Benefits and Challenges of Using Life History Calendars to Research Intimate Partner Violence
Research on intimate partner violence (IPV) suffers from high rates of attrition, which can be a problem for collecting longitudinal data. As one strategy to help address this problem, this paper will highlight the application of life history calendars (LHCs) to research on IPV. LHCs are cost-effective retrospective alternatives to longitudinal data collection strategies and have the potential to improve recall of events. Additionally, LHC data can establish temporal order and capture instances of repeat victimization. Quantitatively representing repeat victimization is especially important for IPV research because many IPV victims report experiencing more than one incident of abuse. First, a brief overview of LHCs is presented and the Chicago Women Health Risk Study is presented as an example to illustrate the utility of the LHC. Statistical methods to analyze this type of data are discussed as they relate to the data from the Chicago Women Health Risk Study. More specifically, the application of multi-level modeling with repeated measures and survival analyses (also referred to as event history analysis) to calendar data are reviewed. Particular attention is given to using survival analyses to analyze LHC data, especially in instances when the respondent reports more than one incident on the LHC. Benefits of calendar data, including improved recall of events and the ability to address questions related to repeat victimization, are highlighted. Challenges of working with LHC data, such as missing data and the use of open versus closed coding schemes, are reviewed.
KeywordsIntimate partner violence Life history calendar Multi-level modeling Survival analysis Event history analysis
- Block, C. R. (2000). The Chicago women’s health risk study: Risk of serious injury or death in intimate violence: A collaborative research project. Illinois Criminal Justice Information Authority. Retrieved March 4, 2014, from http://www.icjia.state.il.us/public/pdf/cwhrs/cwhrs.pdf.
- Bowman, R. (2006). Partial dates: Decisions and implications of handling partially missing dates. Retrieved Janaury 28, 2018 from https://www.lexjansen.com/phuse/2006/po/PO11.pdf.
- Campbell, J. C. (1993). The Danger Assessment instrument: Risk factors of homicide of and by battered women. In C. R. Block & R. Block (Eds.), Questions and answers in lethal and non-lethal violence (pp. 27–28). Vol. 1. NCJ 142058. Washington, D. C.: National Institute of Justice.Google Scholar
- Campbell, J. C., Webster, D., Koziol-McLain, J., Block, C., Campbell, D., Curry, M. A., … Laughon, K. (2003). Risk factors for femicide in abusive relationships: Results from a multisite case control study. American Journal of Public Health, 93(7), 1089–1097.CrossRefPubMedPubMedCentralGoogle Scholar
- Cleves, M. (2009). Analysis of multiple failure-time survival data. Retrieved January 28, 2018 from http://www.stata.com/support/faqs/statistics/multiple-failure-time-data/.
- Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 34(2), 187–220.Google Scholar
- Eisner, M., Murrary, J., Ribeaud, D., Topcuoglu, T., Kazemian, L., & Besmer, S. (2009). The event history calendar as an instrument for longitudinal criminological research. Monatsschrift für Kriminologie und Strafrechtsreform, 92(2/3), 137–159.Google Scholar
- Farrell, G. (1995). Preventing repeat victimization. In M. Tonry & D. Farrington (Eds.), Building a safer society: Strategic Approaches to crime prevention (Vol. 19, pp. 489–534). Chicago: University of Chicago Press.Google Scholar
- Genn, H. (1988). Multiple Victimisation. In M. Maguire & J. Pointing (Eds.), Victims of crime: A new deal? (pp. 90–100). England: Open University Press.Google Scholar
- Greenberg, D. F., & Phillips, J. A. (2013). Hierarchical linear modeling of growth curve trajectories using HLM. In G. D. Garson (Ed.), Hierarchical linear modeling: Guide and applications (pp. 219–247). Thousand Oaks: SAGE.Google Scholar
- Leech, B. L. (2002). Asking questions: techniques for semistructured interviews. PS: Political Science & Politics, 35(4), 665–668.Google Scholar
- Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and longitudinal modeling using Stata: Volume II: Categorical responses, counts, and survival. College Station: Stata Press.Google Scholar
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: Sage Publications.Google Scholar
- Roberts, J., & Horney, J. (2010). The life event calendar method in criminological research. In A. R. Piquero & D. Weisburd (Eds.), Handbook of Quantitative Criminology (pp. 289–312). Springer New York.Google Scholar
- Stark, E. (2007). Coercive control: The entrapment of women in personal life. New York, NY: Oxford University Press.Google Scholar
- Suchman, L., & Jordan, B. (1990). Interactional troubles in face-to-face survey interviews. Journal of the American Statistical Association, 85(409), 232 – 41.Google Scholar
- Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th edn.). Boston, MA: Pearson Education, Inc.Google Scholar
- Walby, S., & Allen, J. (2004). Domestic violence, sexual assault and stalking: Findings from the British Crime Survey (276 edn.). London: Home Office Research, Development and Statistics Directorate: Home Office Research Study.Google Scholar
- Wang, D., & Lin, C. (2014). Tips to manipulate the partial dates. Retrieved January 28, 2018 from https://www.pharmasug.org/proceedings/2014/CC/PharmaSUG-2014-CC06.pdf.
- Yoshihama, M. (2009). Application of the life history calendar approach to understand women’s experiences of intimate partner violence over the lifecourse. In R. Belli, F. Stafford & D. Alwin (Eds.), Measuring well-being: Using calendar and time diary methods in life course research (pp. 135–155). Thousand Oaks: SAGE.Google Scholar