Qualitative Analysis
EDUCATIONCALENDAR/UPCOMING SHORT COURSES[Calendar widget]Upcoming short courses –Qualitative AnalysisQuantitative AnalysisSpatial Analysis & MappingSurvey ResearchStatistical ComputingOtherQualitative AnalysisQuantitative AnalysisIntroduction to Structural Equation Models (SEM)Marie E. CamerotaTBADavis 219Dates: Monday, September 19, and Wednesday, September 21, 2016Times: 10:00am - 12:00 pmSpatial Analysis & MappingArcGIS I: Introduction to GISPhilip McDanielThis hands-on course will provide an overview of ArcGIS software to beginners. Data resources from the UNC Libraries will be introduced, and the core functionality of the software will be demonstrated and explored with hands-on exercises.Prerequisites: None?No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 247Date: September 9, 2016Times: 11:00am - 12:30pmArcGIS II: Introduction to GIS FunctionsPhilip McDanielThis hands-on short course will build upon the introductory ArcGIS course that we’ve offered over the past several semesters. Exercises will focus on a variety of the tools and functionality of ArcMap, including importing, geocoding, joining, and manipulating data within ArcMap.Prerequisites: This course presumes at least beginner experience in using ArcGIS, so attendees should have some prior experience.?No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 247Date: September 16, 2016Times: 11:00am - 12:30pmMapping Census Data in ArcGISPhilip McDanielThis hands-on short course will introduce a variety of sources for U.S. Census data, and highlight the pros and cons of each. Exercises will focus on importing, manipulating, and displaying Census data within ArcMap. A brief overview of the U.S. Census will be provided.Prerequisites: No prior experience in working with Census data is required, though some familiarity will be helpful. This course presumes either beginner or intermediate experience in using ArcGIS, so attendees should have some prior experience.?No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 247Date: September 23, 2016Times: 11:00am - 12:30pmIntroduction to the QGIS Open Source Software: Part 1Scott MadryThis will be the first of two, 2-hour hands-on workshops using the QGIS open source GIS package. This first workshop will begin with an overall introduction to the “OSGEO Stack” of open source GIS tools, including QGIS, GRASS, R and other tools. Then we will explore the QGIS software, which can run on Windows, Mac or Linux environments, runs in over 40 languages, and includes vector, raster, georegistration, and other capabilities, all using ESRI shapefiles as the basic vector data structure and Geotiffs as the basic raster data structure. The first workshop will be a general introduction to the QGIS user interface and will explore the various elementary functions, loading vector and raster data, etc. Additional tutorials and data will be made available to the participants so you can continue to work on your own. Feel free to bring your own laptop so you can download the software, tutorials, and data, or use a computer in the lab.There is no fee for this course.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 247Date: October 24, 2016TImes: 10:00am - 12:00pmIntroduction to the QGIS Open Source Software: Part 2Scott MadryThe second class will continue the introduction to the QGIS software. We will explore various plugins, including live linking OpenStreetMaps and Bing maps and images, creating cartographic maps using the composer cartographic interface, working with vector attribute tables, and downloading and working with raster satellite imagery. Web resources will be explored. Additional tutorials and data will be made available to the participants so you can continue to work on your own. Feel free to bring your own laptop so you can download the software, tutorials, and data, or use a computer in the lab.AThere is no fee for this course.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 247Date: October 26, 2016Times: 10:00am - 12:00pmIntroduction to the GRASS Open Source GIS and image processing softwareScott MadryThis third 2-hour workshop will cover the GRASS GIS package, which is included in the QGIS download and can be used either as a set of integrated tools in the QGIS environment, or run as the stand-alone GRASS package. GRASS is the original open source GIS package, and is a very powerful and integrated GIS, image processing, spatial analysis, visualization and modeling environment. The first hour of the workshop will use GRASS within the QGIS environment, where data can be used as GRASS files in the same environment as QGIS shapefiles, and can be converted easily between the two. The current QGIS software can now call all of the GRASS functions (over 400) remotely and does raster and vector data format conversion on the fly. In the second hour we will use GRASS in its stand-alone configuration. Additional tutorials and data will be made available to the participants so you can continue to work on your own. Feel free to bring your own laptop so you can download the software, tutorials, and data, or use a computer in the lab.There is no fee for this course.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 247Date: October 28, 2016Times: 10:00am - 12:00pmApplied Spatial Regression AnalysisPaul VossThis short course provides an introduction to the field of spatial regression modeling. When analyzing data aggregated to geographic areas (e.g., census data for counties), a fresh set of issues arise that are not present in traditional non-spatial data analyses. These issues need to be recognized and accounted for when properly specifying regression models using attributes that are linked to geographic location. The topics covered in two afternoon sessions include:? Why standard regression models generally fail when analyzing spatial data? Defining and understanding “spatial autocorrelation”? Causes of spatial autocorrelation? Measuring & operationalizing spatial effects? Defining spatial “neighborhoods”? Creating spatial weights matrices? Moran’s I statistic? Incorporating spatial effects in spatial regression models? Specification & estimation of spatial regression models? Spatial regression model diagnostics? (Time permitting: some interesting extensions to related topics)Examples of estimating spatial regression models will use the open source software suite R (no prior knowledge of R is necessary)?Registration Fees:? UNC-CH Students - $40? All Others - $90?Registration will open 60 days prior to class.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.?Davis 219Dates: November 7 & 9, 2016Times: 1:30pm - 4:00pmSurvey ResearchIntroduction to Focus GroupsEmily GeisenFocus group interviews are commonly used for survey development, content development, and qualitative data collection to capture rich information about attitudes and beliefs that affect behavior. An overview of the basics of focus groups supplemented with real examples and hands-on practice will highlight the most appropriate uses of focus groups, moderating focus groups, developing interview questions, analyzing and using results, as well as reporting findings.Registration Fees:? CPSM Students - $30? UNC Students - $45? Other - $60?To Register, click?hereThis course will count as 7.0 CPSM short course credit hours.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.?Davis 219Date: September 8, 2016Times: 9:00am - 4:30pmDesigning Web SurveysMick CouperThe focus of this course is on the design of Web survey instruments. The course will focus on the appropriate choice and design of input tools (e.g., radio buttons, check boxes, drop boxes, text fields), including new features in HTML 5 and additional tools such as sliders. The course will also address layout and formatting of the instrument, including alignment of questions and response options, typeface, background color, and the design of grids or matrix questions. The design implications of browser-based mobile Web surveys will also be addressed. The course will draw on empirical results from experiments on alternative design approaches as well as on practical experience in the design and implementation of Web surveys. The course will not address the technical aspects of Web survey implementation (such as hardware, software, or programming) and will also not focus on question wording, sampling, or recruitment issues. The course will equip participants with the knowledge needed to make appropriate Web survey instrument design choices.The Instructor: Dr. Mick P. Couper, from the University of Michigan and the Joint Program in Survey Methodology, is the leading authority on web survey design in the U.S. He is the author of Designing Effective Web Surveys (Cambridge, 2008), and co-author (with Roger Tourangeau and Frederick Conrad) of The Science of Web Surveys (Oxford, 2013), and has done extensive research on web survey design and implementation.Registration Fees:? CPSM Students - $30? UNC Students - $45? Other - $60This course will count as 7.0 short course credit hours.Registration will open 60 days prior to class.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.?Davis 219Date: September 22, 2016Times: 9:00am - 4:30pmAn Overview of Topics in "Big Data": Unpacking Data Science for BeginnersCliff LampeThis course is a one-day introduction to “Big Data” as method of conducting research. The course will cover a range of issues, including: ? Characteristics of data that is collected through these techniques. For example, when is scale of data important, vs. the nonreactive nature of the data. ? Common methods for obtaining datasets for “Big Data” ? Epistemological approaches for using data, including the inductive nature of many data analytic techniques. ? Comparison of data analytic techniques with other forms of research. ? Exploration of a variety of tools that are commonly used in Big Data research. ? Common analytical techniques in data science. People who take this course will be able to define the pros and cons of data science as a research method, understand common terms related to Big Data techniques, and identify research questions that are appropriate to these techniques. It’s impossible to give a very technical training in a one day class, so while we’ll cover where one can go to learn more, this class will not delve deeply into technical aspects of big data. Given the nature of the instructor’s research, the class will focus on data mined from social media sites, which is one of the most common sources for data analytic approaches. Any person with a solid background in research methods will benefit from this course.?Instructor:Cliff Lampe is an Associate Professor in the School of Information at the University of Michigan. His work is on the effects of social media use by individuals, groups and organizations with a focus on positive outcomes. He publishes in the the fields of Human Computer Interaction, and Communication Science. In his research, Dr. Lampe has examined interaction on multiple social media platforms, and has frequently used “big data” techniques to study interactions on those platforms. With a background of research at the Institute of Social Research at Michigan, Dr. Lampe has also been recently collaborating on a series of projects that look at the comparison of data analytic techniques and survey measurement in terms of a variety of research goals.?This course will count as 7.0 CPSM short course credit hours.Registration Fees:? CPSM Students - $30? UNC Students - $45? Other - $60?Registration will open 60 days prior to the class date.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.?Davis 219Date: October 6, 2016Times: 9:00am - 4:30pmIntroduction to Cognitive InterviewingTeresa EdwardsThis course will provide an overview of cognitive interviewing as a technique for developing and/or testing survey questions. We will briefly discuss participant recruitment and other planning details before turning to development of an interview guide, discussion of think-aloud and probing techniques, selection of probes, trade-offs of concurrent vs. retrospective probing, and how to choose the best techniques for particular situations. We will use demonstrations and exercises to give participants experience using the technique.?Registration Fees:? CPSM Students - $20? UNC Students - $35? Other - $40?This course will count as 4.0 short course credit hours.Registration will open 60 days prior to class.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.?Davis 219Date: October 25, 2016Times: 9:00am - 1:00pmNew Technologies in SurveysMichael LinkRapid advancements in communications and database technologies are changing the societal landscape across which public opinion and survey researchers operate. In particular, the ways in which people both access and share information about attitudes, opinions, and behaviors have gone through perhaps a greater transformation in the last decade than in any previous point in history and this trend appears likely to continue. This course examines some of the research findings to date with respect to the use of mobile and social media platforms as vehicles for collecting information on attitudes, opinions and behaviors. For each area, we will explore current applications, known best practices, and cautions, including smartphones (for surveys, GPS, and visual data collection) and social network platforms (surveys and other forms of information). Examples will be provided from several topic areas, including assessment of political attitudes, health-related studies, and consumer research. The final section of the course delineates some of the more fruitful areas for on-going research to improve our understanding of these technologies and the role they can play in assessing public opinion.Registration Fees:? CPSM Students - $30? UNC Students - $45? Other - $60?This course will count as 7.0 CPSM short course credit hours.Registration will open 60 days prior to the class date.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.Davis 219Date: November 10, 2016Times: 9:00am - 4:30pmData Collection Using Mobile Phones in Developing Countries: New Approaches with SMS, IVR, and CATICharles LauThe rapid growth of mobile phones in developing countries opens up new possibilities for data collection. Short message service (SMS), interactive voice response (IVR), and computer-assisted telephone interviewing (CATI) can produce data faster and less expensively than face-to-face surveys. This course will introduce students to the design and implementation of SMS, IVR, and CATI surveys in low income countries. Dr. Lau will draw from real world examples to illustrate how these modes work. We will also discuss basic survey design principles in each mode, focusing on sampling and questionnaire design. Third, Dr. Lau will review issues related to implementation - e.g., quality assurance, monitoring sample, and cost. By the end of the course, students will understand each mode operates, as well as the advantages and disadvantages of each mode.There are no prerequisites for this course, but basic familiarity with survey research in developing countries is helpful.The Instructor: Charles Lau designs and implements surveys in low- and middle-income countries. He directs projects through the survey cycle, including study design, questionnaire development, sampling, interviewer training, data collection, analysis, and reporting. Dr. Lau has led surveys in 17 countries in Africa, Asia, and Latin America. In these countries, he has used different modes of data collection, including face-to-face interviewing with tablets, telephone, web, and short message service (SMS). With funding from governments, foundations, and commercial clients, his work has covered various topics including health, education, politics, and technology. He also publishes research on cross-cultural issues in survey design, interviewer and mode effects, and sampling approaches in developing countries. Dr. Lau joined RTI in 2010. He teaches International Survey Methods at North Carolina State University.This course will count as 2.0 CPSM short course credit hours.?Registration will open 60 days prior to the class date. There will be no registration fee but slots are limited.* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register prior to 3 days before the start of the course.?Davis 219Date: November 16, 2016Times: 1:00pm - 3:00pmWeighting Survey DataPaul BiemerThis course is an introduction to the basic concepts for weighting survey data. It begins by defining the goals of weighting including weighting as a correction for differential selection probabilities, non-response and non-coverage. The course covers the process of developing weights for stratified two-stage sampling including computing design weights and methods for nonresponse adjustments and frame coverage error adjustments. Additional topics may include the effect of weighting on variance of the estimates and extreme weights.INSTRUCTORPaul P. Biemer is Distinguished Fellow, Statistics, at RTI International and Associate Director for Survey Research and Development for the Odum Institute at the University of North Carolina at Chapel Hill. He received his Ph.D. in Statistics from Texas A&M University and has taught at the University of Maryland (Joint Program in Survey Methodology), University of Michigan (Summer Institute) and George Washington University (Statistics Department). He was formerly Head of the Department of Experimental Statistics and Director of the Statistics Center at New Mexico State University and has also worked for the Bureau of the Census where he was Assistant Director for Statistical Research. His research has examined the relationships between survey design and survey error, statistical methods for assessing survey errors, particularly measurement errors and methods for the analysis of survey data. His articles have been published in numerous scholarly journals. His book, Introduction to Survey Quality, and several edited volumes including Measurement Errors in Surveys, have been published by John Wiley & Sons.?Registration Fees:? CPSM Students - $20? UNC Students - $35? Other - $40?This course will count as 4.0 short course credit hours.Registration will open 60 days prior to class.* Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.?* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.?Davis 219Date: November 17, 2016Times: 9:00am - 1:00pmStatistical ComputingStataKelsey ShoubThis is a 3-part short course (held over three mornings). Stata part 1 will offer an introduction to Stata basics. Part 2 will teach entering data in Stata, working with Stata do files, and will show how to append, sort, and merge data sets. Part 3 will cover how to perform basic statistical procedures and regression models in Stata.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.*Link to Course Outline to be added later*Davis 219Dates: September 12, 14, and 16, 2016Times: 9:00am - 11:00amSASChris WiesenThis is a four-part course. SAS part 1 of 4 will give an introduction to the SAS system and SAS windows. Topics to be covered include: creating and saving SAS programs; reading in data from simple and complex text data sets; typing variables; obtaining frequencies, contents, and univariate statistics. SAS part 2 of 4 will discuss formatting variable values; creating SAS libraries for storing and retrieving SAS data sets and format files; reading raw data from external files; creating new SAS data sets from existing SAS data sets, subsetting by observation and by variable. SAS part 3 of 4 will explain how to create new SAS data sets combining information from multiple existing SAS datasets; how to sort, concatenate, interleave, and merge data sets; how to perform the t-test, and test for no association in a contingency table. For SAS part 4 of 4, attendants will be allowed to suggest topics. Past topics include variable retyping, creating SAS datasets from SAS output; creating html and Microsoft Word tables, ANOVA, importing and exporting Excel files.Students should bring a flashdrive to class.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.This class always fills so be sure to arrive before the class start time. There are only 21 seats with computers, but a limited number of those who have laptops with SAS loaded will be allowed to sit in.Davis 3010Dates: September 12 - 15, 2016Times: 11:00am - 1:00pmSPSSHeidi VuletichPart 1 of the course will offer an introduction to SPSS and teach how to work with data saved in SPSS format. Part 2 will demonstrate how to work with SPSS syntax, how to create your own SPSS data files, and how to convert data in other formats to SPSS. Part 3 will teach how to append and merge SPSS files, demonstrate basic analytical procedures, and show how to work with SPSS graphics. Please bring a flashdrive to class.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Click here for course handouts:?Handout 1?;?Handout 2?;Handout 3Davis 219Dates: September 26, 28, 30, 2016Times: 4:00pm - 5:30pmSASChris WiesenThis is a four-part course. SAS part 1 of 4 will give an introduction to the SAS system and SAS windows. Topics to be covered include: creating and saving SAS programs; reading in data from simple and complex text data sets; typing variables; obtaining frequencies, contents, and univariate statistics. SAS part 2 of 4 will discuss formatting variable values; creating SAS libraries for storing and retrieving SAS data sets and format files; reading raw data from external files; creating new SAS data sets from existing SAS data sets, subsetting by observation and by variable. SAS part 3 of 4 will explain how to create new SAS data sets combining information from multiple existing SAS datasets; how to sort, concatenate, interleave, and merge data sets; how to perform the t-test, and test for no association in a contingency table. For SAS part 4 of 4, attendants will be allowed to suggest topics. Past topics include variable retyping, creating SAS datasets from SAS output; creating html and Microsoft Word tables, ANOVA, importing and exporting Excel files.Students should bring a flashdrive to class.No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.This class always fills so be sure to arrive before the class start time. There are only 21 seats with computers, but a limited number of those who have laptops with SAS loaded will be allowed to sit in.Davis 3010Dates: October 31 - November 3, 2016Times: 3:00pm - 5:00pmOtherIntroduction to Census ConceptsMichele Matz HayslettDo you know that variables like income and educational attainment are no longer part of the decennial census? Do you understand the differences between the decennial long form methodology and that of the American Community Survey (ACS)? If your answer to these questions is no, this class is for you! We will compare and contrast content and methodology of the decennial census long form and the ACS, and review Census terminology and geographies. (This class or equivalent knowledge is required for the Basic and Advanced Access classes.)Registration will open 60 days prior to class.* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register prior to 3 days before the start of the course.?Davis 3010Date: September 20, 2016Times: 9:00am - 11:30amBasic Access to Census DataMichele Matz HayslettHands-on workshop to help users understand the strengths of various Census data retrieval tools, both freely available ones and those to which the library subscribes: American FactFinder, the Census Bureau’s freely available database; Social Explorer, a commercially licensed tool to which the library subscribes; and the grant-supported (so, free to you) National Historical Geographic Information System (NHGIS). These tools provide access to pre-constructed data tables published by the Census Bureau. Some are better for the most recent data and others are useful for historical data. Come learn how to choose the best tool for your research, and the ins and outs of each tool. (Prerequisite: Intro to Census Concepts or equivalent knowledge.)Registration will open 60 days prior to class.* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register prior to 3 days before the start of the course.?Davis 3010Date: September 20, 2016Times: 1:00pm - 4:00pmAdvanced Access to Census DataMichele Matz HayslettHands-on workshop to help users understand the strengths of various Census (and other survey) data retrieval tools which allow the creation of custom cross-tabulations (that is, custom data tables). Tools to be covered include: DataFerrett; iPUMS; TerraPopulus (in beta); and the Triangle Research Data Center (TRDC). The first three tools are freely available and we will focus on their census data content (U.S. for DataFerrett; U.S. and international for iPUMS and TerraPopulus); researchers must apply to the Census Bureau (or other federal agency, e.g., the Centers for Disease Control) for access to the TRDC in order to utilize survey microdata. (Prerequisite: Intro to Census Concepts or equivalent knowledge.)Registration will open 60 days prior to class.* Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register prior to 3 days before the start of the course.?Davis 3010Date: September 21, 2016Times: 9:00am - 12:00pmTableau IMatt JansenTableau is a user-friendly software application used to create static or interactive visualizations and dashboards. Examples can be found here:?'s drag-and-drop interface provides tools to build a variety of visualizations with no coding required, and visualizations can be embedded in websites by copying and pasting embed code. In this workshop, participants will create basic visualizations and an interactive dashboard in Tableau Public.Note: Participants should create an account at public. before the workshop in order to save the exercise dashboard to the web.? required.?Register hereDavis 247Date: October 5, 2016Times: 10:00am - 11:15amCollecting and Analyzing Textual DataKelsey ShoubThis two-day hands-on short course provides a brief introduction to quantitative text analysis and mining in the social sciences for those who have little to no experience with the topic. The first session will focus on the basics of the collecting and formatting the text, an overview of how to extract specific pieces of information, and how to process your documents. The second session will provide an introduction to supervised, semi-supervised, and unsupervised models used to classify or elicit information from the text. In this context, supervised means that the researcher provides some amount of information for an algorithm to be trained and then be used to make predictions or explanations. A basic working knowledge of R is necessary. For those wanting a refresher, see the online R course available on Odum's website:?Introduction to RNo registration required. UNC students, faculty, and staff will need to show their UNC OneCard.Davis 219Dates: October 11 & 13, 2016 (TENTATIVE)Times: 9:30am - 12:00pm13TH ANNUAL QUALITATIVE RESEARCH SUMMER INTENSIVEHosted by: ResearchTalk, Inc. in partnership with the Odum Institute at UNC?July 25 – 29, 2016 At the Carolina Inn in Chapel Hill, NCFor more information and to register, please go to:? DescriptionsCrafting Phenomenological Research: How Phenomena Can Take Shape in Various ContextsInstructor: Mark VagleDates: Monday-Tuesday, July 25-26Phenomenology is a way for qualitative researchers to look at what we usually look through. It means being profoundly present in our research encounters, to leave no stone unturned, to slow down in order to open up, to dwell with our surroundings, and to know that there is “never nothing going on.” Because the philosophical ideas that underpin phenomenology can be abstract and sometimes elusive, this course will communicate these topics as concretely as possible. That is, the course will provide techniques, tools, and strategies for cultivating a phenomenology. We will use examples, anecdotes, and exercises to work through and navigate the craft.To learn about phenomenological research approaches, we will experience a series of data collection tools and strategies such as going on “phenomenology walks,” writing about lived experiences, and interviewing one another. We will explore Vagle’s five-component methodological process for conducting post-intentional phenomenological research—working to make sense of how our phenomena might take shape in various contexts:Identify a phenomenon in its multiple, partial, and varied contexts.Devise a clear, yet flexible process for gathering data appropriate for the phenomenon under investigation.Make a post–reflexivity plan.Read and write your way through your data in a systematic, responsive manner.Craft a text that captures tentative manifestations of the phenomenon in its multiple, partial, and varied contexts.Finally, we will explore conventional and less-conventional ways to write up our research.A wide variety of methodological and philosophical texts and examples of phenomenological studies will be on hand for participants to read and discuss during the course. The course is based on Vagle’s book by the same name, Crafting Phenomenological Research (Left Coast Press, 2014).Fundamentals of Qualitative ResearchInstructor: Johnny Salda?aDates: Monday-Tuesday, July 25-26“Fundamentals of Qualitative Research” is an intensive two-day introductory overview of basic approaches to and methods for qualitative inquiry. Course content will be adapted from Salda?a’s textbook, Fundamentals of Qualitative Research (Oxford University Press, 2011).Major topics addressed will include: (1) genres, elements, and styles of qualitative research; (2) a survey of qualitative data collection methods; (3) qualitative research design; (4) a survey of qualitative data analytic methods; and (5) writing and presenting qualitative research. Multiple practical and on-your-feet activities will be included throughout the course to provide students experiential knowledge of the subject.Novices to qualitative inquiry will benefit from this course by gaining literacy and workshop experience in the basic methods of qualitative research for future study and application.Experienced qualitative researchers may benefit from this course by refreshing their knowledge bases of methods, plus observing how introductory material is approached with novices for future classroom teaching applications.?Implementation Research: Using Qualitative Research Methods to Improve Policy and PracticeInstructor: Alison HamiltonDates: Monday-Tuesday, July 25-26Implementation research aims to integrate research findings into practice and policy. In order to improve the quality and effectiveness of routine practice, implementation researchers collect qualitative data about the everyday behaviors and beliefs of practitioners and other professionals, stakeholders, and recipients of services. During data collection, special attention is paid to factors that both facilitate and impede effective execution and implementation of major programs and service delivery. The end goal is to increase the likelihood of uptake, adoption, implementation, and sustainability of evidence-based practices.To provide foundational knowledge and skill to help facilitate your own work, the course walks through critical components of building and carrying out an implementation research project:Developing appropriate implementation research questions and specific aimsSelecting conceptual modelsStrategizing about study designDetermining appropriate, feasible qualitative data collection methodsExecuting qualitative analytic strategiesGenerating timely, impactful implementation research productsThe application of methodological concepts will be illustrated via examples from implementation research in the context of varied settings such as healthcare organizations, educational institutions, and communities.Participants will be provided with materials and bibliographies to support the practice of qualitative methods in implementation research. ??