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Cited by 444Evaluation and Performance AssessmentAdvanced Causal Inference TechniquesHealth Policy Implementation Science

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The Value of Interrupted Time-Series Experiments for Community Intervention Research

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Biglan, Anthony, Ary, Dennis, Wagenaar, Alexander C.·2000·Prevention Science
DOI 10.1023/a:1010024016308PMC PMC4553062
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Abstract

Greater use of interrupted time-series experiments is advocated for community intervention research. Time-series designs enable the development of knowledge about the effects of community interventions and policies in circumstances in which randomized controlled trials are too expensive, premature, or simply impractical. The multiple baseline time-series design typically involves two or more communities that are repeatedly assessed, with the intervention introduced into one community at a time. It is particularly well suited to initial evaluations of community interventions and the refinement of those interventions. This paper describes the main features of multiple baseline designs and related repeated-measures time-series experiments, discusses the threats to internal validity in multiple baseline designs, and outlines techniques for statistical analyses of time-series data. Examples are given of the use of multiple baseline designs in evaluating community interventions and policy changes.

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  1. Allison, D. B., & Gorman, B. S. (1993). Calculating effect sizes for meta-analysis: The case of the single case. Behavioral Research Therapy, 31, 621–631. · DOI
  2. Barlow, D. H., Hayes, S. C., & Nelson, R. O. (1984). The essentials of time-series methodology: Case studies&single-case experimentation; Within-series elements; Between-series elements; Combined-series elements. In A. P. Goldstein & L. Krasner (Eds.), The scientist practitioner–Research and accountability in clinical and educational settings. Pergamon general psychology series (pp. 157–272). New York: Pergamon Press.
  3. Barlow, D. H., & Hersen, M. (1984). Single case experimental designs. Strategies for studying behavior change (2). NewYork: Pergamon Press.
  4. Biglan, A. (1995a). Changing cultural practices: A contextualist framework for intervention research. Reno, NV: Context Press.
  5. Biglan, A. (1995b). Choosing a paradigm to guide prevention research and practice. Drugs and Society, 8, 149–160. · DOI
  6. Biglan, A., Ary, D., Koehn, V., & Levings, D. (1996d). Mobilizing positive reinforcement in communities to reduce youth access to tobacco. American Journal of Community Psychology, 24, 625–638. · DOI
  7. Biglan, A., Henderson, J., Humphreys, D., Yasui, M., Whisman, R., Black, C., & James, L. (1995). Mobilising positive reinforcement to reduce youth access to tobacco. Tobacco Control, 4, 42–48. · DOI
  8. Biglan, A., Mrazek, P. J., Carnine, D., & Flay, B. R. (in press). The integration of research and practice in the prevention of youth problem behaviors. American Psychologist.
  9. Box, G. E. P., & Jenkins, G. M. (1976). Times series analysis: Forecasting and control (Revised). Oakland, CA: Holden-Day, Inc.
  10. Busk, P. L., & Serlin, R. C. (1992). Meta-analysis for single-case research. In T. R. Kratochwill & J. R. Levin (Eds.), Singlecase research designs and analysis (pp. 187–212). Hillsdale, NJ: Lawrence Erlbaum.
  11. Campbell, D. T. (1969). Reforms as experiments. American Psychologist, 24, 409–429. · DOI
  12. Chaloupka, F. J., & Grossman, M. (1996). Price, tobacco control policies and youth smoking. (NBER Working Paper 5740). Chicago: National Bureau of Economic Research and University of Illinois at Chicago Department of Economics. · DOI
  13. COMMIT Research Group. (1995a). Community intervention trial for smoking cessation (COMMIT): I. Cohort results from a four-year community intervention. American Journal of Public Health, 85, 183–192. · DOI
  14. COMMIT Research Group. (1995b). Community intervention trial for smoking cessation (COMMIT): II. Changes in adult cigarette smoking prevalence. American Journal of Public Health, 85, 193–200. · DOI
  15. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and Analysis Issues for Field Settings. Chicago: Rand McNally.
  16. Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Schweder (Eds.), Metatheory in Social Science: Pluralism and Subjectivities (pp. 83–107). Chicago: University of Chicago Press.
  17. Crosbie, J. (1993). Interrupted time-series analysis with brief single-subject data. Journal of Consulting and Clinical Psychology, 61, 966–974. · DOI
  18. Crosbie, J. (1995). Interrupted time-series analysis with short series: Why it is problematic; how it can be improved. In J. M. Gottman (Ed.), The Analysis of Change (pp. 361–395). Mahwah, NJ: Lawrence Erlbaum.
  19. Enders, W. (1995). Applied econometric time series. New York: John Wiley & Sons.
  20. Fawcett, S. B., Paine, A. L., Francisco, V. T., & Vliet, M. (1995). Promoting health through community development. In D. Glenwick & L. A. Jason (Eds.), Promoting health and mental health: Behavioral approaches to prevention. New York, NY: Haworth Press.
  21. Forster, J. L., Murray, D. M., Wolfson, M., Blaine, T. M., Wagenaar, A. C., & Hennrikus, D. J. (1998). The effects of community policies to reduce youth access to tobacco. American Journal of Public Health, 88, 1193–1198. · DOI · Paper page
  22. Glass, G. V., Willson, V. L., & Gottman, J. M. (1975). Design and analysis of time-series experiments. Boulder: University of Colorado Press.
  23. Gottman, J. M. (1981). Time-series analysis.Acomprehensive introduction for social scientists. New York: Cambridge University Press.
  24. Harrop, J. W., & Velicer, W. F. (1985). A comparison of alternative approaches to the analysis of interrupted time-series. Multivariate Behavioral Research, 20, 27–44. · DOI
  25. Hayes, S. C. (1993). Goals and the varieties of scientific contextualism. In S. C. Hayes, L. J. Hayes, T. R. Sarbin, & H. W. Reese (Eds.), The varieties of scientific contextualism (pp. 11–27). Reno, NV: Context Press.
