Research Methods Workshops

The Thursday plenary session and invited symposia are developed by the Vice President/Conference Chair.  The Thursday morning workshop sessions are developed by the Vice President-elect.  The SSWR Board of Directors and the Conference Committee provides input into the development of all invited sessions. The registration fee is $150 (by 12/2/2022), $180 (by 1/10/2023), and $225 (by 1/15/2023). Register early as space is limited!

Thursday, January 12, 2023, 8:00 AM-12:00 PM

(RMW-1) Narrative and Phenomenological Methods to Understand the Lived Experiences of Clients/Communities

Quenette L. Walton, PhD, LCSW – Graduate College of Social Work

Rosalyn Denise Campbell, PhD, MSW

This interactive workshop has been designed for those with limited experience in conducting qualitative research.  Even those familiar with qualitative work will find the deep(er) dive into narrative and phenomenological approaches beneficial.

Participants will be given a brief, general overview on the use of qualitative methods to better understand the lived experiences of individuals, groups, and/or communities.  Conducting research with diverse groups, the use of self as an instrument of data collection, and other ethical considerations will be discussed.  The presenters will then explain why and how narrative and phenomenological methods are used.  Theoretical underpinnings, methodological strategies, and data analysis techniques will be explained.  As this workshop is interactive, participants will use their own topics/areas of interest to engage in individual activities and small group discussions.

Participants will leave with a better understanding of qualitative methodology in general, narrative and phenomenological approaches in particular, and a topic-specific study outline to guide a future project.

(RMW-2) Using Python for Applied Data Science in Social Work: Introduction to Concepts, Methods, Model Building, and Critical Perspectives 

Cheng Ren, PhD – School of Social Welfare, University of California Berkeley

Jonathan Alschech, PhD – School for Social Work Smith College

Brian Newton, PhD – Seneca Family of Agencies, Data & Research Department

Woojin Jung, PhD – School of Social Work, Rutgers University

Kevin White, PhD – School of Social Work, East Carolina University

This workshop is designed for social work researchers interested in using data science for their research and who wish to learn more about the promise and questions surrounding data science in social work. Data science is the term for a set of analytic skills and methods that span multiple disciplines, such as math, statistics, and computer science. Analytic approaches are evolving rapidly, but include classification, regression, and clustering techniques. Data science often involves working with relational databases and fitting models with large datasets.

This workshop will discuss key concepts and definitions in data science, and introduce several methods and tools using Python, one of the most effective programming languages for data science. Participants will use Google Colab, which provides a free, browser-based, and easy-to-use environment for running Python packages (e.g., NumPy, Pandas, and Matplotlib) and code. This workshop will also provide opportunities for hands-on exercises with real data, such as ensemble methods, text analysis and spatial/image analysis. The session will conclude by discussing the epistemological normative and political questions posed by social work data science.

The workshop is meant for social work researchers across all career levels, including graduate students and early to late career social work researchers in academia, government, social and health services, and NGOs. Previous experience with basic statistics, including OLS regression modeling, Generalized Linear Modeling, and some experience using statistical software (e.g., SPSS, Stata, R, Python) is recommended. However, no previous knowledge of specific data science methods in the social and health sciences is required.

(RMW-3) Systematic Review and Meta-analysis: A Hands-on Workshop 

Ruopeng An, PhD – Brown School of Social Work, Washington University in St. Louis

A systematic review attempts to identify, appraise, and synthesize all the empirical evidence that meets pre-specified eligibility criteria to answer a specific research question. Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. All meta-analyses should be based on studies identified from a systematic review, but not every systematic review contains a meta-analysis. It is because the designs of the studies may be too different, the outcomes measured may not be sufficiently similar, or there may be concerns about the quality of the studies for an average result across the studies to be meaningful.

This workshop introduces the key concepts and techniques in systematic review and meta-analysis through case study-based presentations and hands-on programming demonstrations. By the end of the workshop, participants should be able to (1) Layout a systematic review protocol, (2) Define inclusion and exclusion criteria, (3) Design a keyword search algorithm and implement it in multiple databases; (4) Design a data extraction tool; (5) Use a study quality or bias assessment tool; and (6) conduct a fixed/random effect meta-analysis on a continuous or binary outcome using R.