“Sort and Sift, Think and Shift”: Learning to Let the Data Guide Your AnalysisInstructor: Ray MaiettaDates: Monday-Tuesday, July 25-26The Sort and Sift approach is an iterative process where analysts dive into data to understand its content, dimensions and properties, and then step back to assess what they have learned and to determine next steps. This process of “diving in” and “stepping back” is repeated throughout the analytic process. Researchers move from establishing an understanding of what is in the data to exploring their relationship to the data. To conclude, they arrive at an evidence-based meeting point that is a hybrid story of data content and researcher knowledge.The Sort and Sift approach is defined by two key analytic shifts qualitative analysts must make over the course of their data work. Shift 1 occurs when analysts move their analytic plans from being driven by what they knew and thought before they collected and engaged with data to allowing data content to define analytic decision-making and directions. Shift 2 occurs as analysts move from processing individual data documents to giving careful thought and attention to what they will present and how this material will be presented to audiences.This course focuses on a toolkit used during initial phases of data collection. This phase is driven by careful and thoughtful attention to core data segments within each data collection episode and the construction and analysis of episode profiles. Episode profiles feature the use of document inventories, diagrams and memos that work together to provide a detailed picture of the learning opportunities that arise within each individual data collection piece. ??Writing Effective Qualitative Mixed Methods ProposalsInstructor: Margarete SandelowskiDates: Monday-Tuesday, July 25-26The focus of this course is on concrete, this-is-how-you-might/should-say-it strategies for designing and writing competitive qualitative and mixed-methods research proposals. Qualitative and mixed-methods research proposals are exercises in artful and mindful design, verbal precision, imaginative and informed rehearsal, elegant expression, and strategic disarmament. We will cover principles generic to proposals, and specific ways to communicate the significance, conceptual framing, methodological details (sampling and data collection and analysis plans, plans for optimizing validity and human subjects protections) of, and budget and budget justification for, the planned study. We will also cover strategies for addressing those aspects of qualitative and mixed-methods research designs likely to arouse the most concern among reviewers less familiar with them, most notably the purposeful sampling frame and generalizability of study findings. This course is appropriate for graduate students and faculty in the practice disciplines (e.g., clinical psychology, education, medicine, nursing, public health, social work) as well as researchers from other fields of study (e.g., sociology, anthropology).In addition to didactic instruction, handouts, and a suggested reference list, the course will include an interactive session where participants will have the opportunity, as time permits, to ask questions about their own proposals for problem solving.?Writing Rites: Working on Your Analysis and WritingInstructor: Kathy CharmazDates: Monday-Tuesday, July 25-26What makes one qualitative study much more compelling than others? How can the writing strategies of professional writers help us improve our work? How do you manage to write when you work in a setting that allows scant time for writing? Would you like to expedite analyzing your data and writing your report? Which strategies help you gain acceptance and admiration from your intended audiences? This class addresses these questions.Qualitative reportage relies on art and science. Learning how to construct an artful rendering of your work increases the power of your analysis. This class covers both professional writers’ tips and tricks and qualitative analysts’ strategies and shortcuts. It will help you develop a more incisive, creative, and clear narrative. Our approach emphasizes how to construct a creative analysis and to write it for varied audiences. You will gain fresh ideas for proceeding with the analysis, integrating your ideas into a cogent, coherent piece of work, and communicating the significance of your work.??This class covers crafting research stories and writing analytic reports, but the two are not separate endeavors. Thus we show how to bring analytic definition and logic to stories and to build imagery, rhythm, metaphor, and surprise into analytic reports. We also cover strategies for developing arguments, writing literature reviews and theoretical frameworks and integrating your manuscripts. Writing abstracts, titles, and introductions share problems and pitfalls. Our agenda includes learning a few tricks to help you resolve these problems and avoid the pitfalls. The last session focuses on choosing journals and publishing houses, preparing your manuscript for submission, and working with editors and reviewers.This class best serves participants who are in the midst of a qualitative project or have had some experience with qualitative research and have engaged in qualitative coding and memo writing. Writers of all types of qualitative research are welcome. Researchers who conduct ethnographies, use discourse analysis, engage in narrative inquiry, follow grounded theory strategies, or create personal narratives will all gain ideas and strategies to advance their work.?Analyzing Online ConversationsInstructor: Trena PaulusDate: Wednesday, July 27This course introduces participants to a research framework to guide the process of analyzing online conversations. From social media to online support groups to massive open online courses (MOOCs), conversations on the Internet have long been of interest to qualitative researchers in a variety of fields who seek to identify the insights and transformations that take place there. While much of the early studies in this area have relied on content analysis methods, this course will familiarize participants with a variety of qualitative methods for analyzing online conversations.Participants will learn how to:Identify underlying theoretical assumptions that impact research designGenerate and/or capture relevant online conversational data in an ethical mannerSelect data analysis methods that ensure methodological alignmentThis framework will assist researchers in creating conceptually congruent research designs to answer important questions about what is happening in online ics will include:Understanding the distinction between language as representation vs. language as constructionEnsuring methodological alignment when designing studies of online conversationsCrafting a taxonomy of online conversations and typical analytic approachesGenerating new online conversations vs. capturing existing online conversationsResolving ethical dilemmas surrounding the analysis of online conversationsSelecting appropriate technologies for working with online conversationsAnalyzing online conversations using thematic, narrative and discursive techniquesEstablishing the quality of the findings?Building a Codebook and Writing MemosInstructor: Paul MihasDate: Wednesday, July 27This course focuses on developing codes and integrating memo writing into a larger analytic process. Coding and memo writing function as simultaneous and fluid tasks that occur during actively reviewing of interviews, focus groups, and multi-media data. We will discuss deductive and inductive codes and how a codebook can evolve, that is, how codes can emerge and shift unexpectedly during analysis. Managing codes also includes developing code connections and possible hierarchies, identifying code “constellations,” and building multidimensional themes. Our discussion of codes will include the following topics:?The importance of code names and definitionsDeductive, inductive, and thematic codesHow many codes are too many?How broad or specific should codes be?Memos function as deep reflections that capture nuanced thoughts and cumulative reactions to data. Memo writing strategies help us capture analytical thinking, inscribed meaning, and cumulative evidence for emerging meaning. Memos can also resemble early writing for reports, articles, chapters, and other forms of presentation. Researchers can also mine memos for codes and incorporate memos in building evocative themes and theory. The following types of memos and memo-writing will be discussed in an effort to offer strategies to begin applying these techniques to your own work: holistic memos, positionality memos, statement memos, thematic memos, and memos that engage critical data segments.?Creating Credible, Vivid, and Persuasive Qualitative Stories: Research As PerformanceInstructor: Johnny Salda?aDate: Wednesday, July 27An arts-based approach can enrich our understanding of how people experience their worlds. When the audiences of our research hear poems and see plays that portray the themes and meanings in our data, they witness the power of nuance and the integrated nature of qualitative findings. Our audiences become more present in our story telling and are more likely to absorb the multi-dimensional messages we convey.?Johnny Salda?a, one of the best known practitioners of this research tradition, will guide participants through improvisational and writing exercises to explore how dramatic texts add credibility and make presentations more vivid and persuasive. These skills will help researchers document and represent fieldwork ranging from education to health care.The course will also provide a literature review of exemplary play scripts and videos in research-based theatre; methods of dramatizing field notes and adapting interview transcripts; and the developmental process of autoethnographic monologues. Throughout, Salda?a emphasizes the vital importance of creating good theatre as well as good research for impact on an audience and performers.Key figures in qualitative inquiry, Norman Denzin and Yvonna Lincoln, endorse the arts-based research techniques outlined and supported in this course as a powerful way for ethnographers to interrogate and represent the meanings of lived experiences.No prior theatre or performance experience is needed to participate in this workshop.?Designing, Using, and Evolving Qualitative Interview and Focus Group GuidesInstructor: Alison HamiltonDate: Wednesday, July 27Interview/focus group guides are tools for prompting people to share their stories and perspectives on particular topics. This course will position you to develop an active posture toward initial development of an interview/focus group guide and prepare you to engage actively and evolve the fit of your guide to what you experience and learn in the field.The eight strategies listed here will serve as an action plan to accomplish this goal:AligningPreparingOpeningAskingFollowingShifting/adjustingClosingProcessingAligning: What is the overall point of the interview or focus group? How do the questions in your interview/focus group guide assist you in achieving project goals?Preparing: Who are your participants? How does knowledge of the participants inform questioning format and approach? How do you ensure (e.g., through pilot testing, think-aloud methods) that the questions you develop are relevant and aligned with project goals? How do you foster a sense of ownership for participants in the data collection experience??Opening: What are ways to open the conversation appropriately and comfortably?Asking: What do you ask participants when and why? What questions open conversation topics? When and how do you probe and ask for further detail and example?Following: How do you manage the conversation in a way that allows you to follow your participants’ unfolding narratives while keeping them interested and involved in their own story telling?Shifting/adjusting: When and why do you make adjustments to the interview or focus group? How can you shift your approach, language, and direction on the spot as you listen to people’s unfolding narratives?Closing: How can you naturally and affirmatively reach the conclusion of the data collection episode?Processing: How do you track and understand the evolution of your interview/focus group guide and process the meanings these changes have for your project?Employing these strategies through the life of your project will enhance the quality of the data you collect. This practice will also help you to understand how the conversations occurring during data collection fit what is currently known about, and practiced in, your field.?Foundational Principles of and Approaches to Mixed Methods ResearchInstructor: Margarete SandelowskiDate: Wednesday, July 27The focus of this course is on primary mixed-methods studies and programs of research. We will cover misconceptions about mixed methods research, key points of interface between “qualitative” and “quantitative” methods and data, and the problems posed by the qualitative/quantitative binary foundational to the “mix” in mixed methods.Also covered will be issues concerning and techniques for combining:Purposeful and probability sampling framesMinimally structured and open-ended and highly structured and closed-ended data collection approachesTextual and statistical analysis strategiesApproaches for the integration of diverse data sets, including linking and assimilation techniquesThis course is appropriate for graduate students and faculty in the practice disciplines (e.g., clinical psychology, education, medicine, nursing, public health, social work) as well as researchers from other fields of study (e.g., sociology, anthropology).?Learning from Lived Experiences: How We Can Study the World As It Is LivedInstructor: Mark VagleDate: Wednesday, July 27This workshop will explore what “lived experience” means for qualitative researchers and how we can study the world as it is lived, not the world as it is measured, transformed, represented, correlated, and broken down. In paying close attention to lived experience, we are interested in the felt and sensed aspects of our participants’ and our own experiences, as well as the contextual aspects in which these experiences are lived. How can we listen to and make sense of this significance and use it in our qualitative research?We will identify lived experiences that we are interested in studying and use theoretical tools from phenomenological traditions to explore how we can open up, wonder about, and understand these experiences more deeply. We will treat theorizing as an active and generative process of exploration.We will also put these theoretical tools to use in our data collection processes—focusing on observing and interviewing lived experiences. As a concrete example, we will spend time exploring how various visual and popular media can serve as data for studying lived experience. With data from some of Vagle’s current studies of social class lived experiences in schools and communities, we will further practice data analysis using the theoretical tools we have learned. Participants are also encouraged to bring their own data and/or research ideas so they can apply these tools and techniques to their work.?Coding and Analyzing Qualitative DataInstructor: Johnny Salda?aDates: Thursday-Friday, July 28-29This two-day workshop focuses on a range of selected methods of coding qualitative data for analytic outcomes that includes patterns, categories, themes, processes, and causation. The course will also touch upon how these methods fit with or differ from coding strategies