. 37.
    Schlatter, M. C., & Cameron, L. D. (2010). Emotional suppression tendencies as predictors of symptoms, mood, and coping appraisals during AC chemotherapy for breast cancer treatment. Annals of Behavioral Medicine, 40, 15–29.CrossRefPubMedGoogle Scholar
  38. 38.
    Junghaenel, D. U., Schneider, S., Stone, A. A., Christodoulou, C., & Broderick, J. E. (2014). Ecological validity and clinical utility of patient-reported outcomes measurement information system (PROMIS®) instruments for detecting premenstrual symptoms of depression, anger, and fatigue. Journal of Psychosomatic Research, 76, 300–306.PubMedCentralCrossRefPubMedGoogle Scholar
  39. 39.
    Murphy, S. L., Smith, D. M., Clauw, D. J., & Alexander, N. B. (2008). The impact of momentary pain and fatigue on physical activity in women with osteoarthritis. Arthritis Care Res (Hoboken), 59, 849–856.CrossRefGoogle Scholar
  40. 40.
    Broderick, J. E., Schneider, S., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Validity and reliability of patient-reported outcomes measurement information system (PROMIS) instruments in osteoarthritis. Arthritis Care & Research, 65, 1625–1633.Google Scholar
  41. 41.
    Daly, M., Delaney, L., Doran, P. P., Harmon, C., & MacLachlan, M. (2010). Naturalistic monitoring of the affect-heart rate relationship: A day reconstruction study. Health Psychology, 29, 186–195.CrossRefPubMedGoogle Scholar
  42. 42.
    Daly, M., Baumeister, R. F., Delaney, L., & MacLachlan, M. (2014). Self-control and its relation to emotions and psychobiology: Evidence from a Day Reconstruction Method study. Journal of Behavioral Medicine, 37, 81–93.CrossRefPubMedGoogle Scholar
  43. 43.
    Dolan, P., & Kudrna, L. (2015). More years, less yawns: Fresh evidence on tiredness by age and other factors. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70, 576–580.Google Scholar
  44. 44.
    Smith, J., Ryan, L. H., Queen, T. L., Becker, S., & Gonzalez, R. (2014). Snapshots of mixtures of affective experiences in a day: Findings from the health and retirement study. Journal of population ageing, 7, 55–79.PubMedCentralCrossRefPubMedGoogle Scholar
  45. 45.
    Broderick, J. E., Schwartz, J. E., Vikingstad, G., Pribbernow, M., Grossman, S., & Stone, A. A. (2008). The accuracy of pain and fatigue items across different reporting periods. Pain, 139, 146–157.PubMedCentralCrossRefPubMedGoogle Scholar
  46. 46.
    Stone, A. A., Broderick, J. E., Shiffman, S. S., & Schwartz, J. E. (2004). Understanding recall of weekly pain from a momentary assessment perspective: Absolute agreement, between- and within-person consistency, and judged change in weekly pain. Pain, 107, 61–69.CrossRefPubMedGoogle Scholar
  47. 47.
    Miron-Shatz, T., Stone, A., & Kahneman, D. (2009). Memories of yesterday’s emotions: Does the valence of experience affect the memory-experience gap? Emotion, 9, 885–891.CrossRefPubMedGoogle Scholar
  48. 48.
    Shrier, L. A., Shih, M. C., & Beardslee, W. R. (2005). Affect and sexual behavior in adolescents: A review of the literature and comparison of momentary sampling with diary and retrospective self-report methods of measurement. Pediatrics, 115, e573–e581.PubMedCentralCrossRefPubMedGoogle Scholar
  49. 49.
    Bennett, A. V., Amtmann, D., Diehr, P., & Patrick, D. L. (2012). Comparison of 7-day recall and daily diary reports of COPD symptoms and impacts. Value in Health, 15, 466–474.CrossRefPubMedGoogle Scholar
  50. 50.
    Houtveen, J. H., & Oei, N. Y. (2007). Recall bias in reporting medically unexplained symptoms comes from semantic memory. Journal of Psychosomatic Research, 62, 277–282.CrossRefPubMedGoogle Scholar
  51. 51.
    Van den Brink, M., Bandell-Hoekstra, E., & Abu-Saad, H. H. (2001). The occurrence of recall bias in pediatric headache: A comparison of questionnaire and diary data. Headache: The Journal of Head and Face Pain, 41, 11–20.CrossRefGoogle Scholar
  52. 52.
