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VAW17310.1177/1077801211398229Yoshihama and BybeeViolence Against Women
Article
The Life History Calendar Method and Multilevel Modeling: Application to Research on Intimate Partner Violence
Violence Against Women 17(3) 295–308 © The Author(s) 2011 Reprints and permission: http://www. sagepub.com/journalsPermissions.nav DOI: 10.1177/1077801211398229 http://vaw.sagepub.com
Mieko Yoshihama1 and Deborah Bybee2
Abstract Intimate partner violence (IPV) is prevalent and often recurrent in women’s lives. To better understand the changing risk of IPV over the life course, which could guide more effective policies and program responses, methodological innovations are needed. Life History Calendar methods enhance respondents’ recall of the timing of specific types of IPV experienced over the life course. Multilevel modeling provides a way to analyze individual and collective trajectories and examine covariates of IPV risk. We apply these complementary methods to examine IPV trajectories for a sample of women of Filipina descent living in the United States, examining life course timing and cohort effects. Keywords Asian women, domestic violence, intimate partner violence, life history calendar, multilevel modeling, self-report The high prevalence and long-lasting, wide-ranging negative effects of intimate partner violence (IPV) on women’s lives call for enhanced prevention and intervention efforts grounded in women’s experiences. Such efforts demand methodological innovations to enhance our understanding of women’s experiences of IPV over the life course. Obtaining information about women’s experience of IPV is complicated for a number of reasons. First, IPV is often recurrent in women’s lives. A woman may experience abuse by a single partner intermittently with or without clear cessations and resumptions. A woman 1
University of Michigan, Ann Arbor Michigan State University, East Lansing
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Corresponding Author: Mieko Yoshihama, School of Social Work, 3840 Social Work, University of Michigan, Ann Arbor, MI 48109 Email: [email protected]
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may experience abuse by multiple partners simultaneously or sequentially (by a single partner at a time). This recurrent nature of IPV and its complex sequences pose numerous methodological challenges in obtaining information about women’s experiences of IPV over the life course. Second, collateral sources of data are generally unavailable, unreliable, or invalid for documenting women’s life course experience of IPV. Most IPV is committed in private, without witnesses other than the victim and the perpetrator, who is unlikely to be a credible source of information, due to self-protective motivation and social desirability bias (Arias & Beach, 1987; Dutton & Hemphill, 1992). It is extremely unlikely that a researcher would be able to identify, contact, and gather data from multiple intimate partners across a woman’s life course. Attempts to collect data from perpetrators can also compromise women’s safety. Institutional data sources, such as police and hospital records, are insufficient for life course data collection, due to geographic dispersion and inadequacies in historical record retention and access. Most important, the majority of incidents of IPV go unreported and undocumented, rendering institutional data sources an invalid source of information about women’s IPV experiences. Thus the use of women’s self-reports is typically the best viable option for obtaining information on women’s IPV experiences over their life course. An encouraging empirical finding is that social desirability biases are not associated with women’s self-reports of experiencing partners’ abusive acts (Arias & Beach, 1987; Dutton & Hemphill, 1992; Saunders, 1986). However, numerous methodological challenges remain. Research on autobiographic memory and survey methodologies indicate that the longer the reporting period, the more likely underreporting and inaccuracy occur (Rubin & Wenzel, 1996; Thompson, Skowronski, Steen, Larsen, & Betz, 1996; Wagenaar, 1986). Not surprisingly, when using a conventional survey interview method without mechanisms to prime respondents’ memory retrieval, a substantially lower proportion of middle-aged women reported IPV that occurred during their younger years compared to younger women (Yoshihama & Gillespie, 2002). Given that women’s self-report is often the sole source of information about their experiences of IPV, optimizing the accuracy of their memory retrieval is critical. Of course, prospective data collection could reduce the problem of long-term recall; however, longitudinal designs necessary to collect information about IPV over women’s life course are costly, require a long study period, and are likely to suffer from sample attrition. More important, such designs would pose major ethical problems; following participants who experience IPV without intervening compromises their safety and well-being. Yet intervening would jeopardize the study design needed to examine the occurrence of IPV. Thus enhancing respondents’ recall in retrospective studies remains the most practical alternative. The most promising method for optimizing retrospective recall is the Life History Calendar (Freedman, Thornton, Camburn, Alwin, & Young-DeMarco 1988). This flexible method has been applied to studies of IPV (Yoshihama, Clum, Crampton, & Gillespie, 2002; Yoshihama, Gillespie, Hammock, Belli, & Tolman, 2005; Yoshihama, Hammock, & Horrocks, 2006) and shows promise for use in cross-sectional, large-scale surveys, serving as a more cost-effective alternative to prospective longitudinal studies.
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Life History Calendar (LHC) Method Although the LHC method can and has been used to assist in collecting qualitative data (Martyn & Belli, 2002), in this article we discuss the application of the LHC method to quantitative studies using face-to-face interviews. The LHC method uses a calendar format usually within a semistructured interview schedule to help the respondents remember the timing and other aspects of events of interest. In general, a respondent in an LHC interview is asked to first report on events that are memorable and/or relatively easily recalled, which then can serve as memory aids in remembering less easily recalled information (Freedman et al., 1988; McPherson, Popielarz, & Drobnic, 1992). The interviewer records the respondents’ experiences in a preprinted calendar-like form in plain view of the respondent. This format, unlike a conventional interview format where the interviewer records the respondent’s answers on a form available only to the interviewer, allows the respondent to use the information recorded on the calendar as a memory cue in recalling the occurrence and timing of events subsequently asked. The LHC method has been applied to studies of various topics, such as youth delinquency and violence, stressful life events, and fertility history (Axinn, Pearce, & Ghimire, 1997; Belli, Stafford, & Alwin, 2009; Caspi et al., 1996; Ensel, Peek, Lin, & Lai, 1996). Studies that compared the retrospective reports collected by the LHC method and reports collected prospectively have found high degrees of agreement between the two (Caspi et al., 1996; Ensel et al., 1996). Interestingly, reports of more subjective, ambiguous events, such as an increase in arguments with spouse and sexual difficulties, had higher agreement than employment events (Ensel et al., 1996). Besides Caspi and colleagues’ (1996) study of youth in New Zealand, which included assessment of IPV, LHC was first applied to IPV in a study of low-income women in Detroit, MI (Yoshihama et al., 2002). Psychometric data indicate good reliability and validity: The test-retest correlation was r = .80 for the number of lifetime abusive partners, and high test-retest agreement was found on whether the respondent reported experiencing physical violence (95%), sexual violence (80%), and threats/harassment (90%; kappa range .53-.71). The respondents’ perceived effectiveness of the LHC interview (M = 3.9 on a 5-point scale) was not associated with either their ages or the lengths of the interviews. Furthermore, when compared to a conventional behavior-specific interview method in a different sample drawn from the same sampling frame, LHC interviews elicited more reports of IPV incidents, especially those occurring in the distant past (Yoshihama, 2009; Yoshihama et al., 2005). To illustrate what a LHC instrument looks like, we will describe the paper instrument (Calendar) we developed in a recent study of Asian battered women. We placed various life events and situations on the calendar’s vertical axis, and time units (the respondent’s age and calendar year) on the horizontal axis (Figure 1) . Every other column is color-coded to help distinguish them and to minimize the risk of recording events in the wrong column.