(RMW-4) Community Based System Dynamics

Peter Hovmand, PhD – School of Medicine, Case Western Reserve University

This workshop introduces participants to community-based system dynamics (CBSD) as a participatory systems science method for engaging and working with organizations and communities to address complex problems and advance equity. The first part of the workshop will focus on a general overview of when and how to apply CBSD to identify systems underlying structural violence and the use of group model building. The second part will provide a demonstration of how results from group model building can be translated into computer simulation models that can in turn be quantitatively analyzed to identify leverage points and the underlying feedback mechanisms driving trends.

Participants will see a range of domestic and international examples from CBSD research across a wide variety of topics from food, energy, and housing insecurities to health disparities. The emphasis will be on providing participants with a broad view of CBSD and applications to advancing social work research to understand and changing systems underlying structural violence to advance equity.

Participants will experience a group model building exercise, and see a demonstration of software for simulating models, and publishing interactive online interface that run on any smartphone, tablet, or desktop/laptop computer. Participants will be able to access workshop materials and explore models with a free workshop license to the software.

While CBSD draws on both qualitative and quantitative methods, the approach does not rely on prior formal training in qualitative or quantitative research methods, computer programming, or math beyond basic algebra.

Special Sessions on Research Priorities and Capacity Building

These training-oriented sessions target cutting-edge topics vital to contemporary social work research. Enroll early for these important opportunities to engage with national experts, funding institutions, and research colleagues. The registration fee is $50 (by 12/2/2022), $60 (by 1/10/2023), and $75 (by 1/15/2023). Register early as space is limited!

Thursday, January 12, 2023, 8:00 AM-10:00 AM

(SSRPCB-1) Developing an Anti-Racist Scholarly Agenda 

Gina Miranda Samuels, PhD – Crown Family School of Social Work, Policy & Practice, Univ of Chicago

Sean Joe, PhD – Brown School of Social Work, Washington University in St. Louis

(SSRPCB-2) Building Successful Community Partnerships to Address Complex Problems

Mary Ohmer, PhD – School of Social Work, University of Pittsburgh

Today’s complex social problems require successful partnerships among academics, community partners, residents, government agencies, and policy makers.  The goal of this workshop focuses on strategies for building successful community partnerships to address social issues that have become exacerbated by persistent inequities. Strategies will be shared from a 5-year collaboration among county government, community-based organizations and residents, schools of Social Work and Pediatrics, and a Children’s Hospital to address community violence and trauma through multi-level violence prevention interventions focusing on community resiliency and thriving (funded by the Substance Abuse and Mental Health Services Administration). This partnership addresses the stark increase in community violence in our region. For example, homicide rates increased by 43% in the city of Pittsburgh from 2019 to 2021, largely reversing the declines of previous years. Furthermore, Black men are victims in 66% of annual homicides on average, with most the ages of 18 and 34 years old, despite making up only 6% of the population (Allegheny County, 2022).

This workshop will discuss partnership strategies, including a county wide steering committee; community advisory boards, paid community organization partners, resident facilitators and mentors in target neighborhoods; and research strategies. The learning objectives are to (1) discuss strategies and principles for successful community intervention and research partnerships; (2) discuss challenges and lessons learned through a case study of a partnership addressing community violence and trauma; and (3) apply these strategies and principles to complex social problems in your own community using a deep dive worksheet and hands on activity.

(SSRPCB-3) Mixed Methods Research Using R: Combining Structural Topic Modeling with Linear Models

Richard Smith, PhD – School of Social Work, Wayne State University

Maria Rodriguez, PhD – School of Social Work, University at Buffalo

Goals: The goal of this workshop is to practice key packages used for mixed methods research in R. In particular, this workshop will be useful for those analyzing large bodies of text to create codes used in subsequent quantitative analysis. First, we briefly describe how to use R using Posit (i.e., the integrated development environment software formerly known as RStudio) and Github (i.e., a cloud solution for data and code) for reproducible research. Second, we will review generalized linear models and mixed models in R that work well with mixed methods (e.g., logistic, count and survival). Third, we will present structured topic modeling, a method of organizing and analyzing large volumes of text (e.g., policy documents, case notes, interviews, newspaper articles, or Twitter feed). Resources will be shared with the workshop participants on Github (see below).

Appropriate Level of Attendee Knowledge: This workshop is for researchers who have experience with mixed methods research and would like to take advantage of some of the packages that R has to offer to support that work. Previous experience with R is recommended, but not necessary. Researchers with experience using any statistical script should be able to follow along.

Other Details: Please bring a Mac, Windows, or Linux laptop with software and data pre-installed. We will be using the Posit ( Integrated Development Environment (a.k.a., RStudio) in conjunction with Github: smithrichardj/SSWR_2023_MM_R. Materials will be posted by January 5th.

Thursday, January 12, 2023, 10:15 AM-12:15 PM

(SSRPCB-4) Building Capacity for Art-Based Social Work Research, Education, & Practice 

Rogério Pinto, PhD – School of Social Work, University of Michigan

Shelley Cohen Konrad, PhD – School of Social Work, University of New England

Evidence suggests that art-based practices – illustrations, photography, poetry, dance, music, and performance – used as research methods offer insight that informs and improves practice with disenfranchised and minoritized groups. Art-based methodology surfaces untold narratives that both deconstruct and add to our knowledge of clients’ realities. Creative tensions raise fruitful dialogue that evoke diverse perspectives offering new ways for participants to give voice to their experiences and meaningfully engage researchers.

This workshop invites social workers to learn about art-based research and share experiences using it to guide inquiry, education, and practice. Artistic skills are not required! Brief didactic presentation, small group discussions, consultation, and debrief will be used in the workshop. Collectively, we will (1) define what we mean by “art”, (2) describe and demonstrate how art-forms and artifacts are used in the research process, (3) discuss how art-making is used for data collection, interpretation, and dissemination of findings, (4) consider how community-engaged art-based inquiry enhances beneficial collaborations with communities, and (5) engage in an experiential exercise that applies key principles for using art practices in research. Participants will be encouraged to experiment with art as a critical tool for social work practice, build and sharpen their research skills using myriad types of expressive modalities, and cull ideas to bring back to their respective settings and communities. We hope to promote the use of art-based research to better inform education and practice, especially with diverse populations and those experiencing circumstances that are often too difficult for words.

(SSRPCB-5) Emergent Methods in Community-Engaged Research 

Claudette Grinnell-Davis, PhD – Anne & Henry Zarrow School of Social Work, University of Oklahoma

Amy Castro, PhD – School of Social Policy & Practice, University of Pennsylvania

Bailey Stevens – Anne & Henry Zarrow School of Social Work, University of Oklahoma

This workshop for scholars at any career stage focuses on using emergent technological methods with populations experiencing new forms of inequality and alienation due to marginalization by global markets and impersonal policy subsystems in ways that prevent collective organizing, create legislative invisibility, and generate mechanisms of inequality that long-standing research approaches are poorly suited to address. Likewise, community-based researchers often encounter challenges in understanding phenomena not amenable to quantitative measurement, particularly around community process, social location, and time. These dynamics create methodological gaps that mixed-methodologists address through promising power-sharing and emergent technological approaches, but the strategies employed are not well understood by the systems governing research infrastructure. The workshop will present best practices for resolving common ethical and IRB tensions, data-use agreements, and managing expectations around public-facing emergent methods.  The presenters will draw from case studies in child welfare, unconditional cash, and time-use research. Case study one focuses on using online collaboration tools for building consensus and collective vision during two participatory action child welfare projects.  The second focuses on data-visualization of aggregate spending data from unconditional cash experiments co-created with elected officials, community organizers, and local stakeholders. The third highlights how a mixed-methods app-based data collection strategy was used to resolve methodological challenges when studying time-use, economic mobility, and unpaid care work. Hour one will focus on strategies for mitigating the community and infrastructure challenges commonly encountered with emergent methods. In hour two, participants will have the opportunity to workshop and troubleshoot their own projects with facilitators.

(SSRPCB-6) Disaggregating Racial-Ethnic Classification Systems to Improve Data Equity 

Tara Becker, PhD – UCLA

The most commonly used classification systems to code race-ethnicity obscure the wide variation in racial-ethnic experiences within these broad categories. This workshop will provide attendees with a more detailed understanding of the impact of data collection, coding, and tabulation on efforts to disaggregate racial-ethnic data into more granular categories in order to provide greater insight into the diversity within these groups. Specifically, it will provide an overview of the ways in which federal data collection guidelines influence the collection and weighting of racial-ethnic data in the United States, methods that have been used to expand upon these guidelines to collect and tabulate more granular data, when such expansions may be warranted, and the effects of data collection methodology on the representativeness of data from small racial-ethnic populations. In addition to discussing how, when, and why one should consider disaggregating racial-ethnic data, it will consider—on a conceptual, rather than statistical level—the ways in which methodological decisions, such as the language(s) in which a survey is administered, the method(s) used to oversample racial-ethnic groups, and weighting decisions can affect the generalizability of statistical estimates derived from this data and influence what we know about more granular racial-ethnic populations.