    Homma, Y., Ando, T., Yoshida, M., Kageyama, S., Takei, M., Kimoto, K., et al. (2002). Voiding and incontinence frequencies: Variability of diary data and required diary length. Neurourology and Urodynamics, 21, 204–209.CrossRefPubMedGoogle Scholar
  53. 53.
    Shiffman, S. (2009). How many cigarettes did you smoke? Assessing cigarette consumption by global report, time-line follow-back, and ecological momentary assessment. Health Psychology, 28, 519–526.PubMedCentralCrossRefPubMedGoogle Scholar
  54. 54.
    Carney, M. A., Tennen, H., Affleck, G., del Boca, F. K., & Kranzler, H. R. (1998). Levels and patterns of alcohol consumption using timeline follow-back, daily diaries and real-time. Journal of Studies on Alcohol and Drugs, 59, 447–454.CrossRefGoogle Scholar
  55. 55.
    Litt, M. D., Cooney, N. L., & Morse, P. (1998). Ecological momentary assessment (EMA) with treated alcoholics: Methodological problems and potential solutions. Health Psychology, 17, 48–52.CrossRefPubMedGoogle Scholar
  56. 56.
    Bennett, A., Patrick, D., Bushnell, D., Chiou, C., & Diehr, P. (2011). Comparison of 7-day and repeated 24-h recall of type 2 diabetes. Quality of Life Research, 20, 769–777.CrossRefPubMedGoogle Scholar
  57. 57.
    Jamison, R. N., Raymond, S. A., Slawsby, E. A., McHugo, G. J., & Baird, J. C. (2006). Pain assessment in patients with low back pain: Comparison of weekly recall and momentary electronic data. Journal of Pain, 7, 192–199.CrossRefPubMedGoogle Scholar
  58. 58.
    Stel, V. S., Smit, J. H., Pluijm, S. M. F., Visser, M., Deeg, D. J. H., & Lips, P. (2004). Comparison of the LASA physical activity questionnaire with a 7-day diary and pedometer. Journal of Clinical Epidemiology, 57, 252–258.CrossRefPubMedGoogle Scholar
  59. 59.
    Cella, D., & Stone, A. A. (2015). Health-related quality of life measurement in oncology: Advances and opportunities. American Psychologist, 70, 175–185.CrossRefPubMedGoogle Scholar
  60. 60.
    Bogaerts, K., Wan, L., Van Diest, I., Stans, L., Decramer, M., & Van den Bergh, O. (2012). Peak-end memory bias in laboratory-induced dyspnea: A comparison of patients with medically unexplained symptoms and healthy controls. Psychosomatic Medicine, 74, 974–981.CrossRefPubMedGoogle Scholar
  61. 61.
    Stone, A. A., Broderick, J. E., Kaell, A. T., DelesPaul, P. A. E. G., & Porter, L. E. (2000). Does the peak-end phenomenon observed in laboratory pain studies apply to real-world pain in rheumatoid arthritics? Journal of Pain, 1, 212–217.CrossRefPubMedGoogle Scholar
  62. 62.
    Redelmeier, D. A., Katz, J., & Kahneman, D. (2003). Memories of colonoscopy: A randomized trial. Pain, 104, 187–194.CrossRefPubMedGoogle Scholar
  63. 63.
    Kent, G. (1985). Memory of dental pain. Pain, 21, 187–194.CrossRefPubMedGoogle Scholar
  64. 64.
    Stone, A. A., Schwartz, J. E., Broderick, J. E., & Shiffman, S. S. (2005). Variability of momentary pain predicts recall of weekly pain: A consequence of the peak (or salience) memory heuristic. Personality and Social Psychology Bulletin, 31, 1340–1346.CrossRefPubMedGoogle Scholar
  65. 65.
    Sohl, S. J., & Friedberg, F. (2008). Memory for fatigue in chronic fatigue syndrome: Relationships to fatigue variability, catastrophizing, and negative affect. Behavioral Medicine, 34, 29–38.PubMedCentralCrossRefPubMedGoogle Scholar
  66. 66.
    Schneider, S., Broderick, J. E., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Temporal trends in symptom experience predict the accuracy of recall PROs. Journal of Psychosomatic Research, 75, 160–166.PubMedCentralCrossRefPubMedGoogle Scholar
  67. 67.
    Smith, W. B., & Safer, M. A. (1993). Effects of present pain level on recall of chronic pain and medication use. Pain, 55, 355–361.CrossRefPubMedGoogle Scholar
  68. 68.
    Begg, A., Drummond, G., & Tiplady, B. (2003). Assessment of postsurgical recovery after discharge using a pen computer diary. Anaesthesia, 58, 1101–1105.CrossRefPubMedGoogle Scholar
  69. 69.