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ID: ___________ YEAR AGE Residential move Schooling Work Children
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Relationship - Partner Initials [C,M,S,D,AN,W,AB,AW] 1 Pushed, grabbed, or shoved you? 2 Hit, slap, punch you? (w/o object) 3 Kicked you? 4 Strangled or choked you? 5 Use knife,gun,other objects?[K,G,O] 6 Forced you to have sex? 7 Attempted to force you to have sex? 8 Forced you to have sex with others? 9 Other physical or sex violence/abuse? 10 11 1 Help-Seeking from Health Care System Response 2 Help-Seeking from Police Response 3 Response AGE
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Figure 1. An example of life history calendar instrument
Note: [Letters in brackets] denote coding used: C = cohabitation; M = marriage; S = separation; D = divorce; AN = annulment; W = widowhood; AB = abandonment; Aw = away; K = knife; G = gun; O = other.
Vertical Axis As seen in Figure 1, prior to asking about the study’s main focus—the respondent’s experiences with IPV—we asked about a number of life events and situations designed to serve as memory cues. This organization reflects the thrust of the LHC method: first obtaining information about memorable and/or easily recalled experiences and using such information
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to assist the respondent’s recall of events that are more difficult to remember. These types of events and life situations were selected based on previous studies (Belli, 2000; Yoshihama et al., 2002). Personal events, such as births of children, can serve as effective memory cues to recall sensitive and difficult aspects of life such as IPV. Although incorporating a larger number of memory cues can assist in the respondent’s recall, collecting data for the sole purpose of using them as memory cues extends interview length. Thus it is critical to select events and situations that are conceptually and/or empirically linked to the study’s main focus so that they “double-up” as both memory cues and covariates in analyses. In general, we placed less sensitive questions first, a strategy designed to facilitate the development of rapport between the interviewer and respondent before asking more sensitive questions.
Horizontal Axis The calendar’s time units can be any increment of time (e.g., one year, month, week, or day) appropriate to the study; we used the respondent’s age. This decision reflects the relatively long recall period to be covered (up to 45 years) in this study, which made it difficult for the respondent to accurately recall a smaller time unit in which a particular event occurred. Smaller time units can be used in studies that cover a shorter period of time (e.g., a study of adolescent dating violence or repeated data collection in a panel study of short intervals). For example, a study that assessed stressful life events in the previous 12 months used three parts of a month (beginning, middle, and end) as the time unit (Kessler & Wethington, 1991). Although we chose to place the time unit on the calendar’s horizontal axis, it can be placed on the vertical axis depending on the specific purpose and scope of the study. Depending on the number of time units covered in the recall period and the number of life events/situations to assess, the calendar instrument can be quite large. For respondents aged 25 or younger, the calendar size was 22 × 8 inches (2 letter-sized papers); for older respondents, the size extended up to 22 × 40 inches. This sizing allowed most cells to be less than 1/4 inch × less than 1/2 inch. Although having larger cell sizes to record respondents’ answers was preferable, a larger sheet of paper would have been difficult to use in this and other communitybased survey settings where large desktop space may not be available.
Interview Administration We developed an interview schedule to guide data collection in a semistructured format. To aid the interview, we used a respondent booklet that listed a range of response options, such as a list of countries for assessment of the respondent’s and her partner’s country of birth and a behavior-specific list of types of IPV. As indicated previously, prior to answering questions about IPV, respondents were asked about their experiences in various life domains. These questions were designed to build an overview picture of a respondent’s life to later aid her recollection of other, more sensitive life events. Subsequently, respondents were asked about relationship history. Respondents in Yoshihama’s previous study
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of IPV using a LHC instrument in Detroit identified relationship history as an important memory cue for remembering their experiences of IPV (Yoshihama et al., 2002). In general, we asked about the first intimate partner, and then about subsequent partners chronologically; however, some respondents chose to provide relationship history starting with the current partner and move their way backward. Some respondents blended forward and backward recall. The main aim of this section of the interview was to identify all intimate partners in the respondent’s life and obtain information about the timing of relationship formation, cohabitation, marriage, separation, and divorce. To distinguish specific partners, we obtained the initials or first name of each partner. “Intimate partner” was self-defined by respondents. Respondents were told that the study was about abuse in “relationships with boyfriends and husbands.” We also asked the respondents about any periods in which they were generally dating but involved with no one in particular. The interviewer then asked the respondent whether she had experienced IPV in each intimate relationship mentioned in the previous section using behavior-specific items: physical IPV (5 items), as well as other forms of IPV (sexual violence, stalking, threats, and emotional and financial abuse). These various behavior-specific IPV acts were written on a respondent booklet. The interviewer probed about the age at which the respondent experienced each type of IPV for the first time and whether it happened in subsequent years, and if so, at what age.
Analytic Approaches Using LHC Data LHC data lend themselves to a variety of analytic strategies. For example, in a study of low-income women in Detroit, Yoshihama and colleagues (2005) used survival analysis to investigate whether the probability of experiencing IPV varies between groups. Using the same data, Yoshihama and colleagues (2006) examined whether previous receipt of welfare was associated with a subsequent risk of experience of IPV, using a type of longitudinal data analysis called Generalized Estimating Equations (GEE). By controlling for length of exposure to risk, these methods represent advances over simpler analysis methods that examine unadjusted proportions of respondents reporting IPV. Another analytic approach that can be used to analyze the life course data collected through LHC is multilevel modeling (MLM; Hedeker & Gibbons, 2006; Raudenbush & Bryk, 2002). This approach models each individual’s trajectory of change on a dependent variable (e.g., year-to-year experiences of IPV) across the life course and estimates an average model that describes the general trajectory of the sample or identified subgroups. MLM can incorporate covariates that reflect differences between individuals (e.g., country of origin, age at interview) as well as time-varying covariates that reflect changing characteristics or circumstances for a given individual (e.g., age, relationship status). Analyses can also examine interactions between individual-level and time-varying variables (e.g., to test whether life course trajectories of IPV vary among women who were interviewed at different ages). MLM is especially useful for LHC data because it can appropriately incorporate life course data that vary in length, as is common when individuals vary in age at the
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time they are interviewed. It can also accommodate periods of data that cannot be recalled or are missing for some other reason. Methods are available for analyzing all forms of life course data, including variables that are continuous, count, ordinal, and categorical. In addition to statistical procedures that allow sophisticated assessment of model fit and extensive testing of specific effects, results can be presented in graphical form, thereby facilitating interpretation and communication of findings.
Example of MLM Analysis of Data Gathered Through LHC To illustrate the use of MLM, we present an analytic example using data gathered through the LHC presented earlier (illustrated in Figure 1), modeling the trajectory of physical IPV over the life course. The respondents were 87 women of Filipina descent aged 18 to 60 in the San Francisco Bay Area, who were interviewed as part of a larger study of IPV and help-seeking among Asian women. Approval was obtained from an institutional review board. Specific selection criteria included having experienced physical violence, sexual violence, and/or stalking at the hands of an intimate partner while residing in the San Francisco Bay Area (comprised of nine counties). Although Filipinos are the second largest in population size and third fastest-growing Asian population in the United States (Barnes & Bennett, 2002), they are severely understudied in IPV research. Yet Filipina women are overrepresented in IPV-related homicide statistics. For example, in San Francisco, 3 out of 5 IPV-related homicide victims in 1999 were Filipina, and in Hawaii, 5 out of 7 IPV-related homicides in 2000 were Filipinas (Yoshihama & Dabby, 2009). Respondents were recruited through various community outreach methods, including distribution of flyers and other recruitment materials via email and listserves and at community events and centers, places of worship, culturally specific vendors and other gathering places; placement of advertisements in mainstream and ethnic media outlets; and outreach to staff at community-based organizations, informants, and stakeholders. The interview lasted on average 57.81 min (SD = 22.63). Each respondent received US$45 (later increased to US$75) for participation in the study. Women’s mean age at the time they were interviewed was 40.70 (SD = 11.13). The 87 women reported their experiences of abuse starting at age 16, yielding data of about 2,233 person-years. More than half (58.6%) of the women were born in the Philippines; 36.7% were born in the United States, and 4.5% were born in other countries. Of those born outside the United States, 23.6% immigrated before age 13, and 29.1% between 13 and 24. At the time of the interview, 37.9% had a high school diploma or equivalent, 18.3% had additional vocational training or an associate’s degree, and 41.4% had attained a bachelor’s or master’s degree. Most (85.1%) women had at least one child. All but four of the women reported having experienced physical IPV in at least 1 and up to 23 years (M = 6.03, SD = 5.11). The age at which women reported first experiencing physical IPV ranged from 16 (the initial year of the calendar) to 55 (M = 26.17, SD = 9.47).
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Analysis To obtain a clearer picture of the timing of women’s experiences of physical IPV across the life course, we used MLM to model women’s individual and collective trajectories. Because we were interested in whether women experienced any physical IPV in a given year, we specifically used multilevel logistic regression, as implemented in HLM software (Raudenbush, Cheong, Bryk, & Congdon, 2004). This approach models changes in the probability of experiencing abuse and provides ways to test whether changing probabilities are associated with covariates that change over time (known in MLM as Level 1 covariates, e.g., age) and those that are constant over time but vary across women (Level 2 variables, e.g., a woman’s age at the time of the interview). The effects of Level 1 covariates can be estimated as random (i.e., estimated for each individual and then averaged) or fixed (estimated as the same for all women). Because we expected considerable variability in individual women’s trajectories, we estimated all Level 1 effects as random. We expected that women’s trajectories of abuse would not be strictly linear but were likely to accelerate and decelerate at various points; therefore, we included polynomial terms (age-squared and age-cubed) to capture trajectory curvilinearity. Because the LHC started at age 16 rather than 0, we centered age at 20, which was the age of the youngest woman’s interview. In other words, we subtracted 20 from each age across the life course, effectively setting age 20 as the model intercept or zero point. This is commonly done in longitudinal analysis and has the advantage of both making the model intercept interpretable and reducing collinearity among polynomial and interaction terms (Singer & Willett, 2003). For similar reasons, we centered age at the time of the interview to 40, which was near the mean age at which women were interviewed.
Results Results of the MLM analysis are presented in Table 1. In the left panel, the fixed (average) effects indicate the contribution of each covariate to the average estimated trajectory of physical IPV. The odds ratio (OR) for the intercept indicates that for women interviewed at 40 (the centered intercept for age at interview), the probability of having experienced physical IPV at age 20 (the centered intercept of age) was .09. Across the life course, there was a significant positive linear effect of age (OR = 1.38) as well as a significant negative effect of age-squared (OR = 0.98), indicating that the probability of physical IPV increased over the early portion of the life course and then declined. The pattern of this complex effect can clearly be seen in the graph of modeled effects in Figure 2. In general, the probability of physical IPV increased from age 16 (labeled as –4, due to centering at age 20) to the early 30s, then declined. As can be seen in Figure 2, the effect of age on IPV across the life course varied, depending on the age of the woman when she was interviewed. Women interviewed at younger ages reported earlier physical IPV, while women interviewed at older ages reported later onset but a sharper rate of increase. These effects were significant, as can be seen in the bottom section of Table 1. The probability of experiencing IPV at age 20 (the intercept)
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Table 1. Multilevel Logistic Regression Model of Physical IPV Across the Life Course, by Age at Interview Fixed (average) effects
Coefficient
Intercept -2.46 Time-varying covariates (Level 1) Age (linear effect) 0.32 -0.02 Age-squared (curvilinear effect) Time invariant covariates (Level 2) -0.10 Age at interview Age at interview × Age 0.01 (linear)
Random effects
SE
T ratio (df = 85)
Odds ratio
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Chi-square (df = 63)
0.32
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5.22*** -5.50***
1.38 0.98
0.45 0.02
178.51*** 152.93***
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-3.21** 3.12***
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Note: Level 2 N = 87 women; Level 1 N = 2,233 person-years. **p < .01. ***p < .001.
Est. Probability of Physical Violence
0.350 Age 30 at interview Age 40 at interview Age 50 at interview 0.262
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Figure 2. Estimated probability of physical IPV across the life course, by age at interview
Note: Plotted values are estimated from the multilevel model. Age at interview was analyzed as a continuous variable; the lines illustrate the effects of this variable at three selected points—M (age 40), –1 SD (age 30), and +1 SD (age 50).
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was 10% less (OR = 0.90) for each additional year in age at the time of the interview. The increasing probability of physical IPV across the life course was slightly steeper for women who were older at the time of the interview: 0.01% greater (OR = 1.01) for each year older at the time of the interview. Age at the interview did not significantly affect deceleration (the declining, curvilinear effect of age-squared across the life course); physical IPV probability declined at a similar rate regardless of the age at which a woman was interviewed. This is partly because the later years of the life course trajectory relied more heavily on the experiences of women who were older when interviewed. The right panel of Table 1 shows the random effects of the Level 1 variables, or the extent to which trajectories of physical IPV across the life course varied among the 87 women. All three terms—intercept, linear, and curvilinear effects of age—had large standard deviations that were significantly different from zero, indicating that women’s individual trajectories varied substantially, despite the similarities in pattern that were reflected in the significant fixed or average effects. This suggests that a substantial amount of variance in women’s physical IPV trajectories remains to be explained by variables other than age and age at the interview.
Discussion The combination of LHC methods for data collection and MLM approaches to data analysis is a promising strategy for examining women’s experiences of IPV across the life course. LHC interview methods offer improvements in memory cuing and recall, and MLM analysis makes full use of time-variant and time-invariant data that are gathered. Combined, these two methods open a variety of research questions to examination. Both methods are flexible and can be used to examine life course trajectories of many types of experiences, including social support, help-seeking, and well-being. The combination of methods can be used to examine the influence of many types of covariates as well. Building on a basic model of age and age at interview, as in the example presented here, covariates can be added to explain additional variance in life course IPV. For example, trajectories of IPV across the life course can be examined for subgroup differences, addressing research questions about variability in the timing and level of risk for IPV by age at immigration to the United States, country of origin, or other variables distinguishing subgroups of women. Assessing variables that change across the life course, such as relationship status, pregnancy, living situation, employment, and social support, would allow examination of the effects of these time-varying covariates on changes in IPV risk across women’s life course. Adding time-varying covariates such as these would allow researchers to ascertain, for example, whether risk for IPV changes significantly during pregnancy, whether IPV changes in intensity during various relationship phases (e.g., initial phase, ongoing marriage, postseparation), and whether IPV risk is associated with women’s employment status. Examining the trajectories of multiple forms of IPV would allow examination of the relationships among occurrence of physical, sexual, and emotional IPV, stalking, and threats, as well as the effects of the co-occurrence of multiple types of IPV on changes in well-being
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and help-seeking. Combining both time-varying and between-person covariates would allow examination of cross-level interactions to examine questions such as whether the risk for physical IPV during pregnancy is similar or different for women in various cultures and whether there are cultural, regional, or cohort differences in the timing of stalking relative to relationship status and phase. LHC data collected in yearly or other time increments can be configured in numerous ways that make full use of the flexibility of MLM analytic strategies. Covariates in the model (e.g., residential moves or efforts to seek help from police or shelters) can be configured in multiple ways, so that MLM analyses can examine a variety of research questions related to timing and duration of effects. Three possible configurations are contemporaneous, lagged, and cumulative covariates. Contemporaneous covariates are hypothesized to exert immediate effects, such as the effect of pregnancy on IPV occurring while a woman is pregnant. Lagged covariates exert delayed effects that can be seen in a later time period, such as the possible effect of loss of social support on IPV in a subsequent time period. Cumulative covariates exert compounding effects that get stronger with repeated occurrence, such as the effect of repeated moves over time on life course IPV. The flexibility of MLM allows the researcher to examine these alternative configurations of LHC data and to make formal statistical comparisons of their ability to explain changes across the life course. The combination of LHC and MLM methods could also be productively applied to understanding the influences on life course help-seeking for IPV (e.g., calling the police, seeking protection orders, using domestic violence programs). MLM analyses could address research questions such as: What types of help are likely to be sought by women with different life course experiences of IPV? When in the life course are various forms of help-seeking most likely? Do the cumulative effects of continued or repeated IPV over time increase or decrease the likelihood that women will seek various forms of help? Do positive or negative help-seeking experiences over the life course have lagged or cumulative effects on subsequent help-seeking? Results of analyses such as these would be invaluable for policy and practice efforts to improve access and help-seeking responses. One of the strengths of the M LM approach is the ability to examine the effects of substantive covariates while adjusting for the effects of age and for cohort effects due to differences in respondents’ ages at the time of the interview. Although LHC methods have been observed to reduce recall bias relative to noncued methods, especially for events experienced in the distant past (Yoshihama et al., 2005), our analysis found significant cohort effects, with older women reporting later initial IPV and lower levels of IPV overall. It is unclear if these differences reflect recall biases, historical shifts in women’s perceptions of what behaviors “count” as IPV, age-related differences in willingness to disclose IPV, or actual cohort differences in the level and timing of IPV, which may be related to changing relationship type or timing. In this study of Filipina immigrants to the United States, cohort effects may be associated with age at immigration and may therefore reflect changes in sociocultural or political environment or stresses and challenges related to immigration itself. More research is needed to examine these possible influences. Regardless of the source
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of these effects, it is important that analyses of self-report life course data use methods such as MLM that can examine and control for cohort influences. Assessing women’s experiences over the life course using the LHC method, which involved obtaining the timing of each type of IPV for every intimate partner in a respondent’s life, might be thought to be extremely time consuming. However, the average survey administration for this study—57.81 min—is within a typical range for in-depth, face-to-face interviews. The average interview length for a similar study that used the LHC method in the Tokyo Metropolitan Area was 114 min (SD = 39.03). An LHC-based interview can be designed for any length, just as one would design a conventional interview; the researcher decides on what to ask with what degrees of specificity. As discussed previously, the key is to maximize precious survey administration time by assessing the areas of a respondent’s life that are salient to the study’s focus, which can serve as both memory cues and covariates in analytic models. LHC methods have the potential to increase the amount of information gathered from the expenditure of survey resources, while also improving the accuracy of self-report. Coupled with appropriate analytic approaches such as MLM, LHC offers important methodological advantages for the study of women’s experience of violence across the life course.
Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article.
Funding The data set used for this article was obtained in a study funded by the National Institute of Justice 2005-WG-BX-0009.
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for collecting retrospective event-history data. International Journal of Methods in Psychiatric Research, 6, 101-114. Dutton, D. G., & Hemphill, K. J. (1992). Patterns of socially desirable responding among perpetrators and victims of wife assault. Violence and Victims, 7, 29-39. Ensel, W. M., Peek, M. K., Lin, N., & Lai, G. (1996). Stress in the life course: A life history approach. Journal of Aging and Health, 8, 389-416. Freedman, D., Thornton, A., Camburn, D., Alwin, D., & Young-DeMarco, L. (1988). The Life History Calendar: A technique for collecting retrospective data. Sociological Methodology, 18, 37-68. Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. New York: Wiley. Kessler, R., & Wethington, E. (1991). The reliability of life event reports in community surveys. Psychological Medicine, 21, 723-738. Martyn, K. K., & Belli, R. F. (2002). Retrospective data collection using event history calendars. Nursing Research, 51, 270-274. McPherson, J. M., Popielarz, P. A., & Drobnic, S. (1992) . Social network and organizational dynamics. American Sociological Review, 57, 153-170. Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: SAGE. Raudenbush, S., Bryk, A., Cheong, Y. F., & Congdon, R. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Chicago, IL: Scientific Software International. Rubin, D. C., & Wenzel, A. E. (1996). One hundred years of forgetting: A quantitative description of retention. Psychological Review, 103, 734-760. Saunders, D. G. (1986). When battered women use violence: Husband abuse or self-defense? Violence and Victims, 1, 47-60 Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis. New York: Oxford University Press. Thompson, C. P., Skowronski, J. J., Steen, F., Larsen, S. F., & Betz, A. L. (1996). Autobiographical memory: Remembering what and remembering when. Mahwah, NJ: Lawrence Erlbaum. Wagenaar, W. A. (1986). My memory: A study of autobiographical memory over six years. Cognitive Psychology, 18, 225-252. 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, CA: SAGE. Yoshihama, M., Clum, K., Crampton, A., & Gillespie, B. (2002). Measuring the lifetime experience of domestic violence: Application of the Life History Calendar method. Violence and Victims, 17, 297-317. Yoshihama, M., & Dabby, C. (2009, September). The facts & stats: Domestic violence in Asian, Native Hawaiian and Pacific Islander homes. San Francisco, CA: Asian & Pacific Islander Institute on Domestic Violence. Yoshihama, M., & Gillespie, B. (2002). Age adjustment and recall bias in the analysis of domestic violence data: Methodological improvement through the application of survival analysis methods. Journal of Family Violence, 17, 199-221.
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Yoshihama, M., Gillespie, B., Hammock, A. C., Belli, R., & Tolman, R. (2005). Does the Life History Calendar method facilitate the recall of intimate partner violence? Comparison of two methods of data collection. Social Work Research, 29, 151-163. Yoshihama, M., Hammock, A. C., & Horrocks, J. (2006). Intimate partner violence, welfare receipt, and health status of low-income African American women: A lifecourse analysis. American Journal of Community Psychology, 37, 95-109
Bios Mieko Yoshihama, Ph.D, LMSW, ACSW is a professor at the University of Michigan School of Social Work. Her research and practice focus on violence against women, immigrants, and community organizing. She has conducted numerous survey research projects on domestic violence and community-based prevention programs in the U.S. and Japan. Her work at local, state, national, and international levels over the last 25 years combines research and action to promote the safety and well-being of marginalized communities. Dr. Yoshihama serves on the boards and committees of various national, state, and local organizations aimed at ending violence against women. Deborah Bybee is a professor in the Department of Psychology at Michigan State University. As a community psychologist, she focuses on methodological and statistical issues in real world research, especially longitudinal approaches. Her main substantive interests are violence against women and mental health.
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VAW17310.1177/1077801211398229Yoshihama and BybeeViolence Against Women
Article
The Life History Calendar Method and Multilevel Modeling: Application to Research on Intimate Partner Violence
Violence Against Women 17(3) 295–308 © The Author(s) 2011 Reprints and permission: http://www. sagepub.com/journalsPermissions.nav DOI: 10.1177/1077801211398229 http://vaw.sagepub.com
Mieko Yoshihama1 and Deborah Bybee2
Abstract Intimate partner violence (IPV) is prevalent and often recurrent in women’s lives. To better understand the changing risk of IPV over the life course, which could guide more effective policies and program responses, methodological innovations are needed. Life History Calendar methods enhance respondents’ recall of the timing of specific types of IPV experienced over the life course. Multilevel modeling provides a way to analyze individual and collective trajectories and examine covariates of IPV risk. We apply these complementary methods to examine IPV trajectories for a sample of women of Filipina descent living in the United States, examining life course timing and cohort effects. Keywords Asian women, domestic violence, intimate partner violence, life history calendar, multilevel modeling, self-report The high prevalence and long-lasting, wide-ranging negative effects of intimate partner violence (IPV) on women’s lives call for enhanced prevention and intervention efforts grounded in women’s experiences. Such efforts demand methodological innovations to enhance our understanding of women’s experiences of IPV over the life course. Obtaining information about women’s experience of IPV is complicated for a number of reasons. First, IPV is often recurrent in women’s lives. A woman may experience abuse by a single partner intermittently with or without clear cessations and resumptions. A woman 1
University of Michigan, Ann Arbor Michigan State University, East Lansing
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Corresponding Author: Mieko Yoshihama, School of Social Work, 3840 Social Work, University of Michigan, Ann Arbor, MI 48109 Email: [email protected]
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may experience abuse by multiple partners simultaneously or sequentially (by a single partner at a time). This recurrent nature of IPV and its complex sequences pose numerous methodological challenges in obtaining information about women’s experiences of IPV over the life course. Second, collateral sources of data are generally unavailable, unreliable, or invalid for documenting women’s life course experience of IPV. Most IPV is committed in private, without witnesses other than the victim and the perpetrator, who is unlikely to be a credible source of information, due to self-protective motivation and social desirability bias (Arias & Beach, 1987; Dutton & Hemphill, 1992). It is extremely unlikely that a researcher would be able to identify, contact, and gather data from multiple intimate partners across a woman’s life course. Attempts to collect data from perpetrators can also compromise women’s safety. Institutional data sources, such as police and hospital records, are insufficient for life course data collection, due to geographic dispersion and inadequacies in historical record retention and access. Most important, the majority of incidents of IPV go unreported and undocumented, rendering institutional data sources an invalid source of information about women’s IPV experiences. Thus the use of women’s self-reports is typically the best viable option for obtaining information on women’s IPV experiences over their life course. An encouraging empirical finding is that social desirability biases are not associated with women’s self-reports of experiencing partners’ abusive acts (Arias & Beach, 1987; Dutton & Hemphill, 1992; Saunders, 1986). However, numerous methodological challenges remain. Research on autobiographic memory and survey methodologies indicate that the longer the reporting period, the more likely underreporting and inaccuracy occur (Rubin & Wenzel, 1996; Thompson, Skowronski, Steen, Larsen, & Betz, 1996; Wagenaar, 1986). Not surprisingly, when using a conventional survey interview method without mechanisms to prime respondents’ memory retrieval, a substantially lower proportion of middle-aged women reported IPV that occurred during their younger years compared to younger women (Yoshihama & Gillespie, 2002). Given that women’s self-report is often the sole source of information about their experiences of IPV, optimizing the accuracy of their memory retrieval is critical. Of course, prospective data collection could reduce the problem of long-term recall; however, longitudinal designs necessary to collect information about IPV over women’s life course are costly, require a long study period, and are likely to suffer from sample attrition. More important, such designs would pose major ethical problems; following participants who experience IPV without intervening compromises their safety and well-being. Yet intervening would jeopardize the study design needed to examine the occurrence of IPV. Thus enhancing respondents’ recall in retrospective studies remains the most practical alternative. The most promising method for optimizing retrospective recall is the Life History Calendar (Freedman, Thornton, Camburn, Alwin, & Young-DeMarco 1988). This flexible method has been applied to studies of IPV (Yoshihama, Clum, Crampton, & Gillespie, 2002; Yoshihama, Gillespie, Hammock, Belli, & Tolman, 2005; Yoshihama, Hammock, & Horrocks, 2006) and shows promise for use in cross-sectional, large-scale surveys, serving as a more cost-effective alternative to prospective longitudinal studies.
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Life History Calendar (LHC) Method Although the LHC method can and has been used to assist in collecting qualitative data (Martyn & Belli, 2002), in this article we discuss the application of the LHC method to quantitative studies using face-to-face interviews. The LHC method uses a calendar format usually within a semistructured interview schedule to help the respondents remember the timing and other aspects of events of interest. In general, a respondent in an LHC interview is asked to first report on events that are memorable and/or relatively easily recalled, which then can serve as memory aids in remembering less easily recalled information (Freedman et al., 1988; McPherson, Popielarz, & Drobnic, 1992). The interviewer records the respondents’ experiences in a preprinted calendar-like form in plain view of the respondent. This format, unlike a conventional interview format where the interviewer records the respondent’s answers on a form available only to the interviewer, allows the respondent to use the information recorded on the calendar as a memory cue in recalling the occurrence and timing of events subsequently asked. The LHC method has been applied to studies of various topics, such as youth delinquency and violence, stressful life events, and fertility history (Axinn, Pearce, & Ghimire, 1997; Belli, Stafford, & Alwin, 2009; Caspi et al., 1996; Ensel, Peek, Lin, & Lai, 1996). Studies that compared the retrospective reports collected by the LHC method and reports collected prospectively have found high degrees of agreement between the two (Caspi et al., 1996; Ensel et al., 1996). Interestingly, reports of more subjective, ambiguous events, such as an increase in arguments with spouse and sexual difficulties, had higher agreement than employment events (Ensel et al., 1996). Besides Caspi and colleagues’ (1996) study of youth in New Zealand, which included assessment of IPV, LHC was first applied to IPV in a study of low-income women in Detroit, MI (Yoshihama et al., 2002). Psychometric data indicate good reliability and validity: The test-retest correlation was r = .80 for the number of lifetime abusive partners, and high test-retest agreement was found on whether the respondent reported experiencing physical violence (95%), sexual violence (80%), and threats/harassment (90%; kappa range .53-.71). The respondents’ perceived effectiveness of the LHC interview (M = 3.9 on a 5-point scale) was not associated with either their ages or the lengths of the interviews. Furthermore, when compared to a conventional behavior-specific interview method in a different sample drawn from the same sampling frame, LHC interviews elicited more reports of IPV incidents, especially those occurring in the distant past (Yoshihama, 2009; Yoshihama et al., 2005). To illustrate what a LHC instrument looks like, we will describe the paper instrument (Calendar) we developed in a recent study of Asian battered women. We placed various life events and situations on the calendar’s vertical axis, and time units (the respondent’s age and calendar year) on the horizontal axis (Figure 1) . Every other column is color-coded to help distinguish them and to minimize the risk of recording events in the wrong column.
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ID: ___________ YEAR AGE Residential move Schooling Work Children
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Figure 1. An example of life history calendar instrument
Note: [Letters in brackets] denote coding used: C = cohabitation; M = marriage; S = separation; D = divorce; AN = annulment; W = widowhood; AB = abandonment; Aw = away; K = knife; G = gun; O = other.
Vertical Axis As seen in Figure 1, prior to asking about the study’s main focus—the respondent’s experiences with IPV—we asked about a number of life events and situations designed to serve as memory cues. This organization reflects the thrust of the LHC method: first obtaining information about memorable and/or easily recalled experiences and using such information
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to assist the respondent’s recall of events that are more difficult to remember. These types of events and life situations were selected based on previous studies (Belli, 2000; Yoshihama et al., 2002). Personal events, such as births of children, can serve as effective memory cues to recall sensitive and difficult aspects of life such as IPV. Although incorporating a larger number of memory cues can assist in the respondent’s recall, collecting data for the sole purpose of using them as memory cues extends interview length. Thus it is critical to select events and situations that are conceptually and/or empirically linked to the study’s main focus so that they “double-up” as both memory cues and covariates in analyses. In general, we placed less sensitive questions first, a strategy designed to facilitate the development of rapport between the interviewer and respondent before asking more sensitive questions.
Horizontal Axis The calendar’s time units can be any increment of time (e.g., one year, month, week, or day) appropriate to the study; we used the respondent’s age. This decision reflects the relatively long recall period to be covered (up to 45 years) in this study, which made it difficult for the respondent to accurately recall a smaller time unit in which a particular event occurred. Smaller time units can be used in studies that cover a shorter period of time (e.g., a study of adolescent dating violence or repeated data collection in a panel study of short intervals). For example, a study that assessed stressful life events in the previous 12 months used three parts of a month (beginning, middle, and end) as the time unit (Kessler & Wethington, 1991). Although we chose to place the time unit on the calendar’s horizontal axis, it can be placed on the vertical axis depending on the specific purpose and scope of the study. Depending on the number of time units covered in the recall period and the number of life events/situations to assess, the calendar instrument can be quite large. For respondents aged 25 or younger, the calendar size was 22 × 8 inches (2 letter-sized papers); for older respondents, the size extended up to 22 × 40 inches. This sizing allowed most cells to be less than 1/4 inch × less than 1/2 inch. Although having larger cell sizes to record respondents’ answers was preferable, a larger sheet of paper would have been difficult to use in this and other communitybased survey settings where large desktop space may not be available.
Interview Administration We developed an interview schedule to guide data collection in a semistructured format. To aid the interview, we used a respondent booklet that listed a range of response options, such as a list of countries for assessment of the respondent’s and her partner’s country of birth and a behavior-specific list of types of IPV. As indicated previously, prior to answering questions about IPV, respondents were asked about their experiences in various life domains. These questions were designed to build an overview picture of a respondent’s life to later aid her recollection of other, more sensitive life events. Subsequently, respondents were asked about relationship history. Respondents in Yoshihama’s previous study
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of IPV using a LHC instrument in Detroit identified relationship history as an important memory cue for remembering their experiences of IPV (Yoshihama et al., 2002). In general, we asked about the first intimate partner, and then about subsequent partners chronologically; however, some respondents chose to provide relationship history starting with the current partner and move their way backward. Some respondents blended forward and backward recall. The main aim of this section of the interview was to identify all intimate partners in the respondent’s life and obtain information about the timing of relationship formation, cohabitation, marriage, separation, and divorce. To distinguish specific partners, we obtained the initials or first name of each partner. “Intimate partner” was self-defined by respondents. Respondents were told that the study was about abuse in “relationships with boyfriends and husbands.” We also asked the respondents about any periods in which they were generally dating but involved with no one in particular. The interviewer then asked the respondent whether she had experienced IPV in each intimate relationship mentioned in the previous section using behavior-specific items: physical IPV (5 items), as well as other forms of IPV (sexual violence, stalking, threats, and emotional and financial abuse). These various behavior-specific IPV acts were written on a respondent booklet. The interviewer probed about the age at which the respondent experienced each type of IPV for the first time and whether it happened in subsequent years, and if so, at what age.
Analytic Approaches Using LHC Data LHC data lend themselves to a variety of analytic strategies. For example, in a study of low-income women in Detroit, Yoshihama and colleagues (2005) used survival analysis to investigate whether the probability of experiencing IPV varies between groups. Using the same data, Yoshihama and colleagues (2006) examined whether previous receipt of welfare was associated with a subsequent risk of experience of IPV, using a type of longitudinal data analysis called Generalized Estimating Equations (GEE). By controlling for length of exposure to risk, these methods represent advances over simpler analysis methods that examine unadjusted proportions of respondents reporting IPV. Another analytic approach that can be used to analyze the life course data collected through LHC is multilevel modeling (MLM; Hedeker & Gibbons, 2006; Raudenbush & Bryk, 2002). This approach models each individual’s trajectory of change on a dependent variable (e.g., year-to-year experiences of IPV) across the life course and estimates an average model that describes the general trajectory of the sample or identified subgroups. MLM can incorporate covariates that reflect differences between individuals (e.g., country of origin, age at interview) as well as time-varying covariates that reflect changing characteristics or circumstances for a given individual (e.g., age, relationship status). Analyses can also examine interactions between individual-level and time-varying variables (e.g., to test whether life course trajectories of IPV vary among women who were interviewed at different ages). MLM is especially useful for LHC data because it can appropriately incorporate life course data that vary in length, as is common when individuals vary in age at the
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time they are interviewed. It can also accommodate periods of data that cannot be recalled or are missing for some other reason. Methods are available for analyzing all forms of life course data, including variables that are continuous, count, ordinal, and categorical. In addition to statistical procedures that allow sophisticated assessment of model fit and extensive testing of specific effects, results can be presented in graphical form, thereby facilitating interpretation and communication of findings.
Example of MLM Analysis of Data Gathered Through LHC To illustrate the use of MLM, we present an analytic example using data gathered through the LHC presented earlier (illustrated in Figure 1), modeling the trajectory of physical IPV over the life course. The respondents were 87 women of Filipina descent aged 18 to 60 in the San Francisco Bay Area, who were interviewed as part of a larger study of IPV and help-seeking among Asian women. Approval was obtained from an institutional review board. Specific selection criteria included having experienced physical violence, sexual violence, and/or stalking at the hands of an intimate partner while residing in the San Francisco Bay Area (comprised of nine counties). Although Filipinos are the second largest in population size and third fastest-growing Asian population in the United States (Barnes & Bennett, 2002), they are severely understudied in IPV research. Yet Filipina women are overrepresented in IPV-related homicide statistics. For example, in San Francisco, 3 out of 5 IPV-related homicide victims in 1999 were Filipina, and in Hawaii, 5 out of 7 IPV-related homicides in 2000 were Filipinas (Yoshihama & Dabby, 2009). Respondents were recruited through various community outreach methods, including distribution of flyers and other recruitment materials via email and listserves and at community events and centers, places of worship, culturally specific vendors and other gathering places; placement of advertisements in mainstream and ethnic media outlets; and outreach to staff at community-based organizations, informants, and stakeholders. The interview lasted on average 57.81 min (SD = 22.63). Each respondent received US$45 (later increased to US$75) for participation in the study. Women’s mean age at the time they were interviewed was 40.70 (SD = 11.13). The 87 women reported their experiences of abuse starting at age 16, yielding data of about 2,233 person-years. More than half (58.6%) of the women were born in the Philippines; 36.7% were born in the United States, and 4.5% were born in other countries. Of those born outside the United States, 23.6% immigrated before age 13, and 29.1% between 13 and 24. At the time of the interview, 37.9% had a high school diploma or equivalent, 18.3% had additional vocational training or an associate’s degree, and 41.4% had attained a bachelor’s or master’s degree. Most (85.1%) women had at least one child. All but four of the women reported having experienced physical IPV in at least 1 and up to 23 years (M = 6.03, SD = 5.11). The age at which women reported first experiencing physical IPV ranged from 16 (the initial year of the calendar) to 55 (M = 26.17, SD = 9.47).
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Analysis To obtain a clearer picture of the timing of women’s experiences of physical IPV across the life course, we used MLM to model women’s individual and collective trajectories. Because we were interested in whether women experienced any physical IPV in a given year, we specifically used multilevel logistic regression, as implemented in HLM software (Raudenbush, Cheong, Bryk, & Congdon, 2004). This approach models changes in the probability of experiencing abuse and provides ways to test whether changing probabilities are associated with covariates that change over time (known in MLM as Level 1 covariates, e.g., age) and those that are constant over time but vary across women (Level 2 variables, e.g., a woman’s age at the time of the interview). The effects of Level 1 covariates can be estimated as random (i.e., estimated for each individual and then averaged) or fixed (estimated as the same for all women). Because we expected considerable variability in individual women’s trajectories, we estimated all Level 1 effects as random. We expected that women’s trajectories of abuse would not be strictly linear but were likely to accelerate and decelerate at various points; therefore, we included polynomial terms (age-squared and age-cubed) to capture trajectory curvilinearity. Because the LHC started at age 16 rather than 0, we centered age at 20, which was the age of the youngest woman’s interview. In other words, we subtracted 20 from each age across the life course, effectively setting age 20 as the model intercept or zero point. This is commonly done in longitudinal analysis and has the advantage of both making the model intercept interpretable and reducing collinearity among polynomial and interaction terms (Singer & Willett, 2003). For similar reasons, we centered age at the time of the interview to 40, which was near the mean age at which women were interviewed.
Results Results of the MLM analysis are presented in Table 1. In the left panel, the fixed (average) effects indicate the contribution of each covariate to the average estimated trajectory of physical IPV. The odds ratio (OR) for the intercept indicates that for women interviewed at 40 (the centered intercept for age at interview), the probability of having experienced physical IPV at age 20 (the centered intercept of age) was .09. Across the life course, there was a significant positive linear effect of age (OR = 1.38) as well as a significant negative effect of age-squared (OR = 0.98), indicating that the probability of physical IPV increased over the early portion of the life course and then declined. The pattern of this complex effect can clearly be seen in the graph of modeled effects in Figure 2. In general, the probability of physical IPV increased from age 16 (labeled as –4, due to centering at age 20) to the early 30s, then declined. As can be seen in Figure 2, the effect of age on IPV across the life course varied, depending on the age of the woman when she was interviewed. Women interviewed at younger ages reported earlier physical IPV, while women interviewed at older ages reported later onset but a sharper rate of increase. These effects were significant, as can be seen in the bottom section of Table 1. The probability of experiencing IPV at age 20 (the intercept)
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Table 1. Multilevel Logistic Regression Model of Physical IPV Across the Life Course, by Age at Interview Fixed (average) effects
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Intercept -2.46 Time-varying covariates (Level 1) Age (linear effect) 0.32 -0.02 Age-squared (curvilinear effect) Time invariant covariates (Level 2) -0.10 Age at interview Age at interview × Age 0.01 (linear)
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Note: Level 2 N = 87 women; Level 1 N = 2,233 person-years. **p < .01. ***p < .001.
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Figure 2. Estimated probability of physical IPV across the life course, by age at interview
Note: Plotted values are estimated from the multilevel model. Age at interview was analyzed as a continuous variable; the lines illustrate the effects of this variable at three selected points—M (age 40), –1 SD (age 30), and +1 SD (age 50).
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was 10% less (OR = 0.90) for each additional year in age at the time of the interview. The increasing probability of physical IPV across the life course was slightly steeper for women who were older at the time of the interview: 0.01% greater (OR = 1.01) for each year older at the time of the interview. Age at the interview did not significantly affect deceleration (the declining, curvilinear effect of age-squared across the life course); physical IPV probability declined at a similar rate regardless of the age at which a woman was interviewed. This is partly because the later years of the life course trajectory relied more heavily on the experiences of women who were older when interviewed. The right panel of Table 1 shows the random effects of the Level 1 variables, or the extent to which trajectories of physical IPV across the life course varied among the 87 women. All three terms—intercept, linear, and curvilinear effects of age—had large standard deviations that were significantly different from zero, indicating that women’s individual trajectories varied substantially, despite the similarities in pattern that were reflected in the significant fixed or average effects. This suggests that a substantial amount of variance in women’s physical IPV trajectories remains to be explained by variables other than age and age at the interview.
Discussion The combination of LHC methods for data collection and MLM approaches to data analysis is a promising strategy for examining women’s experiences of IPV across the life course. LHC interview methods offer improvements in memory cuing and recall, and MLM analysis makes full use of time-variant and time-invariant data that are gathered. Combined, these two methods open a variety of research questions to examination. Both methods are flexible and can be used to examine life course trajectories of many types of experiences, including social support, help-seeking, and well-being. The combination of methods can be used to examine the influence of many types of covariates as well. Building on a basic model of age and age at interview, as in the example presented here, covariates can be added to explain additional variance in life course IPV. For example, trajectories of IPV across the life course can be examined for subgroup differences, addressing research questions about variability in the timing and level of risk for IPV by age at immigration to the United States, country of origin, or other variables distinguishing subgroups of women. Assessing variables that change across the life course, such as relationship status, pregnancy, living situation, employment, and social support, would allow examination of the effects of these time-varying covariates on changes in IPV risk across women’s life course. Adding time-varying covariates such as these would allow researchers to ascertain, for example, whether risk for IPV changes significantly during pregnancy, whether IPV changes in intensity during various relationship phases (e.g., initial phase, ongoing marriage, postseparation), and whether IPV risk is associated with women’s employment status. Examining the trajectories of multiple forms of IPV would allow examination of the relationships among occurrence of physical, sexual, and emotional IPV, stalking, and threats, as well as the effects of the co-occurrence of multiple types of IPV on changes in well-being
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and help-seeking. Combining both time-varying and between-person covariates would allow examination of cross-level interactions to examine questions such as whether the risk for physical IPV during pregnancy is similar or different for women in various cultures and whether there are cultural, regional, or cohort differences in the timing of stalking relative to relationship status and phase. LHC data collected in yearly or other time increments can be configured in numerous ways that make full use of the flexibility of MLM analytic strategies. Covariates in the model (e.g., residential moves or efforts to seek help from police or shelters) can be configured in multiple ways, so that MLM analyses can examine a variety of research questions related to timing and duration of effects. Three possible configurations are contemporaneous, lagged, and cumulative covariates. Contemporaneous covariates are hypothesized to exert immediate effects, such as the effect of pregnancy on IPV occurring while a woman is pregnant. Lagged covariates exert delayed effects that can be seen in a later time period, such as the possible effect of loss of social support on IPV in a subsequent time period. Cumulative covariates exert compounding effects that get stronger with repeated occurrence, such as the effect of repeated moves over time on life course IPV. The flexibility of MLM allows the researcher to examine these alternative configurations of LHC data and to make formal statistical comparisons of their ability to explain changes across the life course. The combination of LHC and MLM methods could also be productively applied to understanding the influences on life course help-seeking for IPV (e.g., calling the police, seeking protection orders, using domestic violence programs). MLM analyses could address research questions such as: What types of help are likely to be sought by women with different life course experiences of IPV? When in the life course are various forms of help-seeking most likely? Do the cumulative effects of continued or repeated IPV over time increase or decrease the likelihood that women will seek various forms of help? Do positive or negative help-seeking experiences over the life course have lagged or cumulative effects on subsequent help-seeking? Results of analyses such as these would be invaluable for policy and practice efforts to improve access and help-seeking responses. One of the strengths of the M LM approach is the ability to examine the effects of substantive covariates while adjusting for the effects of age and for cohort effects due to differences in respondents’ ages at the time of the interview. Although LHC methods have been observed to reduce recall bias relative to noncued methods, especially for events experienced in the distant past (Yoshihama et al., 2005), our analysis found significant cohort effects, with older women reporting later initial IPV and lower levels of IPV overall. It is unclear if these differences reflect recall biases, historical shifts in women’s perceptions of what behaviors “count” as IPV, age-related differences in willingness to disclose IPV, or actual cohort differences in the level and timing of IPV, which may be related to changing relationship type or timing. In this study of Filipina immigrants to the United States, cohort effects may be associated with age at immigration and may therefore reflect changes in sociocultural or political environment or stresses and challenges related to immigration itself. More research is needed to examine these possible influences. Regardless of the source
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of these effects, it is important that analyses of self-report life course data use methods such as MLM that can examine and control for cohort influences. Assessing women’s experiences over the life course using the LHC method, which involved obtaining the timing of each type of IPV for every intimate partner in a respondent’s life, might be thought to be extremely time consuming. However, the average survey administration for this study—57.81 min—is within a typical range for in-depth, face-to-face interviews. The average interview length for a similar study that used the LHC method in the Tokyo Metropolitan Area was 114 min (SD = 39.03). An LHC-based interview can be designed for any length, just as one would design a conventional interview; the researcher decides on what to ask with what degrees of specificity. As discussed previously, the key is to maximize precious survey administration time by assessing the areas of a respondent’s life that are salient to the study’s focus, which can serve as both memory cues and covariates in analytic models. LHC methods have the potential to increase the amount of information gathered from the expenditure of survey resources, while also improving the accuracy of self-report. Coupled with appropriate analytic approaches such as MLM, LHC offers important methodological advantages for the study of women’s experience of violence across the life course.
Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article.
Funding The data set used for this article was obtained in a study funded by the National Institute of Justice 2005-WG-BX-0009.
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Bios Mieko Yoshihama, Ph.D, LMSW, ACSW is a professor at the University of Michigan School of Social Work. Her research and practice focus on violence against women, immigrants, and community organizing. She has conducted numerous survey research projects on domestic violence and community-based prevention programs in the U.S. and Japan. Her work at local, state, national, and international levels over the last 25 years combines research and action to promote the safety and well-being of marginalized communities. Dr. Yoshihama serves on the boards and committees of various national, state, and local organizations aimed at ending violence against women. Deborah Bybee is a professor in the Department of Psychology at Michigan State University. As a community psychologist, she focuses on methodological and statistical issues in real world research, especially longitudinal approaches. Her main substantive interests are violence against women and mental health.
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