  26. Hingson, R., Heeren, T., Kovenock, D., Mangione, T., Meyers, A., Morelock, S., Lederman, R., & Scotch, N. A. (1987). Effects of Maine's 1981 and Massachusetts' 1982 drivingunder-the-influence legislation. American Journal of Public Health, 77, 593–597. · DOI
  27. Hollis, J. F., Lichtenstein, E., Vogt, T. M., Stevens, V. J., & Biglan, A. (1993). Nurse-assisted counseling for smokers in primary care. Annals of Internal Medicine, 118, 521–525. · DOI
  28. Kellam, S.G. (1999). Integrating prevention science strategies. Presidential address at the 7th Annual Society for Prevention Research Conference, June 24–26, 1999, New Orleans.
  29. Kratochwill, T. R. (1978). Single subject research. Strategies for evaluation change. New York: Academic Press.
  30. Liu, L. M., & Hudak, G. B. (1994). Forecasting and time series analysis using the SCA Statistical System. Oak Brook, IL: Scientific Computing Associates Corp.
  31. Matyas, T. A., & Greenwood, K. M. (1990). Visual analysis of single-case time series: Effects of variability, serial dependence, and magnitude of intervention effects. Journal of Applied Behavior Analysis, 23, 341–351. · DOI · Paper page
  32. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of mulivariate experimental psychology (2nd ed.) (pp. 561–613). New York: Plenum Press. · DOI
  33. McCleary, R., & Hay Jr., R. A. (1980). Applied time series analysis for the social sciences. Newbury Park, CA: Sage Publications, Inc.
  34. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122. · DOI
  35. Metzler, C. W., Biglan, A., Ary, D. V., & Li, F. (1998). The stability and validity of early adolescents' reports of parenting practices constructs. Journal of Family Psychology, 12, 600–619. · DOI
  36. O'Malley, P. M., & Wagenaar, A. C. (1991). Effects of minimum drinking age laws on alcohol use, related behaviors and traffic crash involvement among American youth: 1976–1987. Journal of Studies on Alcohol, 52, 478–491. · DOI
  37. Ockene, J. K. (1987). Physician-delivered interventions for smoking cessation: Strategies for increasing effectiveness. Preventive Medicine, 16, 723–737. · DOI
  38. Perry, C. L., Finnegan, J. R., Forster, J. L., Wagenaar, A. C., & Wolfson, M. (1996). Project Northland: Outcomes of a communitywide alcohol use prevention program during early adolescence. American Journal of Public Health, 86, 956–965. · DOI
  39. Ross, H. L. (1973). Law, science, and accidents: The British Road Safety Act of 1967. The Journal of Legal Studies, 11, 1–78. · DOI
  40. Sarbin, T. R. (1977). Contextualism: A world view for modern psychology. Nebraska symposium on motivation, Vol. 24 (pp. 3–40). Lincoln: University of Nebraska Press.
  41. Sidman, M. (1960). Tactics of scientific research. New York: Basic Books.
  42. Simonton, D. K. (1977a). Cross-sectional time-series experiments: Some suggested statistical analyses. Psychological Bulletin, 84, 489–502. · DOI
  43. Simonton, D. K. (1977b). Erratum to Simonton. Psychological Bulletin, 84, 1097. · DOI
  44. Trickett, E. J. (1991). Living an idea. Empowerment and the evolution of an alternative high school. Cambridge, MA: Brookline Books.
  45. Tryon, W. W. (1982). Reinforcement history as possible basis for the relationship between self-percepts of efficacy and responses to treatment. Journal of Behavioral and Experimental Psychiatry, 13, 201–202. · DOI
  46. US Department of Health and Human Services. (1994). Preventing tobacco use among young people: A report of the Surgeon General. Atlanta, Georgia: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health.
  47. Velicer, W. F., & Harrop, J. (1983). The reliability and accuracy of time series model identification. Evaluation Review, 7, 551–560. · DOI
  48. Velicer, W. F., & McDonald, R. P. (1984). Time series analysis without model identification. Multivariate Behavioral Research, 19, 33–47. · DOI
  49. Velicer, W. F., & McDonald, R. P. (1991). Cross-sectional time series designs: A general transformation approach. Multivariate Behavioral Research, 26, 247–254. · DOI
  50. Wagenaar, A. C. (1983). Alcohol, young drivers, and traffic accidents: Effects of minimum age laws. Lexington, MA: Lexington Books.
  51. Wagenaar, A. C. (1986). Preventing highway crashes by raising the legal minimum age for drinking: TheMichigan experience six years later. Journal of Safety Research, 17, 101–109. · DOI
  52. Wagenaar, A. C. (1993). Minimum drinking age and alcohol availability to youth: Issues and research needs. Alcohol and health monograph: Economics and the prevention of alcohol-related problems (pp. 175–200). Rockville, MD: National Institute on Alcohol Abuse and Alcoholism.
  53. Wagenaar, A. C., Gehan, J. P., Jones-Webb, R., Wolfson, M., Toomey, T. L., Forster, J. L., & Murray, D. M. (1999). Communities mobilizing for change on alcohol: Lessons and results from a 15-community randomized trial. Journal of Community Psychology, 27, 315–326. · DOI
  54. Wagenaar, A. C., & Maybee, R. G. (1986). The legal minimum drinking age in Texas: Effects of an increase from 18 to 19. Journal of Safety Research, 17, 165–178. · DOI
  55. Wagenaar, A. C., Murray, D. M., Gehan, J. P., Wolfson, M., Forster, J. L., Toomey, T. L., Perry, C. L., & Jones-Webb, R. Interrupted Time-Series Experiments 49 (in press). Communities mobilizing for change on alcohol: Outcomes from a randomized community trial. Journal of Studies on Alcohol.
  56. Wagenaar, A. C., Murray, D. M., Wolfson, M., & Forster, J. L. (1994). Communities mobilizing for change on alcohol: Design of a randomized community trial. Journal of Community Psychology, 1994–101. · DOI
  57. Wagenaar, A. C., & Webster, D. W. (1986). Preventing injuries to children through compulsory automobile safety seat use. Pediatrics, 78, 662–672.
  58. Warner, K. E. (1977). The effects of the anti-smoking campaign on cigarette consumption. American Journal of Public Health, 67, 645–650. · DOI
  59. Windsor, R. A. (1986). The utility of time series designs and analysis in evaluating health promotion and education programs. Advances in Health Education and Promotion, 1, 435–465.