    Chapman, C. R., Davis, J., Donaldson, G. W., Naylor, J., & Winchester, D. (2011). Postoperative pain trajectories in chronic pain patients undergoing surgery: The effects of chronic opioid pharmacotherapy on acute pain. Journal of Pain, 12, 1240–1246.CrossRefPubMedGoogle Scholar
  70. 70.
    Jim, H. S., Small, B., Faul, L. A., Franzen, J., Apte, S., & Jacobsen, P. B. (2011). Fatigue, depression, sleep, and activity during chemotherapy: Daily and intraday variation and relationships among symptom changes. Annals of Behavioral Medicine, 42, 321–333.PubMedCentralCrossRefPubMedGoogle Scholar
  71. 71.
    Junghaenel, D. U., Cohen, J., Schneider, S., Neerukonda, A. R., & Broderick, J. E. (2015). Identification of distinct fatigue trajectories in patients with breast cancer undergoing adjuvant chemotherapy. Supportive Care in Cancer. doi: 10.1007/s00520-015-2616-x.
  72. 72.
    Shiffman, S. (2009). Ecological momentary assessment (EMA) in studies of substance use. Psychological Assessment, 21, 486–497.PubMedCentralCrossRefPubMedGoogle Scholar
  73. 73.
    Almeida, D. M. (2005). Resilience and vulnerability to daily stressors assessed via diary methods. Current Directions in Psychological Science, 14, 62–68.CrossRefGoogle Scholar
  74. 74.
    Edwards, R. R., Almeida, D. M., Klick, B., Haythornthwaite, J. A., & Smith, M. T. (2008). Duration of sleep contributes to next-day pain report in the general population. Pain, 137, 202–207.PubMedCentralCrossRefPubMedGoogle Scholar
  75. 75.
    Cranford, J. A., Shrout, P. E., Iida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin, 32, 917–929.PubMedCentralCrossRefPubMedGoogle Scholar
  76. 76.
    Schneider, S., Junghaenel, D. U., Keefe, F. J., Schwartz, J. E., Stone, A. A., & Broderick, J. E. (2012). Individual differences in the day-to-day variability of pain, fatigue, and well-being in patients with rheumatic disease: Associations with psychological variables. Pain, 153, 813–822.PubMedCentralCrossRefPubMedGoogle Scholar
  77. 77.
    Barge-Schaapveld, D. Q., Nicolson, N. A., & Berkhof, J. (1999). Quality of life in depression: Daily life determinants and variability. Psychiatry Research, 88, 173–189.CrossRefPubMedGoogle Scholar
  78. 78.
    Maes, I. H., Delespaul, P. A., Peters, M. L., White, M. P., van Horn, Y., Schruers, K., et al. (2015). Measuring health-related quality of life by experiences: The experience sampling method. Value in Health, 18, 44–51.CrossRefPubMedGoogle Scholar
  79. 79.
    Nesselroade, J. R., & Ram, N. (2004). Studying intraindividual variability: What we have learned that will help us understand lives in context. Research in Human Development, 1, 9–29.CrossRefGoogle Scholar
  80. 80.
    Zautra, A. J., Fasman, R., Parish, B. P., & Davis, M. C. (2007). Daily fatigue in women with osteoarthritis, rheumatoid arthritis, and fibromyalgia. Pain, 128, 128–135.CrossRefPubMedGoogle Scholar
  81. 81.
    Trull, T. J., Solhan, M. B., Tragesser, S. L., Jahng, S., Wood, P. K., Piasecki, T. M., et al. (2008). Affective instability: Measuring a core feature of borderline personality disorder with ecological momentary assessment. Journal of Abnormal Psychology, 117, 647–661.CrossRefPubMedGoogle Scholar
  82. 82.
    MacDonald, S. W., Hultsch, D. F., & Dixon, R. A. (2003). Performance variability is related to change in cognition: Evidence from the Victoria Longitudinal Study. Psychology and Aging, 18, 510–523.CrossRefPubMedGoogle Scholar
  83. 83.
    Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., & Rowe, J. W. (1997). Intraindividual variability in perceived control in a older sample: The MacArthur successful aging studies. Psychology and Aging, 12, 489–502.CrossRefPubMedGoogle Scholar
  84. 84.
    Chow, S.-M., Ram, N., Boker, S. M., Fujita, F., & Clore, G. (2005). Emotion as a thermostat: Representing emotion regulation using a damped oscillator model. Emotion, 5, 208–225.CrossRefPubMedGoogle Scholar
  85. 85.
