Class Descriptions

SW838

Special Seminars in Research Methods for Practice and Policy

Credits:
3 Credit Hours
Prerequisites:
Doctoral Standing or Permission of Instructor
Description:
These seminars cover variable topics related to faculty and student analysis of critical and emerging issues in research methods for social work policy and practice. These topics may include research strategies, designs, techniques, and skills needed to develop knowledge of human services or research methods relevant to: the advancement of knowledge about practice interventions, the organization of service delivery, and social welfare policies; evaluation of practice, programs, and policies; the formulation and development of innovative practice interventions, service delivery systems, and social welfare policies.
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SW840

Navigating the Joint Doctoral Program

Credits:
1 Credit Hour
Prerequisites:
Doctoral Standing or permission of instructor
Description:
This seminar is designed to support doctoral students in the Joint Doctoral Program in Social Work and Social Sciences to develop skills for navigating their doctoral program and prepare for work after graduation. The first objective of the course is an introduction to knowledge and skills that will be useful for engaging with the doctoral program. The second objective is to introduce and share information, resources, and guidance on preparing for life with a doctoral degree. This will include identifying and discussing different career paths, learning about sought after knowledge and skills for different professions, and identifying resources for developing these skills. Participation in a doctoral program is often a demanding and stressful experience and developing strategies to handle the stress and demands faced by many during this period.
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SW841

Social Work Graduate Workshop

Credits:
2 Credit Hours
Prerequisites:
Doctoral Standing or permission of instructor
Description:
This Social Work Graduate Workshop is designed to support joint Social Work PhD students in developing, presenting, and receiving feedback on their research and scholarship. Through opportunities to share their work, joint Social Work PhD students will learn ways for tailoring their presentations to a Social Work audience. Joint Social Work PhD students are invited to present their research and scholarship, with work being “in progress” (e.g. not finished/polished) and guiding questions that seek feedback around its Social Work framing or orientation. The Workshop supports and challenges students with work-in-progress, enabling a space for thoughtful and constructive critique from a Social Work audience that represents a wide variety of backgrounds, perspectives, and training.
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SW842

Social Equality and Equity

Credits:
3 Credit Hours
Prerequisites:
Doctoral Standing or permission of instructor
Description:
This course focuses on variations in the structure of opportunity and outcomes within the United States and between the United States and other countries. The forms inequality may take and changes over time in conceptions of inequality and inequity will be examined. Attention will be given to: effects of diverse values, perspectives, and ideologies on conceptualizations of social equality and equity; operational definitions of these conceptualizations; the antecedents and consequences of equality/inequality and equity/inequity as variously defined; and the implications of the above for social work and social welfare. Current levels of inequality in the United States will be assessed by critically reviewing the literature on differentials in opportunities and outcome. Comparative analysis of empirical work on inequality within the United States and between the United States and other countries will be used as a basis for examining debates about the relative costs and benefits of particular levels of inequality and about the trade‐off’s between equality and other social goods. Key research issues and gaps in knowledge will be identified.
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SW849

Special Seminars in Social Context for Practice and Policy

Credits:
3 Credit Hours
Prerequisites:
Doctoral Standing or permission of instructor
Description:
This seminar covers particular aspects of individual and family well being, social participation, social equity and equality,responses to social trends, or other human conditions that may influence social work and social welfare. The seminar will consider the influences of diverse ideologies and values on conceptualizations of these conditions, operational definitions of the variables considered, an analysis of antecedents and consequences of the conditions, and implications for social work and social welfare of the above. Students will analyze how social units are affected by and respond to current or emerging social trends. Selected trends will provide the substantive theme, addressed with five foci: the trend's nature and antecedents, its consequences for particular social units, social problems/opportunities created by it, responses of various social units to those problems/opportunities, and implications for social work and social welfare in responding to the trend through innovative policies, programs, and treatment methods. Differential effects of the trend on subgroups such as minorities, women and the elderly will be of special interest. Topic selection criteria will include: timeliness, relevance to problems/opportunities of importance to social work/social welfare, and congruence with faculty scholarly work.
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SW850

Statistical Methods in Social Sciences I

Credits:
4 Credit Hours
Prerequisites:
None
Description:
First of a two‐part introductory statistics sequence for doctoral students in SW & Social Welfare. Students will learn important theories and concepts behind key statistical methods and their applications to addressing social problems and issues and advancing social justice. Core topics covered in this course will include descriptive statistics, point estimation and confidence interval, central limit theorem and its role in inferential statistics, univariate statistical methods, and analysis of variance methods. Students will learn R statistical software for all analyses and class assignments.
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SW851

