Synchronous: Mixed-Methods for Public Health Researchers

Thursday, June 23, 2022 - Friday, June 24, 2022 - 1:30 PM-5:30 PM

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Course Description

Health outcomes are the product of complex social and biological factors, which interact at the molecular, individual, organizational, and broader ecological levels over time. There have been calls for public health researchers, specifically epidemiologists, to study the biomedical and the social causes of disease, however, it is still unclear how to do it. Mixed-methods (MM) offers a possible solution, in that qualitative approaches could be combined with quantitative ones to improve causal inference through both informing causal theory and through integrating measures of biological and social factors. While not every research question is poised to be studied through MM research, complex bio-socio-cultural problems, such as most public health and epidemiological issues, are better approached with a combined use of qualitative and quantitative methods. Public health training programs may offer limited training in MM, and public health students might be unsure about the benefits of applying these methods to their studies. This one-day course provides the rationale and a guide for students interested in applying a MM approach to their research questions. To do so, the course wil begin by describing the current paradigms guiding quantitative methods, qualitative methods and MM, highlighting the benefits and challenges of MM research. In addition, the course will describe specific applications of MM to public health research with particular attention to causal inference, including: 1) identifying new causes 2) explaining the causal mechanisms 3) identifying sources of non-comparability 4) improving measurement. We will review the different types of MM study design (i.e. triangulation, exploratory sequential) and review how to integrate data generated by specific qualitative and quantitative methods, focusing on analytical methods (i.e, survey development, joint display). Students will discuss case studies of salient MM studies that illustrate improved causal inference. The course will culminate with students designing their own MM studies using our bespoke online webtool.


Course Objectives

By the end of the course, participants will be able to:

  • Provide rationale when and why to use mixed-methods in answering critical public helath questions
  • Critically assess current mixed-methods studies in public health, including strengths and weaknesses in the conceptualization, study design, measurement, and interpretation of study findings
  • Describe the strengths and limitations of qualitative, quantitative, and mixed-methods
  • Identify and articulate different mixed-method study designs
  • Apply knowledge to design a mixed-methods study
  • Practice integrating qualitative and quantitative data during analysis

Prerequisites

None, but assumes experience analyzing quantitiatve data using statistical software (R, Stata, SAS).


Course Reading List

Zhang W, Creswell J. The Use of “Mixing” Procedure of Mixed Methods in Health Services Research.; 2012.

 

Guetterman TC, Fetters MD, Creswell JW. Integrating Quantitative and Qualitative Results in Health Science Mixed Methods Research Through Joint Displays. Ann Fam Med. 2015;13(6):554-561. doi:10.1370/afm.1865

 

Houghton LC, Troisi R, Sommer M, et al. “I'm not a Freshi”: Culture shock, puberty and growing up as British-Bangladeshi girls. Soc Sci Med. 2020;258:113058. doi:10.1016/j.socscimed.2020.113058

 

Nagata JM, Valeggia CR, Barg FK, Bream KDW. Body mass index, socio-economic status and socio-behavioral practices among Tz'utujil Maya women. Econ Hum Biol. 2009;7(1):96-106. doi:10.1016/j.ehb.2009.02.002

 

Barg FK, Huss-Ashmore R, Wittink MN, Murray GF, Bogner HR, Gallo JJ. A Mixed-Methods Approach to Understanding Loneliness and Depression in Older Adults NIH Public Access.; 2006.

 

OPTIONAL READINGS

 

Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. Third Edit. Los Angeles: SAGE Publications; 2018.

 

Susser M. Should the epidemiologist be a social scientist or a molecular biologist? Int J Epidemiol. 1999;28(5):S1019-S1019. doi:10.1093/oxfordjournals.ije.a019905

 

Glass TA, Goodman SN, Hernán MA, Samet JM. Causal Inference in Public Health. Annu Rev Public Health. 2013;34(1):61-75. doi:10.1146/annurev-publhealth-031811-124606

 

Rothman KJ, Greenland S. Causation and Causal Inference in Epidemiology. Am J Public Health. 2005;95(S1):S144-S150. doi:10.2105/AJPH.2004.059204


Instructor


Lauren Houghton, PhD

Lauren C. Houghton, PhD, takes a life course approach to understanding the intersection of environmental and hormonal factors in breast cancer carcinogenesis, focusing primarily on exposures during puberty. Dr. Houghton is interested in how culture gets beneath the skin, specifically in relation to women's reproductive lives from puberty to child rearing to menopause. She gained experience in Cancer Epidemiology as a post-doctoral fellow at the National Cancer Institute where she explored international variation in sex steroids and other biomarkers. She has extensively worked with migrant studies in Bangladesh, the UK and Mongolia to better understand genetic and environmental risk factors among females moving from low to high risk geographic areas. She has conducted fieldwork with Native Americans in the US, menopausal women in the UK and school girls in Bangladesh and is currently a co-investigator of The LEGACY girls study in New York City. Having a background in anthropology, Dr. Houghton is also interested in developing mixed-methods to be implemented in epidemiological studies to better capture biological and cultural mediators of health disparities.



Course Fee

Early registration discount before April 1, 2022: $450.00
After April 1, 2022: $500.00


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The registration period has closed for this event.


Location

Synchronous Course

 

The Zoom link for this live webinar course will be made available to course registrants prior to the start of class.


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