in grounded theory and phenomenology.The workshop will address:Various coding methods for qualitative data (interview transcripts, field notes, documents)Analytic memo and vignette writingHeuristics for thinking qualitatively and analyticallyManual (hard copy) coding will be emphasized with a discussion of available analytic software for future use. Workshop content is derived from Salda?a’s The Coding Manual for Qualitative Researchers (3rd ed., Sage Publications, 2016).Designing and Making Decisions in Qualitative Data Collection ProjectsInstructor: Mark VagleDates: Thursday-Friday, July 28-29At first blush, decisions regarding data collection can seem straightforward and clear. In this workshop, we will consider how data collection is more complicated and dynamic than it may first appear. We will begin by cultivating a set of principles that can direct a comprehensive approach to designing and carrying out qualitative data collection projects.Four core data collection principles:Successful data collection depends on how we as researchers employ a posture of openness, flexibility, and responsiveness in our data collection practices.The phenomenon (case, problem, context, etc.) possesses properties, dimensions, and dynamics that we must become aware of as a first step to directing decision making that will continue throughout the data collection process. In other words, the phenomenon calls for how it should be studied.Considerations of ethical, political, and social implications related to our study, our participants, and the communities in which our study is located must guide and direct our practices. Paying attention to these issues from the onset encourages a posture of inclusiveness and avoids potential obstacles that arise from a disconnect with study participants.Researcher reflexivity throughout the data collection process helps us distinguish what is purely in and directed by the data and how our attitudes and behaviors, intentionally or not, may direct our practices.Using these principles, we will work through a number of ways to talk to people (interviews, focus groups, informal conversations); observe people and places (structured, semi-structured, unstructured, short-term, long-term, participatory); and examine artifacts (content analysis, policy analysis, discourse analysis). In addition, we will consider how forms of visual art, film, popular media, historical documents, poetry, and theory can be used as important forms of data collection in qualitative research.?Digital Tools for Qualitative ResearchInstructor: Trena PaulusDates: Thursday-Friday, July 28-29This course introduces participants to how both free and proprietary technologies can be used to support the entire qualitative research process, including: engaging in reflexivity, networking through social media, collaborating with colleagues, conducting paperless literature reviews, collecting data through mobile apps and from social media, creating a “hands-free” transcribing process, analyzing text and multi-media data, and writing through storyboarding.?While most researchers know about and understand the benefits of qualitative data analysis software (QDAS) packages such as ATLAS.ti, NVivo and MAXQDA, fewer have thought about the ways new digital tools may impact every aspect of our work as researchers. Not only will participants gain a comprehensive introduction to the most recent digital tool developments as they apply to qualitative research, but, through detailed demonstrations by the instructor, they will also learn how to analyze critically the affordances and constraints of such tools and the ethical implications of their use.?Topics and tools will include:Networking and collaborating through academic social media platforms (Academia.edu, Google Scholar profiles, and ResearchGate)Developing a paperless literature review process using cloud storage (Dropbox), citation management software (Mendeley), annotating apps (GoodReader), and QDAS tools (ATLAS.ti and NVivo)Collecting data through mobile apps (ATLAS.ti, Evernote) and social media sites (QSR NVivo’s NCapture tool)Engaging in reflexivity and collaborating with colleagues and participants through cloud-based notebooks (Evernote) and blogging platforms (Blogger and Feedly)Transcribing in ways that synchronize the media file with the text (Inqscribe) and enable “hands-free” transcription (Dragon Dictate)Selecting an appropriate qualitative data analysis software package (e.g. deDoose, ATLAS.ti, MAXQDA, NVivo, Quirkos)Analyzing multi-media data (Transana)Writing long documents through storyboarding (Scrivener)The purpose of the workshop is to provide a comprehensive demonstration, rather than a tutorial, of how these digital tools can support efficient, effective, and theoretically-grounded methodological work.Introductions to Grounded Theory: A Social Constructionist ApproachInstructor: Kathy CharmazDates: Thursday-Friday, July 28-29This class introduces grounded theory methods from a social constructionist approach to new and experienced qualitative researchers. You will gain practical guidelines for handling data analysis, a deeper understanding of the logic of grounded theory, and strategies for increasing the theoretical power and reach of your work. I treat grounded theory as a set of flexible guidelines to adopt, alter, and fit particular research problems, not to apply mechanically. With these guidelines, you expedite and systematize your research. Moreover, using grounded theory sparks fresh ideas about your data. The sessions cover an overview of basic guidelines and hands-on exercises. I offer ideas about data gathering and recording to help you obtain nuanced, rich data. We discuss relationships between qualitative coding, developing analytic categories and generating theory and attend to specific grounded theory strategies of coding, memo-writing, theoretical sampling, and using comparative methods. You will receive guided practice in using each analytic step of the grounded theory method.If you have collected some qualitative data, do bring a completed interview, set of fieldnotes, or document to analyze. If you do not have data yet, we will supply qualitative data for you. If you prefer to use a laptop for writing, bring one, but you can complete the exercises without a computer.?Mixed Methods: Bridging Qualitative and Quantitative Methods and ResultsInstructor: Alison HamiltonDates: Thursday-Friday, July 28-29A researcher or research team pursues a mixed methods approach to understand a given topic or phenomenon more deeply when numbers or narratives alone do not provide a complete picture. Combining qualitative and quantitative approaches can enhance conversations about theory and/or inform the evolution of practice and policy. This complex and demanding research paradigm requires knowledge, skill, and expertise in quantitative and qualitative methods, as well as the art of carefully integrating the approaches to and findings from each mode of inquiry.This course focuses on strategies, tips, and best practices to accomplish this integration in accessible and effective ways, including:--Rationales to guide decision making related to study design and execution. For example:?Will the qualitative and quantitative data collection efforts occur concurrently or sequentially, and why?Will either the qualitative or quantitative be privileged or will each contribute equally to answering the research questions and generating the project’s final products?How much time will be allocated to integration and/or subsequent data collection phases? (What factors will contribute to the timing of the integration and phasing of data collection?)What expertise and resources are needed?What are the priority end products and how does the integrated analytic plan lead to those products?--Conceptual, theoretical, and/or logic models as roadmaps to set the stage for and guide integration.??--Analytic strategies that advance frameworks and processes of connecting, building, merging, embedding, and bridging. For example:The power and role of using data displays and visual diagramming during the analytic process, e.g., side-by-side comparisons, integrated matrices, joint displays.--Qualities of good reporting and attributes of good mixed methods articles.?Shifting Analytic Direction with Final Products in Mind: Using the “Sort and Sift” ToolkitInstructor: Ray Maietta and Kevin SwartoutDates: Thursday-Friday, July 28-29In every qualitative project there is a point where analysts move from processing individual data documents to giving careful thought and attention to what content they will present and how this material will be presented to audiences. Phase 2 of ResearchTalk’s Sort and Sift, Think and Shift analysis process is driven by a toolkit that directs this important shift in the analysis process. This toolkit can be used regardless of whether or not you employed earlier phase Sort and Sift techniques.A first beneficial step in this transition involves mining through memos, code topics, document summaries and episode profiles. As researchers review their analytic work, memoing and diagramming techniques help them discover, understand and document important connections within and across data documents.A second component of this transition features a question and answer procedure we call “bridging.” Two bridging tools we highlight are a “story evolution tool” and a “code combination tool.” The story evolution tool introduces a process of interrogating data to understand better how key actors, places, time periods, actions, attitudes and emotions interact in the lives of our participants. The code combination tool introduces a set of techniques to discern shared meaning between and among code categories.?For more information and to register, please go to:? PROGRAM IN SURVEY METHODOLOGYThe Odum Institute’s Certificate Program in Survey Methodology gives graduate students and working professionals the knowledge and skills they need to design and conduct surveys and analyze and report survey results. It introduces students to the vast and growing literature on survey research methods and provides training to pursue employment in a variety of fields where survey research plays an important roleThe program provides well-balanced training at the graduate-level in:survey samplingsurvey computing and data analysisquestionnaire designdata collection methodsproblem solving skills for complex surveysreal-world, hands-on experienceThe Certificate is instantly recognized and highly regarded by employers and can be obtained as a companion to a graduate degree or a supplement to a bachelor's degree.PROGRAM OVERVIEWA Certificate in Survey Methodology is 17-hour graduate program which includes coursework in sampling, questionnaire design, data collection methods, and computing, as well as hands-on experience working as a survey specialist in a local survey data collection facility. The Program is designed for individuals currently enrolled in a graduate degree program at UNC, and for individuals who have completed a bachelor's degree, are currently working, and may have no immediate plans to pursue a graduate degree.The Certificate is an attractive addition to a graduate degree in:social sciencesbusinessbiostatisticseducationstatisticspublic administrationjournalismepidemiologymany other fields which use survey dataStudents may complete the course requirements at their own pace; no maximum time limit is applied. However, the minimum time to complete the Certificate is two academic years, because two of the required courses (Survey Sampling and Case Studies in Survey Research) are only offered in alternate years.Program RequirementsThe Certificate Program requires:Completion of 17 credit hours towards the certificate:Required courses -- 14 credit hours, including a practicum in a survey data collection facilityElectives -- 3 credit hoursCompletion of 32 short course credit hours in survey methodology from:?????????? - Odum Institute;?????????? - Joint Program in Survey Methodology at the University of Maryland;?????????? - Survey Research Center at the University of Michigan; or?????????? - Other approved short courses in survey methodologyAPPLICATIONPrerequisites for AdmissionBaccalaureate degree from an accredited undergraduate institutionGPA of 3.0 or betterAt least 6 credit hours in quantitative social science, applied statistics, biostatistics, or mathematicsApplication InstructionsPlease download the attached Application form, which you can find?here?(pdf) or?here?(MSWord).Fill the form out completely.Then either:Mail the application form to: Paul Biemer, Odum Institute for Research in Social Science, University of North Carolina, CB #3355, 228 Davis Library, Chapel Hill, NC 27599-3355ORE-mail the application form to:?Paul BiemerORFax, (919)962-2875.Application DeadlinesStudents wishing to be admitted to the CPSM may submit their application at any time. However, if you wish to enroll for the Fall semester, your application must be received by Odum before July 10th of the same year. For enrollment in the Spring semester, your application must be received by November 10th of the prior year.?? **Please note: Applying to the program by these deadlines does not guarantee a spot in the classes.? Students must register for courses?and space is subject to availability.Before registering for semester courses, if you are not already enrolled at UNC, you will need to submit an application to the Friday Center for Continuing Education. Click?here?to view the online application form, instructions, and deadlines.Note that Continuing Education applications received by the Friday Center after the deadline will incur a late fee of $25 added to the $80 application feeVisit the?Friday Center?for more information concerning Part-time Classroom Studies.COURSE LISTBelow is a list of the required and elective courses for the CPSM. Descriptions of all short courses are also available in the Short Course Catalog.Required Semester Courses and ScheduleBelow is a list of the required courses for the Certificate Program in Survey Methodology. If you have questions, please contact?cpsm@unc.edu.(Spring) BIOS 664 (3 credits)-or-(Spring - Even Numbered Years) SOCI 754 (4 credits)Sample Survey Methodology?(Denise Esserman)-or-Survey Sampling?(Christopher Weisen)(Fall - Every Year) SOCI 760 (3 credits)Data Collection Methods in Survey Research?(Doug Currivan)(Fall - Every Year) SOCI 761 (3 credits)Questionnaire Design?(Emilia Peytcheva and Emily Geisen)(Spring - Odd Numbered Years) SOCI 762 (3 credits)Case Studies in Survey Research?(Doug Currivan)(Fall & Spring) SOCI 905 (1 credit)Practicum in Survey Data Collection?(Teresa Edwards)Elective CoursesThese electives are pre-approved. Students may propose alternative courses to the director for elective course credit.ANTH 725Quantitative Methods in AnthropologyBIOS 545Principles of Experimental AnalysisBIOS 665Analysis of Categorical DataBIOS 667Applied Longitudinal Data AnalysisBIOS 668Design of Public Health StudiesBIOS 670Demographic Technique IBIOS 764Advanced Survey MethodsBUSI 808Applied Research Methods IBUSI 887Quantitative Methods in FinanceCOMM 410Introduction to Quantitative ResearchEDUC 680Introduction to Educational ResearchEDUC 785Program Evaluation in EducationEDUC 801Fundamentals of Education ResearchEDUC 981Field Techniques in Educational ResearchEPID 726Epidemiologic Research MethodsHBHE 852Scale Development MethodsHBHE 860Research Proposal DevelopmentINSL 509Information RetrievalMHCH 713Research Methods in Maternal and Child HealthMHCH 714MCH Program, Planning and EvaluationPLCY 801Design of Policy Oriented ResearchPOLI 880Design & Analysis of Experiments and SurveysSOCI 707Measurement and Data CollectionCurrent Short Course Schedule[redirect to upcoming short courses]FACULTY & INSTRUCTORSPaul Biemerleft000Director for the Certificate Program in Survey MethodologyAssociate Director for Survey Research, Odum InstitutePaul Biemer holds a joint appointment with the Odum Institute and RTI International, where he is a Distinguished Fellow. He also holds adjunct faculty appointments in the University of Maryland Joint Program for Survey Research and in the University of Michigan Survey Research Center. Biemer has more than 35 years of experience in survey methods and statistics. He specializes in evaluating survey quality and is a leading expert on statistical modeling, analysis, and interpretation of survey results. Biemer has a Ph.D. in statistics from Texas A&M University. His research interests include: Measurement error in surveys; nonsampling error modeling and estimation; general survey methodology and statistical methods. Biemer teaches several short courses for the program including: Introduction to Survey Quality, An Overview of Methods for Evaluating Survey Error, and Techniques for Modeling Survey Measurement Error.Teresa Edwardsleft000Odum Instructor and Assistant Director for Survey Research, Odum InstituteTeresa Edwards teaches the CPSM?Practicum in Survey Data Collection?and provides survey design consultation to UNC faculty and students. She oversees the Institute's web survey services and collaborates with and/or manages projects conducted by the Institute. Before joining the Odum Institute in 2003, Edwards was a survey methodologist at RTI International for 14 years, where she specialized in the design and implementation of computer-assisted interviewing systems. She holds an M.A. in Applied Social Research from the University of Michigan. Edwards teaches several CPSM short courses including Designing Self-Administered Questionnaires, Response Rate Issues in Telephone Surveys, and Introduction to Cognitive Interviewing.Doug Currivanleft000Odum Instructor and Survey Methodologist, RTI InternationalDoug Currivan, a survey methodologist with RTI International, has survey research experience that covers telephone, mail, in-person, and mixed-mode data collection projects for government, academic, and not-for-profit clients. Dr. Currivan's primary area of specialization is in telephone surveys, and his research interests include nonresponse bias in RDD surveys, improvement of survey measures through T-ACASI (telephone audio computer-assisted self-interviewing) methodology, and the impact of sample design on data collection and survey estimates. He taught a graduate-level course in survey research methods for four years at the University of Massachusetts and teaches courses titled?Data Collection Methods in Survey Research?and?Case Studies in Survey Research?at the Odum Institute of the University of North Carolina. He has given numerous professional presentations on survey methods and currently has journal articles under review on the effects of using T-ACASI methods and currently has journal articles under review on the effects of using T-ACASI methods to obtain smoking estimates among teens and the impact of declining response rates on estimates from adult tobacco surveys.Emilia Peytchevaleft000Odum Instructor and Research Survey Methodologist, RTI InternationalEmilia Peytcheva, is a research survey methodologist with RTI International.? She holds a PhD. in survey methodology from the University of Michigan.? Peytcheva's research expertise includes measurement error-inducing factors in cross-cultural research and the interplay among survey errors and their combined effect on total survey error.? Her interests include methods for minimizing measurement error induced by the survey questionnaire.? Peytcheva is also one of the instructors for the required CPSM course titled?Questionnaire Design.Chris Wiesenleft000Odum Instructor and Statistical Consultant, Odum InstituteChris Wiesen earned a M.S.Ed at the University of Pennsylvania (1988) and an M.A. (1992) and a Ph.D. (1994) at UNC. Before coming to the Odum Institute, Wiesen spent one year with the National Institute of Statistical Sciences, two years visiting Duke University and three years at Research Triangle Institute (now Research Triangle International). Along with offering consulting services to graduate students and faculty in the UNC system, he teaches short courses on various software packages including SAS and SUDANN and topics on quantitative anaylsis. Wiesen teaches? the required CPSM course: ?Survey Sampling.Mick Couperleft000Adjunct Odum Instructor and?Research Professor, University of Michigan and Joint Program in Survey Methodology, University of MarylandMick Couper received a Ph.D. in sociology from Rhodes University, an M.A. in applied social research from the University of Michigan and an M.Soc.Sc. from the University of Cape Town. His current research interests include survey nonresponse, design and implementation of survey data collection, effects of technology on the survey process, and computer-assisted survey methods, including both interviewer-administered (CATI and CAPI) and self-administered (web, audio-CASI, etc.) methods. Couper teaches a short course for the Certificate Program in Survey Methodology titled "Designing Web Surveys."Don Dillmanleft000Adjunct Odum Instructor and Professor in the Department of Sociology and Deputy Director for Research in the Social and Economic Sciences Research Center,Washington State UniversityDon A. Dillman is Regents Professor in the Department of Sociology and Deputy Director for Research in the Social and Economic Sciences Research Center at Washington State University, where he has been a faculty member since 1969. His 1978 book,?Mail and Telephone Surveys, was the first to provide step-by-step procedures for conducting such surveys, and is now in its third edition as?Internet, Mail and Mixed-Mode Surveys: The Tailored Design Method?(Wiley, 2009). His current research emphasizes how the visual design and layout of questionnaires influences respondent answers. In addition he is studying the use of addressed-based sampling to survey the general public using mail and the Internet and the integrated use of mail and email contact to improve web survey response. The course he teaches for the Odum Institute is “Visual Layout of Questionnaires and the Consequences for Data Quality.”Lisa Pearceleft000Adjunct Odum Instructor and Associate Professor in the Department of Sociology, University of North Carolina at Chapel HillLisa Pearce is an Associate Professor of Sociology and a Faculty Fellow at the Carolina Population Center here at UNC. Pearce studies intersections of religion and family life, especially during adolescence and the transition to adulthood in the U.S. and Nepal. Her work often takes a mixed methods approach combining survey research with semi-structured interviews or focus group interviews. Pearce received her Ph.D. in Sociology and Demography from Penn State University, and completed a postdoctoral traineeship at the University of Michigan’s Population Studies Center. She teaches the required graduate research methods course in UNC’s Department of Sociology, offers one-day workshops on mixed methods research through the Odum Institute, and teaches a two-week course on combining the use of qualitative and quantitative data at University of Michigan’s Summer Institute in Survey Research Techniques. Together with William G. Axinn, she co-authored?Mixed Method Data Collection Strategies(Cambridge 2006).Andy Peytchevleft000Adjunct Odum Instructor and Survey Methodologist, RTI InternationalAndy Peytchev holds an MS in Survey Research and Methodology from the University of Nebraska-Lincoln and a PhD in Survey Methodology from the University of Michigan. His main interests are in nonresponse measurement, reduction, and adjustment, causal understanding of the relationship between survey errors, and designing studies that minimize total survey error in estimates. He has experience in multiframe and multiphase study designs, weighting, and imputation. He serves as associate editor for Survey Practice. He is a member of the panel on the redesign of the Consumer Expenditure surveys organized by the Committee on National Statistics at the National Academy of Sciences and participates in expert review meetings that aim to help government agencies conducting surveys. Peytchev teaches a short course for the Certificate Program in Survey Methodology titled "Nonresponse Analysis."Trivellore Raghunathanleft000Adjunct Odum Instructor and Professor of Biostatistics, School of Public Health, University of MichiganTrivellore Raghunathan is a Professor of Biostatistics, Chair, Department of Biostatistics, School of Public Health and Research Professor, Survey Research Center, Institute for Social Research. He also teaches in the Joint Program in Survey Methodology at the University of Maryland. He is the Director of Biostatistics Collaborative and Methodology Research Core, a research unit designed to foster collaborative and methodological research with the researchers in other departments in the School of Public Health and other allied schools. He is an Associate Director of the Center for Research on Ethnicity, Culture and Health. He is a faculty member at the Center of Social Epidemiology and Population Health (CSEPH). He is also affiliated with the University of Michigan Transportation Research Institute (UMTRI). He received his Ph.D. in Statistics from Harvard University in 1987. Before joining the University of Michigan in 1994, he was on the faculty in the Department of Biostatistics at the University of Washington. He continues to be involved in several projects at the Cardiovascular Health Research Unit (CHRU) at the University of Washington. His research interests are in the analysis of incomplete data, multiple imputation, Bayesian methods, design and analysis of sample surveys, small area estimation, confidentiality and disclosure limitation, longitudinal data analysis and statistical methods for epidemiology.Gordon Willisleft000Adjunct Odum Instructor and Cognitive Psychologist, National Cancer InstituteGordon Willis is Cognitive Psychologist in the Office of the Associate Director of the Applied Research Program. Prior to that he was Senior Research Methodologist at Research Triangle Institute, and he also worked for over a decade at the National Center for Health Statistics, CDC, to develop methods for developing and testing survey questions. Willis attended Oberlin College, and received a PhD in Cognitive Psychology from Northwestern University. He now works mainly in the area of the development and evaluation of surveys on cancer risk factors, and focuses on questionnaire pretesting. He has produced the "Questionnaire Appraisal System" for use in evaluating draft survey questions, and has written the book "Cognitive Interviewing: A Tool for Improving Questionnaire Design." He also co-teaches a graduate-level questionnaire design course at the Joint Program for Survey Methodology at the University of Maryland, and serves as Adjunct Faculty at the Uniformed Services University of the Health Sciences (USUHS). His research interests have recently turned to cross-cultural issues in self-report surveys and research studies, and in particular the development of best practices for questionnaire translation, and the development of pretesting techniques to evaluate the cross-cultural comparability of survey questions.Emily McFarlane Geisenleft000Adjunct Odum Instructor and Survey Methodologist, RTI InternationalEmily Geisen a survey methodologist at RTI International where she manages RTI’s cognitive/usability laboratory. She received her MS in Survey Methodology from the University of Michigan in 2004. She has over 10 years of experience designing methodological research studies, developing and evaluating survey instruments, leading data collection tasks, managing projects, and performing statistical analyses using SAS. She specializes in evaluating survey instruments to improve data quality and reduce respondent burden. She is lead author of a 2017 book titled?Usability Testing for Survey Research, published by Morgan Kaufmann. Emily is currently serving?AAPOR?as the Membership and Chapter Relations (MCR) Communications sub-chair. She was the 2010 conference chair for the Southern Association for Public Opinion Research (SAPOR) and the 2009–2011 secretary of the Survey Research Methods Section of the American Statistical Association. Geisen is one of the instructors for the required CPSM course titled? HYPERLINK "'14%20Syllabus.pdf" \t "blank" Questionnaire Design.Emily Geisen is a survey methodologist at RTI International. She received her M.S. in survey methodology from the University of Michigan. She has over 10 years experience designing methodological research studies, developing and evaluating survey instruments, leading data collection tasks, managing projects, and performing statistical analyses using SAS. She is the manager of RTI’s cognitive/usability laboratory and specializes in evaluating survey instruments to improve data quality and reduce respondent burden. She was the 2010 conference chair for the Southern Association for Public Opinion Research (SAPOR) and the 2009–2011 secretary of the Survey Research Methods Section of the American Statistical Association. FAQSFrequently Asked QuestionsEligibility and Application ProcessWhat is the procedure for applying for the Certificate?What are the pre-requisites for acceptance?Do I have to enroll in the UNC graduate school to be accepted to the Certificate program?What is the cost to complete the program?Short CoursesHow do I find information about the CPSM short courses, such as when and where they will be offered?Do I have to be a UNC student, faculty or staff member to register for the Survey Research short courses?Is there a fee for the Survey Research short courses???Semester Courses, including Registration HelpWhat semesters will the core courses be offered?How do I sign up for semester courses?How do I find out what my PID# is?What is an "Onyen" and how do I create mine?I tried to register for a semester course and received an error message. What should I do?I've already been accepted into Part-Time Classrooms Studies but I took a semester break in my coursework, am I still active in the system?Course Credits and SubstitutionsCan I take courses in the program and apply for the certificate program later?How do I know how many hours a short course is worth toward the short course requirement?Can core courses be substituted? What is the process for getting that approved?What is the process for petitioning that an elective?course not listed for the program be accepted for credit towards the Certificate?Eligibility and Application ProcessWhat is the procedure for applying for the Certificate?Complete an application form and send it to Paul Biemer at the Odum Institute. You will be notified of your acceptance into the program. We recommend setting up an appointment with?Dr. Biemer?to discuss your plan for completing the requirements for the Certificate.What are the prerequisites for acceptance?Baccalaureate degree from an accredited undergraduate institutionGPA of 3.0 or betterAt least 3 credit hours in graduate-level statistical methodsDo I have to enroll in the UNC graduate school to be accepted to the Certificate program?No. You can enroll in courses through the UNC Continuing Education Program located in the Friday Center. It is not necessary to enroll in UNC graduate school or take the GRE or other graduate school exam for admittance.What is the cost to complete the program?There is no cost to apply or be accepted into the CPSM program. The only costs are tuition for the semester courses and short course registration fees.If you are not currently enrolled in UNC, you will need to submit an application to the Friday Center?Part-time Classroom Studies program?in order to enroll in semester courses. The application fee is $80. Tuition and fees are then charged according to the?UNC Tuition and Fee Schedule?at the "Part-Time Classroom Studies - Graduate" rate. (For example, tuition and fees for one three hour course during the 2014-2015 academic year for a Part-Time Classroom Studies Graduate student would total $2248 for a NC resident or $6550 for a nonresident.) At 2014-2015 tuition rates, the minimum total tuition and fees to complete the Certificate would be about $8000 for a NC resident. Many NC residents incur total tuition and fee costs over $10,000.Full-time UNC graduate students register and pay for CPSM semester courses in the same way and at the same rate as their other coursework.UNC employees who are permanent and work a minimum of 30 hours per week are eligible for a tuition and fee waiver. See?Employee Tuition Waiver Policy.Short course registration fees vary depending on the courses you select, but once admitted to the CPSM, you receive the discounted CPSM student rate for as long as you are also taking semester courses toward completion of the Certificate. The 32 credit hour requirement can typically be met for a total of $200, but many students choose to take additional short courses. (As a bonus, once you successfully complete the Certificate, you continue to receive the CPSM student rate for indefinitely for Odum short courses on survey methodology topics.)Short CoursesHow do I find information about the CPSM short courses, such as when and where they will be offered?All of the Odum upcoming short courses are listed on our website under?Upcoming Short Courses. All short courses require advanced registration. Please check our listing for more information, including how to register, registration openings, course descriptions, etc. Also, courses in the?JPSM?and?University of Michigan Summer Institute?can be viewed at their Web sites.Do I have to be a UNC student, faculty or staff member to register for the Survey Research short courses?No. Our short courses are open to the public.Is there a fee for the Survey Research short courses?Short courses are discounted for CPSM students and graduates (certificate holders). Light refreshments will be provided at all courses; lunch is included for full day courses held at the Friday Center. Charges are as follows:??CPSM studentsUNC-Chapel Hill studentsOther (faculty, staff, other)Half Day$20$35$45Full Day$30$45$60Full Day (Friday Center W/ Lunch)$50$85$100?CPSM students who are not currently enrolled in a CPSM semester (core or elective) course and who have not enrolled in such a course for the previous two semesters are no longer eligible for the CPSM student discounted rates for our short courses.?CPSM students who do not qualify for the discounted rate should register for short courses at either the UNC student or Other rate, as appropriate.? Students who register inappropriately will receive an email, prompting them to re-register under the appropriate rate category.Semester Courses, including Registration HelpWhat semesters will the core courses be offered?A schedule of the CPSM offerings is available under CPSM Courses?Required Semester Courses and ScheduleHow do I sign up for semester courses?If you are already enrolled in a degree program at UNC, you need only register as normal when registration begins. If you are not currently enrolled at UNC, you must also apply to Part-Time Classroom Studies in order to be added to the UNC system, obtain a PIN# and register. For information about deadlines, tuition and instructions on how to apply, please go to the Friday Center's Website? HYPERLINK "" \t "blank" Part-time Classroom Studies?.How do I find out what my PID# is?Your PID# is automatically assigned once you have been accepted in any UNC department. Visit the?UNC PID Office?to look up your PID #.What is an "Onyen" and how do I create mine?Onyen is the name for UNC's campus-wide identifier that you can use to gain access to various electronic resources on campus. To create an Onyen, please go visit? HYPERLINK "" Onyen ServicesI tried to register for a semester course and received an error message. What should I do?This can be a result of several factors listed below:(1) You may have a financial hold on your account. Please contact the Cashier's office to make sure you do not have any financial holds on your account. For contact information, please go to?Student Account Services.(2) You are trying to register for a course which requires instructor permission only. Some courses may require instructor approval for registration. If you would encounter this issue, please email?cpsm@unc.edu?in order to be considered for the course in question.(3) You are not active in the UNC system: This could be either because you need to apply to the Friday Center Part-Time Classroom Studies or you need to submit a re-admit application. To find out what your current status is, please contact the Friday Center at the following number: toll free (800) 862-5669 local (919) 962-1134.I've already been accepted into the Part-time Classrooms Studies, but I took a semester break in coursework, am I still active in the system?No, you must submit a re-admit application to the Friday Center if you took a break from courses. When in doubt, please call the Friday Center's Part-Time Classroom Studies department to check your status. Their phone number is: toll free (800) 862-5669 local (919) 962-1134Course Credits and SubstitutionsCan I take courses in the program and apply for the certificate program later?Yes, you may take up to six semester credit hours in the Program without enrolling in the Program. These courses will still count toward the Certificate. However, we recommend enrolling in the Certificate Program as soon as possible to take advantage the information shared with Program participants and other special Program activities.? Please note that?short course?credit is only granted to those enrolled in the program.?How do I know how many hours a short course is worth toward the short course requirement?Credit hours will be assigned to each short course according to the length of the course and noted on the short course listing on the website. Usually, 1 hour of class time is equivalent to 1 credit hour. To receive credit for a short course, the student must be present in the classroom at least 75% of the instruction time for the course. No partial credit will be given. A total of 32 credit hours must be accumulated to satisfy the short course requirement. Please note: Not all Odum short courses will qualify for CPSM credit. See?Upcoming Short Courses?for a complete listing.Can core courses be substituted? What is the process for getting that approved?Yes, under certain circumstances a course which is very similar to one of our core courses but taken at another University can be substituted. This is at the discretion of the CPSM Program Director. However, no more than six credit hours of coursework can transfer and count as credit toward the Certificate.What is the process for petitioning that an elective course not listed for the program be accepted for credit towards the Certificate?Your request should be submitted to the CPSM Program Director. Provide the details of the course: course title and number; syllabus; description; and department.ORIGINSOn the Origins of the Certificate ProgramThe Certificate Program in Survey Methodology (CPSM) at the University of North Carolina at Chapel Hill was founded by the Odum Institute for Research in Social Science. Kenneth Bollen, the director of the Odum Institute, and Paul Biemer, the director of the Survey Group at the Odum Institute and Distinguished Fellow at RTI International, constructed the core curriculum in 2002 and petitioned to create the new courses in January, 2003. At about the same time, they petitioned to transform this curriculum into a Certificate Program. The CPSM received official approval from UNC-Chapel Hill in the fall semester of 2003. In the spring of 2004, five new courses were established in the Department of Sociology to form the core of the Program. Our first cohort to complete the CPSM training and to receive the Certificate was a group of five recipients in the spring semester of 2006.?The CPSM would not be the success it is today without our valuable partnerships with other organizations. The CPSM at the Odum Institute has closely collaborated with RTI since its origins. In addition to having staff members enrolled in the CPSM, RTI has provided senior staff as course instructors and has cosponsored speakers and provided student practicum opportunities and administrative support. Furthermore, the Joint Program in Survey Methodology (JPSM) at the Universities of Maryland (College Park) and Michigan (Ann Arbor) has been a partner in teaching courses and sharing knowledge. The Departments of Sociology and Biostatistics at UNC have also made important contributions of faculty time and courses and student practicum opportunities in support of the program.?CONTACTSFor more information, contact:cpsm@unc.eduTeresa Edwards, teresa_edwards@unc.edu, 843-0253Paul Biemer, ppb@ICPSR-CHAPEL HILLRegistration for ParticipantsPlease click?here?for information on fees and to register online.UNC-Chapel Hill, the first university to offer joint workshops with the the Interuniversity Consortium for Political and Social Research (ICPSR) outside of Ann Arbor, Michigan, is pleased to continue this tradition and to announce our joint Odum Institute/ICPSR courses for this summer. Registration and payment of fees are handled by ICPSR at?ICPSR Summer Program in Quantitative Methods of Social Research. UNC-Chapel Hill is a member of ICPSR; all UNC faculty, students, and staff pay the membership rate for the fees.?Courses offered at the Odum Institute at UNC-Chapel Hill are:Machine Learning for the Analysis of Text as Data (May 24-27)Latent Growth Curve Models (LGSM): A Structural Equation Modeling Approach (June 6-10)Multi-Level Models: Pooled and Clustered (June 13-17)Introduction to Mixed Methods Research (July 6-8)Growth Mixture Models: A Structural Equation Modeling Approach (July 11-13)Qualitative Research Methods (August 3-5)Analyzing Social Networks: An Introduction (August 8-12)Fee for the 3-day course: $1,500Fee for the 4-day course: $1,600Fee for the 5-day course: $1,700For more information, please go to:? ICPSRClick?here?for more information on the ICPSR Summer Program.DATA MATTERS: DATA SCIENCE SHORT COURSESSponsored by the National Consortium for Data Science (NCDS), the Renaissance Computing Institute (RENCI), and the Odum Institute for Research in Social Science, the "Data Matters: Data Science Summer Workshop Series" is a week-long series of classes for researchers, data analysts, and other individuals who wish to increase their skills in data studies and integrate data science methods into their research designs and skill sets. Scholars, analysts, and researchers from all disciplines and industries are welcome. Both one- and two-day courses will be offered; participants are welcome to register for one, two, or three classes. Classes will run from 10 a.m. to 4:45 p.m.?Data Matters?from?Odum Institute?on?Vimeo.Registration is now OPEN: To register, please go to? will also hold a second Data Matters Course Series in August at NC State. For more information, go to you register for the June UNC session, you will not be able to switch your registration to August. NCSU will have their own registration process for August.?June 20-24, 2016June 20-21?Introduction to Data Science (Tom Carsey)Introduction to Information Visualization (Angela Zoss)Introduction to Data Science Using R (Chris Bail)Data Curation: Managing Data throughout the Research Lifecycle (Jon Crabtree, Thu-Mai Christian, Sophia Lafferty-Hess)Writing Questions for Surveys (Nora Cate Schaeffer)June 22Conceptual Diagrams in in Information Visualization (Eric Monson)Programming in R (Chris Bail)Introduction to Big Data and Machine Learning for Survey Researchers and Social Scientists (Trent Buskirk)Introduction to Geospatial Data for the Data Scientist (Bill Wheaton)June 23-24Introduction to Data Mining and Machine Learning (Ashok Krishnamurthy)Collecting, Classifying, and Analyzing Textual Data (Chris Bail)Simulation Strategies in Data Science: System Dynamics and Agent-based Modeling (Todd BenDor)Conducting and Analyzing Cognitive Interviews: A Hands-on Approach (Gordon Willis)Analysis with Complex Sample Survey Data (Brady West)If you have questions, please contact?Paul_Mihas@unc.edu.??Course DescriptionsJune 20-21Introduction to Data ScienceTom Carsey?This course provides an introduction to data science, focusing on data about people. It will cover basic building blocks, key concepts, strengths and limitations, and the ethical issues that emerge in data science. Numerous examples will be discussed and sample code and data will be explored.?Why Take This Course?Data science combines tools from information science, computer science, and statistics to collect, manage, analyze, and understand digital data. Modern data science pays particular interest to data regarding the social and economic attitudes and behaviors of people.?What Will Participants Learn?This course will help equip participants from various disciplines and industries with a general understanding of data science terms, approaches, and strategies for effectively using data science.?PrerequisitesNone ??Introduction to Information VisualizationAngela Zoss?This course will help beginners get started preparing and designing information visualizations – a true “zero to sixty” course. Participants will learn how to clean and structure data; see how freely available software can be used to create charts, maps, and graphs; and follow basic design suggestions to fine-tune the final presentation of visualizations for publication or reporting.?Why Take This Course?Visualization is a growing area of interest for researchers in all disciplines. Visualizations can illuminate important trends in a data analysis project or help an audience engage emotionally with a research area. Many tools are available to produce visualizations, however, and it is not always clear which tool is best or how to structure data to work with the tool. This course will walk participants through a wide variety of data sources and chart types to help even beginners to visualization feel comfortable embarking on a new visualization project.?What Will Participants Learn?The course will be organized in four major sections: basic charts; static and web-based maps; network diagrams and hierarchical visualizations; graphic design for information visualization.?The instructor will demonstrate several tools. These will likely include Excel, Tableau, QGIS, CartoDB, RAW, and Gephi (though the course may adjust slightly to take advantage of any sudden changes in available technology). This is not a hands-on course, but participants are welcome to download any of these packages on their laptops and follow along with the instructor’s examples.?Prerequisites and RequirementsThis course will assume a basic understanding of spreadsheets as a way of storing and processing data. No programming will be necessary, though we may cover tools that work with HTML (especially SVG) in advanced examples. Bringing a laptop is not required, but participants are welcome to do so. ?Introduction to Data Science Using RChris Bail?