Statistical Methods in Social Sciences II

Credits:
4 Credit Hours
Prerequisites:
None
Description:
Statistical Methods in Social Sciences II --- This is a foundational course for statistical analyses in social sciences. As the second of a required statistics sequence for doctoral students in Social Work and Social Welfare, students will further advance their understanding of correlation and regression analysis theories, and their applications to addressing social problems and issues and advancing social justice. Core topics covered in this course will include Pearson's correlation (r), other measures of association (e.g., Spearman, Phi coefficient, Point-biserial), simple linear regression, multiple regression, simple mediation and moderation, and be prepared for advanced topics, e.g., multi-level modeling, structural equation modeling, among others. Doctoral and graduate-level students outside of the Social Work and Social Welfare program may be eligible to take this course without taking the first course in the series (SW 850), assuming they have gained the content of SW 850, elsewhere.
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SW858

Special Seminar: Poverty and Inequality (Public Policy)

Credits:
3 Credit Hours
Prerequisites:
Doctoral Standing and permission of instructor
Description:
This course analyzes the conditions and causes of poverty within the United States and the variety of economic, social, and political responses to it. The first part of the course explores the problems of poverty, including a discussion of various causal theories of poverty and the underlying implications of these theories. The second part of the course analyzes specific problems and policy proposals, with particular attention to the most recent round of legislative reforms since the mid‐1990’s.
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SW861

Mixed Methods Research

Credits:
3 Credit Hours
Prerequisites:
Doctoral Standing or permission of instructor
Description:
Social workers often wonder “what is mixed methods research?” And “when should I use mixed methods in my own research?” While mixed methods have been around for a long time, they are beginning to gain momentum in social work research. Before decisions can be made about mixed methods, other aspects of the research process must be addressed such as methodological standpoint, the research question, collecting the data, and analyzing and interpreting the data. Mixed methods are more than mixing the methods: it also involves mixing the assumptions that we have about the methodologies, the methods, and the data.  In this course, students will be introduced to mixed methods research in the social and behavioral sciences. A primary aspect of the course will be to teach students how to determine if mixed methods are necessary, given their problem statement and research questions. In this course, less attention will be given to the single methods alone, but rather, how they integrate during each stage of the research process for a mixed methods study. The focus of the course is namely how each single method converges during each step of the research process to answer the mixed methods research questions. The course is best suited for students with comfort and familiarity using one or both of the single methods (qualitative or quantitative). By the end of the course, students will be able to:  1. Realize and adopt a philosophical stance and highlight the major theoretical underpinnings of mixed methods research. 2. Identify under what conditions someone should consider conducting a mixed methods study and understand the procedures involved with choosing a mixed methods design. 3. Discuss how to conceptualize and operationalize quantitative and qualitative methods in mixed methods research. 4. Discuss measurement, sampling, and analyzing qualitative and quantitative data for mixed methods research. 5. Describe ways to integrate and interpret qualitative and quantitative data to address a mixed methods research question. 6. Describe ways to disseminate results from mixed methods research.
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SW862

Categorical Data Analysis

Credits:
3 Credit Hours
Prerequisites:
Doctoral Standing or permission of instructor
Description:
Researchers are most commonly aware of methods that are suitable for continuous dependent variables (e.g. mental health scores), such as the use of ordinary least squares regression. However many outcomes of interest to social workers, and other social researchers, are decidedly not continuous, but are dichotomous or binary in nature: entered the program versus did not enter the program; left the program versus stayed in the program; received a particular diagnosis; did not receive a diagnosis. Many researchers are familiar with the basics of logistic regression, yet do not have a grounding in some of the intricacies of logistic regression, such as generating predicted probabilities, or using interaction terms in a categorical model, which can lead to clearer and more accurate reporting of results. Further, the basic logistic regression model serves as the foundation for a wide variety of more advanced statistical approaches that can help advance social work research. Study of the logistic regression model can lead to variations of logistic regression such as logistic regression for ordered variables, or multinomial logistic regression where are more than two categories of the outcome variable (e.g. multiple forms of family violence). An understanding of logistic regression also helps to motivate understanding of models for count data such as the Poisson and negative binomial model suitable for studying counts of events such as incidence of disease or incidence of violence. Lastly, categorical data model serve as the foundation for event history models that are used to study the timing of events, such as the timing of program entry, program departure, or receipt of a diagnosis. Proposed Topics (some topics may span more than 1 week) 1) Review of ordinary least squares regression 2) Logistic and probit models 3) Ordered and multinomial logistic regression models. 4) Models for count data 5) Event history models for the timing of events
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