    Boker, S. M., & Nesselroade, J. R. (2002). A method for modeling the intrinsic dynamics of intraindividual variability: Recovering the parameters of simulated oscillators in multi-wave panel data. Multivariate Behavioral Research, 37, 127–160.CrossRefPubMedGoogle Scholar
  86. 86.
    Smolensky, M. H., & D’alonzo, G. E. (1993). Medical chronobiology: Concepts and applications. American Review of Respiratory Disease, 147, S2–S19.CrossRefPubMedGoogle Scholar
  87. 87.
    Jamison, R. N., & Brown, G. K. (1991). Validation of hourly pain intensity profiles with chronic pain patients. Pain, 45, 123–128.CrossRefPubMedGoogle Scholar
  88. 88.
    Stone, A. A., Broderick, J. E., Schneider, S., & Schwartz, J. E. (2012). Expanding options for developing outcome measures from momentary assessment data. Psychosomatic Medicine, 74, 387–397.CrossRefPubMedGoogle Scholar
  89. 89.
    Affleck, G., Tennen, H., Urrows, S., & Higgins, P. (1991). Individual differences in the day-to-day experience of chronic pain: A prospective daily study of rheumatoid arthritis patients. Health Psychology, 10, 419–426.CrossRefPubMedGoogle Scholar
  90. 90.
    Fayers, P., & Machin, D. (2013). Quality of life: The assessment, analysis and interpretation of patient-reported outcomes. Wichester, West Sussex: Wiley.Google Scholar
  91. 91.
    Mehta, P. D., & Neale, M. C. (2005). People are variables too: Multilevel structural equations modeling. Psychological Methods, 10, 259–284.CrossRefPubMedGoogle Scholar
  92. 92.
    Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201–218.Google Scholar
  93. 93.
    Velicer, W. F., Babbin, S. F., & Palumbo, R. (2014). Idiographic applications: Issues of ergodicity and generalizability. In P. C. M. Molenaar, R. M. Lerner, & K. M. Newell (Eds.), Handbook of developmental systems theory and methodology (pp. 425–441). New York, NY: Guilford Press.Google Scholar
  94. 94.
    Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19, 72–91.CrossRefPubMedGoogle Scholar
  95. 95.
    Mäkikangas, A., Kinnunen, S., Rantanen, J., Mauno, S., Tolvanen, A., & Bakker, A. B. (2014). Association between vigor and exhaustion during the workweek: A person-centered approach to daily assessments. Anxiety, Stress, & Coping, 27, 555–575.CrossRefGoogle Scholar
  96. 96.
    Church, A. T., Katigbak, M. S., Ching, C. M., Zhang, H., Shen, J., Arias, R. M., et al. (2013). Within-individual variability in self-concepts and personality states: Applying density distribution and situation-behavior approaches across cultures. Journal of Research in Personality, 47, 922–935.CrossRefGoogle Scholar
  97. 97.
    Roesch, S. C., Aldridge, A. A., Stocking, S. N., Villodas, F., Leung, Q., Bartley, C. E., et al. (2010). Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation. Multivariate Behavioral Research, 45, 767–789.PubMedCentralCrossRefPubMedGoogle Scholar
  98. 98.
    Schneider, S., Choi, S. W., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Psychometric characteristics of daily diaries for the patient-reported outcomes measurement information system (PROMIS®): a preliminary investigation. Quality of Life Research, 22, 1859–1869.PubMedCentralCrossRefPubMedGoogle Scholar
  99. 99.
    Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Medical Care, 44, S115–S123.CrossRefPubMedGoogle Scholar
  100. 100.
    Roussos, L., & Stout, W. (1996). A multidimensionality-based DIF analysis paradigm. Applied Psychological Measurement, 20, 355–371.CrossRefGoogle Scholar
  101. 101.
    Zumbo, B. D. (2007). Three generations of DIF analyses: Considering where it has been, where it is now, and where it is going. Language Assessment Quarterly, 4, 223–233.CrossRefGoogle Scholar
  102. 102.
    Asparouhov, T., & Muthén, B. (2012). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. Paper presented at the annual meeting of the National Council on Measurement in Education.Google Scholar
  103. 103.
    De Boeck, P. (2008). Random item IRT models. Psychometrika, 73, 533–559.CrossRefGoogle Scholar
  104. 104.
    Verbrugge, L. M. (1980). Health diaries. Medical Care, 18, 73–95.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Dornsife Center for Self-Report Science, Center for Economic and Social ResearchUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations