Methods: In one study, we interviewed Asian women (n=143) aged 18-60 who have experienced intimate partner violence in a large urban area on the west coast. In this quantitatively oriented study, we used multilevel modeling (MLM) to make full use of data gathered through LHC interviews, modeling trajectory of each individual’s life experience of abuse and help-seeking across the lifecourse. In another study, we interviewed youth and young adults (n=9) from the Democratic Republic of Congo about challenges and opportunities in schooling in a midwestern city.
Results: An MLM analysis of the LHC found significantly different trajectories of intimate partner violence by immigration status and age. The probability of contacting the police, seeking assistance from social and legal services also differed significantly. Qualitative analysis of LHC data elucidated refugees’ own communities as integral to education pathways, as middle space for making sense of and individual level barriers (ie. language, academics) and structural challenges (ie. social exclusion in schools, grade-level placement).
Conclusions and Implications: The LHC can account for linguistic, cultural and institutional diversity, as those change along the migrant journey over time and place. Further, these methods not only illustrate temporal and spatial trends and patterns, but also identify critical incidents and factors that are exceptional or anomalous. The combination of the LHC method for data collection and MLM approaches to data analysis allowed for an investigation of within-group differences, such as cohort effects and differences by age of immigration/resettlement. Adaptable to both qualitative and quantitative inquiries, LHC is a promising strategy for examining migrants’ experiences across the lifecourse, to help inform policy and yield theoretical insights for migration studies.
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