Tsogolo La Thanzi (TLT): Fourth Wave, Malawi, 2010 [Healthy Futures]

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16 juillet 2020

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Jenny Ann Trinitapoli et al., « Tsogolo La Thanzi (TLT): Fourth Wave, Malawi, 2010 [Healthy Futures] », Inter-university Consortium for Political and Social Research, ID : 10.3886/ICPSR37460.v1


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Tsogolo la Thanzi (TLT) is a longitudinal study in Balaka, Malawi designed to examine how young people navigate reproduction in an AIDS epidemic. Tsogolo la Thanzi means "Healthy Futures" in Chichewa, Malawi's most widely spoken language. New data is being collected to develop better understandings of the reproductive goals and behavior of young adults in Malawi -- the first cohort to never have experienced life without AIDS. To understand these patterns of family formation in a rapidly changing setting, TLT used the following approach: an intensive longitudinal design where respondents are interviewed every four months at TLT's centralized research center. Data collection began in May of 2009 and was completed in June of 2012. To assess changes on a longer time-horizon, a follow-up survey referred to as Tsogolo la Thanzi 2 (TLT-2) was fielded between June and August of 2016. This study contains data collected from the fourth wave of the multi-wave study. Each wave is comprised of three data files. The Women dataset (dataset 1) is a random sample of women aged 15-25 in 2009 (N=1,505 at wave 1), drawn from a census of the area. Likewise, the Random Men dataset (dataset 3) is a random-sample of men aged 15-25 in 2009 (N=574 at wave 1) drawn from a census of the area. The Male Partners dataset (dataset 2) contains survey data from sexual and romantic partners who were referred into the study by respondents in the women's file; this is a non-random sample of male partners, so analysts should be especially cautious with inferences. Topics covered across all waves include relationships, religion, HIV/AIDS, politics, family composition, mental health, sex and protection, pregnancy, marriage, sexually transmitted diseases, future expectations, school enrollment status, goods purchased/received, and diet. Additional demographic variables in each dataset include age and education.

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