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Metadata

Authors
Biglan, A., Ary, D., & Wagenaar, A. C.
Affiliations
  • Oregon Research Institute (US)
  • University of Minnesota (US)
ORCID
Venue
Prevention Science
Publisher
Springer Science and Business Media LLC
Published
2000-03
Volume
1
Issue
1
Pages
31-49
ISSN
  • 1389-4986 (ISSN-L)
  • 1573-6695
DOI
10.1023/a:1010024016308
PMC
PMC4553062
OpenAlex
W2138039555
Cited by
444
References
2
Citations by year
2026: 10 · 2025: 16 · 2024: 19 · 2023: 20 · 2022: 26 · 2021: 39 · 2020: 34 · 2019: 29 · 2018: 32 · 2017: 28 · 2016: 25 · 2015: 31 · 2014: 21 · 2013: 21 · 2012: 21
FWCI
0.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Prevention Science (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute on Drug Abuse (R01DA009306, DA 09306, DA 09678, R01DA009678)
  • National Cancer Institute (R01CA038273, CA38273)
Topics
  • Social Sciences › Public Administration › Evaluation and Performance Assessment
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
Keywords
interrupted time series design, multiple baseline design, policy evaluation
MeSH terms
Community Health Services, Humans, Research Design, Time Factors, Reproducibility of Results, Randomized Controlled Trials as Topic

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