Course Descriptions - Week 2
Practical Data Management and Analysis for Public Health
Donna Stroup, Ph.D., M.Sc
The course applies biostatistical methods to understanding, managing, and evaluating community health programs. Students will learn, practice, and demonstrate proficiency in data management, data editing, statistical analysis planning and execution and interpretation for public health and epidemiology. Students will design questionnaires, collect data, perform analyses with a statistical package used in public health, and provide interpretations. Students may bring data for analysis and interpretation. Students will work on actual data management and interpretation during lab sessions. As a result, the student will acquire data management and analysis skills for setting priorities and evaluating public health policy.
A Computer Laboratory will be held from 5:30 – 8:00 p.m. Monday through Thursday
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Methods in Family Violence Surveillance and Prevention
Kenneth Steinman, Ph.D. & James Mercy Ph.D.
Efforts to develop effective responses to family violence have been limited by the dearth of useful data on the topic. This course will review common types of family violence and describe efforts to develop a surveillance system. We will review available sources of case-based (i.e., police reports) and population-based (e.g. surveys) data and present a framework for assessing their quality. Using examples from Ohio and elsewhere, students will learn to analyze and present data in order to yield valid conclusions that are relevant to policy makers. The course will feature several guest speakers, including leading experts from the CDC and state agencies.
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Introduction to ArcView GIS Software for Public Health
Bob Brems, M.P.H & Jeff Smith
This course will provide introductory training in the use of ArcView 9.2 software with a focus on public health applications. Students will learn basic GIS terminology, and through the use of descriptive step-by-step exercises will use ArcView 9.2 to gain hands-on experience in using the software.
This is an introductory course and does not require prior knowledge of GIS. Students should have basic computer use skills.
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Applied Infectious Disease Epidemiology
Gregory C. Gray, M.D., M.P.H.
This course will introduce the student to the principles of infectious disease epidemiology. It will include a practical overview of host factors, environmental factors, and microbiological factors that influence this dynamic field of study. Through lectures and exercises, students will be introduced to infectious disease surveillance, diagnostic tools, outbreak investigations, vaccine trials, public health interventions, biodefense, emerging infectious diseases, and analytical approaches as they pertain to infectious disease prevention and control. Students will be introduced to a wide array of reference material (much of it public) that will help them in the practical application of course material. The course is designed for students and non-physician professionals in public health.
This course is a component of the Practice-Based Epidemiology Series
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Methods in Obesity Research/Epidemiology
Lisa Simpson M.B., B.Ch, M.P.H, FAAP
This course will help participants learn the key policy relevant questions in obesity research, a range of existing datasets (national, state, and private sector) available for use in addressing these questions, and a range of research methods to use in studying obesity including: descriptive epidemiology and variation studies, qualitative studies, multivariate models, including HLM approaches, and a focus on state level variables and quality improvement research methods to change health provider responses.
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Scientific Writing for Statisticians Tom Lang, M.A.
Scientific writing differs greatly from the writing you learned in school! This co
urse will increase the participant’s understanding of the communication process, especially in scientific enterprises; explore methods to make writing more efficient and effective; describe standards for reporting research in technical reports and scientific articles; and improve the participant’s ability to present data in tables and figures. All information will be specific, relevant, and evidence-based. The course is not about grammar, punctuation, or basic English for non-native speakers. It is about helping participants become more effective members of a research team and contributors to the scientific community.
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Meta-Analysis William R. Shadish, Ph.D.
This course introduces the use of meta-analysis of treatment outcome research. Topics will include the history of meta-analysis, time and costs of doing meta-analysis, gathering studies, coding protocols, effect size computation for continuous and dichotomous outcomes, combining effect sizes, basic descriptive analyses, fixed and random effects models for synthesizing effect sizes, outliers and influence statistics, categorical tests, multiple regression, multivariate tests, multilevel models, cumulative meta-analysis, publication bias methods, and reporting of meta-analyses. The course will include exposure to computer programs, and a laboratory component on the use of those programs.
Basic knowledge of statistics (ANOVA, regression) required; comfort with either STATA or SPSS helpful.
A Computer Laboratory will be held from 5:30 – 8:00 p.m. Monday through Thursday
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Introduction to Stata William Rising, Ph.D.
The Introduction to Stata course is a hands-on training course designed for those who wish to learn techniques for efficient day-to-day usage of Stata. The course starts with the basics of Stata. It continues with data management, data analysis (including fitting and estimation), Stata's post-estimation and testing commands, and graphics. The course finishes with an overview of how Stata works with specific types of data, including survival and survey data. The course is taught with workflow in mind, so participants will learn how to work a reproducible manner with ease.
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