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Mixed Methods Research Lab (Penn)

Directors: Barg, Frances K., PhD; Cronholm, Peter Foster., MD, MSCE


"The goal of the Mixed Methods Research Lab (MMRL) in the Department of Family Medicine and Community Health is to foster the use of qualitative and mixed methods research methodologies with a focus on integrating key stakeholder perspectives and goals into research designs. The MMRL works with investigators to provide conceptual and technical support for community based and clinical research questions. Qualitative, mixed methods and action research are uniquely suited to capture the contextual, socio-cultural, and experiential factors that contribute to health disparities.

The MMRL offers consultation, training, and staff support at all stages of the research process, including project and proposal conception, instrument development, budget development, data collection, data management, analysis, and publication/dissemination."

MMRL staff has expertise in a variety of traditional and innovative data collection methods including observation, freelisting, individual interviews, and focus groups. The MMRL primarily uses a modified grounded theory approach to analyzing data. Grounded theory is a methodology that involves iterative development of theories about what is occurring in the data as they are collected. The process develops themes that emerge “from the ground,” based on responses to the open-ended questions developed for the proposed study.





  • Data collection training services ( Training service )

    Data collection training services include a focus group moderation training and a semi-structured interview training.

  • Project development services ( Support service )

    Project development services include:

    • Proposal development
    • Research Design
    • Sampling
    • Data Collection Strategies
    • Data Management Strategies
    • Analytic Strategies
    • Human Subjects’ issues
    • Budget development
    • Grant editing
    • Manuscript and report development
    • Reviewing and editing

  • Research and technical support data services ( Data analysis service )

    Data collection services include:
    • semi structured interviews
    • focus groups
    • observations
    • freelisting
    • narratives

    Other research support data services include:
    • iterative quantitative-qualitative synthesis
    • database development and management
    • data coding
    • data analysis
    • Institutional Review Board (IRB) protocol development
    • instrument development
    • study participant recruitment
    • study incentive management
    • transcription coordination

  • Software training service ( Training service )


  • Anthropac ( Software )

    "ANTHROPAC is a menu-driven DOS program for collecting and analyzing data on cultural domains. The program helps collect and analyze structured qualitative and quantitative data including freelists, pilesorts, triads, paired comparisons, and ratings. ANTHROPAC's analytical tools include techniques that are unique to Anthropology, such as consensus analysis, as well as standard multivariate tools such as multiple regression, factor analysis, cluster analysis, multidimensional scaling and correspondence analysis. In addition, the program provides a wide variety of data manipulation and transformation tools, plus a full-featured matrix algebra language."

  • NVivo ( Software )

    "NVivo is software that supports qualitative and mixed methods research. It’s designed to help you organize, analyze and find insights in unstructured, or qualitative data like: interviews, open-ended survey responses, articles, social media and web content."

  • Qualtrics ( Software )

  • REDcap ( Software )

    "REDCap is a mature, secure web application for building and managing online surveys and databases. While REDCap can be used to collect virtually any type of data, it is specifically geared to support data capture for research studies."

  • SPSS ( Software )

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Last updated: 2015-10-20T09:31:22.090-04:00

Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016