+ This course provides a basic introduction to the R software environment for the purpose of data science. The course covers importing and exporting data, manipulating data or recoding variables, and visualization and statistical analysis.?Why Take This Course?R has recently become the preferred computing and statistical analysis software for academic analysis because it offers unparalleled breadth of tools for virtually any model of interest to social scientists—and particularly those interested in so-called “big data.” Unfortunately R also has a steep learning curve because it is maintained by academics that have few career incentives to make it user friendly. Courses such as this one are therefore indispensable for obtaining a basic working knowledge of the language and learning how to navigate the complex web of information about R that is currently available online.?What Will Participants Learn?This course is divided into four sections. The first section provides an overview of how to install R on your computer, import files, and interface with other software such as STATA, SPSS, and R. The second section of the course covers data cleaning and coding, which can be somewhat complicated in R because it uses a variety of data formats that are not used within other languages. The third and fourth sections covers basic descriptive analysis, including cross-tabs, histograms, and scatterplots, and basic linear regression models.?Prerequisites and RequirementsThis course assumes no knowledge of computer programming, but basic familiarity with another statistical analysis software such as STATA, SPSS, or SAS will make the course easier to follow.?Note: In order to participate in the hands-on sections of the course, participants must bring their own laptop computer with enough space to install R and RStudio. ??Data Curation: Managing Data throughout the Research LifecycleJon Crabtree, Thu-Mai Christian, and Sophia Lafferty-Hess?This course will provide an introduction to data management best practices as well as demonstrations of digital curation tools including the Dataverse Network? open source virtual archive platform.?Why Take This Course?Today, a growing number of funding agencies and journals require researchers to share, archive, and plan for the management of their data. In 2013, an Office of Science and Technology policy memo highlighted the importance of providing open access to datasets and scholarly publications as a method of promoting innovation, accountability, transparency, and efficiency. As researchers and information professionals respond to these new requirements, data curation knowledge is necessary for the effective management, long-term preservation, and reuse of data.?What Will Participants Learn?Participants will learn about: the diversity of data and their management needs across the research data lifecycle; the impetus and importance of preserving and sharing data; the processes required for preserving and sharing data; digital repository activities and assessment; the role of advocacy and communication when discussing data management best practices.?PrerequisitesNone ??Writing Questions for SurveysNora Cate Schaeffer?The course focuses on the structure and wording of individual survey questions, whether for interviewer-administered or self-administered instruments. There are opportunities to apply the guidelines and principles during in-class exercises.?Why Take This Course?This course will be of use to researchers who will be writing or reviewing survey questions or survey instruments as well as to those who analyze survey data. This course gives practical guidance to those who have written survey questions but who are not familiar with research on question design, those who are just beginning to design survey instruments, and those who use survey data but do not themselves design survey instruments.?What Will Participants Learn?The course topics include a structural analysis of parts of a survey question and an introduction to cognitive interviewing as a method for testing survey questions. The largest portion of the class is devoted to guidelines for diagnosing problems in survey questions and writing new survey questions. These guidelines summarize and apply research that underlies the key decisions in writing survey questions.?Prerequisites and RequirementsThere are no requirements or prerequisites. Those who attend might find it useful to download these two papers in advance:?Schaeffer NC. Presser S. 2003. “The Science of Asking Questions.” Annual Review of Sociology 29: 65–88. NC, Dykema J. 2011. “Questions for Surveys: Current Trends and Future Directions.” Public Opinion Quarterly, 75, 5: 919-961. 22Conceptual Diagrams in Information Visualization: Graphic Design for Effective CommunicationEric Monson?Well-designed diagrams in information visualization aren’t just pretty; they convey information effectively by working in concert with human perception. This course will equip you with the tools you need to make clear and impactful conceptual diagrams using Adobe Illustrator.?Why Take This Course?Words are essential for thinking and reasoning, but listening and reading are serial processes which require your audience to retain information in working memory while putting the pieces together. Information graphics, on the other hand, can be consumed quickly using the parallel nature of our visual systems, decreasing the cognitive load on the viewer. The problem is that effective graphic design isn’t intuitive – it takes some training that not many of us have had. The good news is that with a bit of guidance, we can quickly make large improvements in what we produce and recognize how to improve what we’ve created in the past.?What Will Participants Learn?In this course you will learn a few core principles of good graphic design, along with common visual metaphors for conveying your ideas. We will also practice the process of diagram creation, from rough brainstorming sketches to final digital artwork. You will learn the basics of using Adobe Illustrator, the professional standard in vector graphics software, which many people avoid because of its steep learning curve. You will see that it is quite easy to combine simple shapes to create interesting and clear diagrams.?PrerequisitesThere are no prerequisites. If you want to practice the Adobe Illustrator techniques in class, you will need to bring a laptop with the free trial version of the software installed. Please go to to sign up for an Adobe ID, download and install the software. Note: Since the free trial period is only 30 days, you’ll want to wait until less than 30 days before the course date to install the package. ??Programming in RChris Bail?This class provides students with an introduction to basic programming techniques in R, a program with stronger object-oriented programming facilities than most statistical computing languages. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R's popularity has increased substantially in recent years.?Why Take This Course?This class will be useful to those who wish to restructure or clean unstructured data, collect new data in an automated fashion, or improve the speed of data analysis.?What Will Participants Learn?Students will learn basic programming techniques such as functions, “for” loops, if/else statements, vectorized functions, and parallel computing techniques.?PrerequisitesBasic familiarity with R syntax, objects (e.g. matrices, lists, data frames etc.)??Introduction to Big Data and Machine Learning for Survey Researchers and Social ScientistsTrent Buskirk?Data science, machine learning and big data are all the rage in many areas where decisions are required or insights need to be made. In this course we explore how big data concepts, processes and methods can be used within the context of social science and survey research. We also provide a technical overview of common machine learning algorithms coupled with examples that are specifically motivated by social science and survey research applications.?Why Take This Course?Big data and machine learning can be valuable assets to survey research and other social science methods. Applications of passive data collection and machine learning in social science have begun to emerge in many contexts and for many purposes. Survey researchers have long used auxiliary data sources to append person-specific information to sampling frames or survey responses. These days the auxiliary data often come from big data sources. In other contexts, administrative data and other big data sources are being harvested as alternatives to traditional surveys, in part due to cost considerations and in other part due to time sensitivities. So in this new era where data are bigger and machines learn along with humans, what does the future of social science look like and how can these methods help us derive better insights, improve our surveys and refine our designs? While certainly big data can provide insights into social and survey related areas, it is not the panacea nor the replacement for traditional methodologies, per se and much work is needed to translate the volume of data into useable information. This course will explore the many roles that big data and machine learning may play in the social science arena, with particular focus survey research methods.?What Will Participants Learn?This course will offer participants: an overview of key big data terminology and concepts; an introduction to common data generating processes; a discussion of some primary issues with linking big data with survey data; issues of coverage and measurement errors within the big data context; a discussion of information extraction and signal detection in the context of big data; a discussion of the similarities and differences in model building for inference versus prediction; an overview of general concepts from machine learning as they apply to processing big data; a discussion of signal detection and information extraction; a discussion of the potential pitfalls for inference from big data; an introduction to a set of key machine learning algorithms (e.g. cluster analysis, classification trees, random forests, conditional forests) to process big data using R with example code provided?Prerequisites/Who Should AttendThe course is aimed at both producers and users of social science and survey data. The course is aimed equally at researchers from academia, government and the voluntary and private sector and is appropriate for researchers new to this topic. While we illustrate big data in the context of survey research concepts such as responsive/tailored survey designs, measurement error, nonresponse bias and data linkage, it is not required that the participants be fully conversant in these concepts. Familiarity with model building and model selection as well as the R program is not required but is suggested. While this course is not intended to teach participants machine learning via R, we will explore four common machine learning algorithms and provide R code and output to illustrate these methods within the context of the R language.?Introduction to Geospatial Data for the Data ScientistBill WheatonThis course offers a broad introduction into the use of geospatial data in data science applications. The course will be highly focused on what makes geospatial data different from other types of data and what these differences imply for using and applying geospatial data. The course materials will be built for non-geospatial professionals who find themselves needing to use geospatial data more effectively.Why Take This Course?The availability and uses of geospatial data has been growing for decades. Recently, with the advent of robust web-mapping and dynamic client-side web tools, many data analysts, applications programmers, web developers, and data scientists of all types have been confronted with geospatial data without having a background in geography or Geographic Information Systems (GIS). This course will ground participants in fundamental concepts of geospatial data science, geospatial computing, and geospatial applications so they can be more efficient and accurate in using geospatial data in their daily jobs.What Will Participants LearnParticipants will learn: basics of map projections and the use of projected and un-projected geospatial data; how issues of scale, precision, and accuracy affect applications of geospatial data; geospatial data models and the main ways geospatial data is presented in computer form; key open-source and commercial-off-the-shelf applications that handle geospatial data.PrerequisitesBasic computer skills. An understanding of tools such as spreadsheets, relational database management systems (RDMS), and programming will be beneficial, but not required.June 23-24Introduction to Data Mining and Machine LearningAshok Krishnamurthy?This course will introduce participants to a selection of the techniques used in data mining and machine learning in a hands-on, application-oriented way. Topics covered will include data exploration, decision trees, clustering, association rules, regression and pattern classification. The computing exercises will be based on the statistical programming language, R. At the end of the two days, you will be able to explore a data set, and determine which analysis method is appropriate for the data, and be able to use R packages to obtain results.?Why Take This Course?The ready availability of digital data from numerous sources is a tremendous opportunity for businesses and scientists to obtain new insights and confirm hypotheses. Data mining provides the theoretical basis, algorithms and computational methods to manage, analyze and get information from the data. In the world of big data and data science, data mining is a fundamental tool for data insights.?What Will Participants Learn?The course will be organized in the following major sections: data exploration; association rules; decision trees; clustering; regression; classification Each section will have an associated computer exercise. We will make extensive use of R and R packages in the computer exercises.?PrerequisitesThis course will assume a basic understanding of statistics and calculus at the undergraduate level. Some experience with R or SAS would be helpful. ?Collecting, Classifying, and Analyzing Big DataChris Bail?This course explains how to collect, classify, and analyze text-based data from the internet or other digital sources using R. The course will cover screen-scraping, interfacing with Application Programming Interfaces (APIs), basic natural language processing such as topic models, and explain how these data can be incorporated into traditional social science models.?Why Take this Course?Big data has become one of the most significant buzzwords in academic circles over the past few years, yet the study of how to use text as data crosses so many different academic disciplines, programming languages, and styles of communication that those who wish to enter this nascent field are quickly overwhelmed. This course will provide students with a panoramic perspective of the field and the programming skills necessary to navigate the rapidly growing wealth of information online about this subject.?What Will Participants Learn?This course is divided into four segments. The first section will cover basic techniques for collecting text-based data from the internet such as screen scraping and writing code to extract data from application programming interfaces. The second section will explain how to clean and code text-based data using a variety of pre-processing techniques such as stemming. The third section will explain how to apply topic models and other natural language processing tools to sample data. The fourth and final section will discuss best practices for incorporating variables produced via these methods into conventional social science models such as regression or social network analysis.?Prerequisites and RequirementsThis course assumes a basic working knowledge of the R language. Students with no knowledge of R might consider pairing this course with the “Introduction to Data Science in R” course that is also being offered early in the week.?Note: In order to participate in the hands-on sections of the course, participants must bring a laptop computer with enough space to install R and R Studio. ?Simulation Strategies in Data Science: System Dynamics and Agent-based ModelingTodd BenDor?This course offers a step-by step, interactive approach to conceptualizing, creating, and implementing simulation models. These analytical tools can be used in addition to traditional triangulation strategies to operationalize quantitative and qualitative variables (or a combination of both) into a simulation. This two-day course will introduce two computer simulation approaches: systems thinking and system dynamics modeling (day 1), and agent-based modeling (day 2). The goal of this course is to enhance knowledge and skills in understanding and analyzing the complex feedback dynamics in social, economic, and environmental problems.Why Take This Course?With an emphasis on aggregate behavior, system dynamics modeling can be useful in understanding the non-intuitive behavior of systems. Using basic concepts such as accumulation, rates of change, and feedback loops, systems thinking (qualitative) and system dynamics modeling (quantitative) can help researchers better address complex questions. Conversely, with a particular emphasis on individual behavior, agent-based modeling techniques can harness large-scale datasets to represent individual behavior and the social, economic, or environmental system structure that emerges. Agent-based modeling provides a sophisticated way to translate research goals into a dynamic model in simulation form. For both modeling approaches, we will emphasize the application and interpretation of modeling concepts and output rather than mathematical theory.?What Will Participants Learn?On day 1, we will also spend substantial time understanding how policy interventions affect the behavior and structure of systems. Students will develop a better understanding of feedback and its non-intuitive effects within social and physical systems, as well as an understanding of how to quantify causal relationships in dynamic, complex systems. The course will introduce system dynamics modeling through the STELLA and Vensim modeling platforms. On day 2, we will introduce the emerging analytical method of agent-based modeling, focusing first on when and why to use agent-based modeling, followed by a tutorial with the NetLogo simulation software.?Prerequisites and RequirementsThis course will assume a basic understanding of computer literacy and algebra. Basic computer programming concepts will be useful for the agent-based modeling part of the course as we will be stepping through the creation of basic models. Note: In order to participate in the hands-on sections of the course, participants must bring a laptop computer. ??Conducting and Analyzing Cognitive Interviews: A Hands-On ApproachGordon Willis?The short course will provide a solid grounding in the design and implementation of cognitive testing of survey questionnaires, and in the analysis of the data produced in cognitive interviews. There will be coverage of a range of verbal probing techniques, with practice exercises included.?Why Take This Course?Cognitive testing is a widely used approach to pretest and evaluate survey questions, but there are few venues for learning how to conduct cognitive interviews. The course will emphasize the development and implementation of verbal probing techniques for both pretesting and evaluating survey questions, focusing on flexible, yet unbiased approaches to probing, based on Willis’s Cognitive Interviewing and Questionnaire Design: A Tool for Improving Survey Questions (2005). Participants will receive hands-on practice and feedback. We will also discuss analysis of cognitive interview results, a commonly neglected area of cognitive testing, guided by Willis’s Analysis of the Cognitive Interview in Questionnaire Design (2015). Finally, Dr. Willis will discuss novel developments in the field, such as web-based probing, and cognitive testing with multicultural populations.?What Will Participants Learn?Participants will learn how to design, conduct, and analyze cognitive interviews. Procedures to be addressed include reviewing the draft questionnaire to identify potential problems and issues, formulating cognitive probing questions to address identified concerns, using probing to detect unanticipated problems, follow-up probing, and avoiding pitfalls when conducting the interview. Regarding analysis of cognitive interview data, participants will learn about: (a) methods for producing data, coding interview observations, and summarizing the results of cognitive interviews; (b) techniques for combining results across interviewers and testing labs; (c) five major analysis strategies applicable to testing results; and (d) the interpretation and communication of findings. Finally, there will be a discussion of software that facilitates analysis, a framework for the transparent and comprehensive development of testing reports, and the inclusion of reports within an online database of existing testing reports.?PrerequisitesBasic knowledge of questionnaire design, but no specific types of training or credentials. ??Analysis of Complex Sample Survey DataBrady West?In order to extract maximum information at minimum cost, sample designs are typically more complex than simple random samples. Stratified cluster sample designs are common. But how do you analyze the survey data collected from a complex sample? In particular, how do you determine margins of error and make inferences that take into account the complex sample design features? This one-day short course will discuss methods for the analysis of complex sample survey data, including estimation of descriptive parameters, methods for variance estimation, and linear and logistic regression modeling. This short course is intended for anyone analyzing survey data collected from complex samples and assumes a background in applied statistical analysis. The course is largely based on selected chapters from the book Applied Survey Data Analysis by Steve Heeringa, Brady West, and Pat Berglund (Chapman & Hall / CRC Press, 2010). The course will be lecture-based, but participants may bring their own laptop computers with software for the analysis of survey data installed to follow the examples.?Why Take This Course?Secondary analyses of large survey data sets selected from complex probability samples further scientific progress in many fields, especially in an era where original data collection can be very costly. Unfortunately, a failure on the part of secondary analysts to properly account for the sample design features when conducting analyses can lead to biased results and incorrect inferences. This course will provide detailed practical guidance regarding appropriate analytic methods for complex sample survey data.?What Will Participants Learn?The course will focus on: complex samples and design effects in survey estimation and inference; weighting and complex sample inference; adjustments for nonresponse; post-stratification; Sampling error calculation models; software for sampling error estimation; Inference for descriptive statistics; subpopulation analysis; percentiles; functions of descriptive statistics; analysis of categorical data; linear regression analysis of complex sample survey data; logistic regression analysis of complex sample survey data.?Pre-requisitesParticipants should have a working knowledge of applied statistical analysis, including hypothesis testing, descriptive estimation, confidence interval construction, and linear and generalized linear regression modeling. Background in applied survey sampling is recommended but not required. No knowledge of specific software is required.RESOURCES PAGEIntroduction to Information VisualizationData for SessionGo to? simply click on the following . . .hurricanes_2005hurricanes_by_statehurricanes_tractsSoftware ToolsExcel, any versionTableau Public, version 9.0?, version 2.8.2?, version 0.8.2-beta?, version 0.91? AccountsCartoDBNormalAcademic?<)Tableau Public? YOUR SURVEY RESEARCH PROJECTA weekly workshop series by the H.W. Odum Institute for Research in Social Sciences for students and other researchers planning a survey data collection effort.Click?here?for a course flyer.VIDEO COURSESThe Odum Institute posts short video classes, video presentations from classes held at Odum,?and video presentations regarding information on our services and tips on using specialized software.??Please note that these are best viewed using Internet Explorer and may not run smoothly on older versions of Firefox.Statistical Computing and Technical EditingIntroduction to Information VisualizationThe Odum Institute has created an Introduction to R and RStudio video series.?Introduction to R TutorialsDownloading R and RStudioGetting Started with RStudioBasic Manipulation of ObjectsUseful Functions and CommandsLoading and Working with DataBasic GraphicsBasic Programming CommandsNumerical OptimizationSupplemental MaterialsThe following supplemental files will be used in the tutorial. Click?here?to download the dataset file, and click?here?to download the R script file.The Odum Institute has created an Introduction to LaTeX video series.Introduction to LaTeX TutorialsDownloading MikTeX and TeXnicCenterSetting Up TeXnicCenterFirst StepsCreating an Article: Part 1Creating an Article: Part 2Creating an Article: Part 3Creating an Article: Part 4Mathematical ExpressionsAutomatic CitationsCreating a SlideshowCreating a PosterSupplemental MaterialsThe following supplemental files will be used in the tutorial.Example Figure 1Example Figure 2Example Figure 3Example Figure 4Beamer Text FilePoster Text FileThis folder contains LaTeX template files that you can use to write your MA thesis or dissertation according to UNC’s style requirements. To download click?here.SEM Using LavaanTo download this presentation, click?here.Data Management CoursesThe Odum Institute posts short video classes, video presentations from classes held at Odum,?and video presentations regarding information on our services and tips on using specialized software.??Please note that these are best viewed using Internet Explorer and may not run smoothly on older versions of Firefox.Overview of REDCap and CDART Clinical Data Management SystemsServices and Resources for Research Computing at UNC-Chapel HillDMPToolData Security: Policies and Regulations Impacting Research Data?Considerations and Strategies for Handling Sensitive SPATIAL DataConsiderations and Strategies for Handling Sensitive DataUnderstanding Data Management Plans and Funder RequirementsData Management Resources at UNC: The Dataverse Network and the Carolina Digital RepositoryCreating Your Own Dataverse NetworkOverview of REDCap and CDART Clinical Data Management SystemsNovember 27: 1–2 p.m.Clarence Potter, Clinical Data Manager, NC Translational and Clinical Science Institute (NC TraCS)This talk provides a brief introduction to the REDCap and the CDART clinical data management systems that the NC TraCS Institute provides to the university community. These two systems enable Electronic Data Capture (EDC) and are hosted and managed by TraCS. REDCap is a tool that investigators can use themselves to create the forms for storing data related to their research projects. CDART is a more extensive tool for complex/multi-center trials and requires programmer support (at this time) to develop the forms and reports.Click?here?for the online presentation.Services and Resources for Research Computing at UNC-Chapel HillOctober 31, 2012: 11 a.m.-12 noon14 Manning HallPresenter: Mark Reed, Director, ITS Research ComputingThis session provides a brief introduction to the services, resources and projects Research Computing makes available to the university community. Attendees will hear how they can use these resources and services to better serve their research needs. Some of the topics to be covered include the Virtual Computing Lab, Training Opportunities, Compute Clusters, Software, Mass Storage, TarHeel Linux, and Secure Data Exchange.The Data Management Short Course series was initiated in November 2010 to examine funders' data management plan requirements and discuss resources available to assist researchers in preparing plans.Click?here?for the online presentation.For the slides, please click?here.DMPToolApril 26, 2012: 12-1 p.m.14 Manning HallAs of January 2011, the National Science Foundation began requiring the submission of data management plans with grant proposals, and many other funders have followed suit (e.g., NEH, IMLS). The DMPTool provides general guidance and assistance for researchers needing to create a data management plan. This tool is the product of a collaborative endeavor and is a service of the University of California Curation Center (UC3) and the California Digital Library. This brown bag session will demonstrate the functionality of the DMPTool, will provide a tutorial on how to use it, and will offer some basic information on the importance of data management plans. In addition, the session will share the added resources the UNC community has at its disposal as a participating institution.Click?here?for the presentation.For the slides, click?here.Data Security: Policies and Regulations Impacting Research DataMarch 20: 10-11:30 a.m.This course will address various aspects of managing and protecting sensitive research data. Panelists will discuss campus policies, human subjects protection requirements and government regulations that impact how research data is managed. Presenters include:Stan Waddell, Director ITS Security - UNC data security policies/requirementsMarc Tillett, Associate Director for Infrastructure and Operations, Office of Research Information Systems - IRB requirements for data securityDennis Schmidt, School of Medicine OIS Director and HIPAA Security Officer - HIPAA requirements for data securityJuli Tenney, Research Compliance Officer – Privacy, FISMA and other regulations applicable to research data, and how breaches are treated by Federal and NC State LawFor the Waddell lecture slides, click?here.For the Tillett lecture slides, click?here.For the Schmidt lecture slides, click?here.For the Tenney lecture slides, click?here.Click?here?for the online presentation.Considerations and Strategies for Handling Sensitive SPATIAL Research DataMay 10, 2011: 12 p.m.-1 p.m.?14 Manning HallPresenter: Brian Frizzelle, Head, Carolina Population Center's Spatial Analysis ServicesResearch data involving human subjects present significant challenges for data sharing, but even more so when the data are analyzed in Geographic Information Systems (i.e., dynamic mapping software) and presented on maps. Brian Frizzelle from the UNC Carolina Population Center (CPC) will discuss key considerations for researchers for protecting sensitive data when presenting GIS analyses.The course is second of several which will address various aspects of managing and protecting sensitive research data.Click?here?for the online presentation.For the slides, click?here.Considerations and Strategies for Handling Sensitive Research DataMarch 29, 2011: 12 p.m.-1 p.m.?14 Manning HallNancy Dole, Deputy Director for Research Services and Joyce Tabor, Add Health Project Data Manager?(both of the Carolina Population Center)Research data involving human subjects present significant challenges for data sharing as promoted by funding agency requirements. Nancy Dole and Joyce Tabor from the UNC Carolina Population Center (CPC) will discuss key considerations for researchers collecting personally identifiable information and other sensitive data. They will present the CPC's National Longitudinal Study of Adolescent Health (Add Health) Project (?) as a case study of a research study involving sensitive data and discuss strategies utilized in this project for managing, protecting, de-identifying and sharing that data.This course is the first of several which will address various aspects of managing and protecting sensitive research data.Click?here?for the online presenation.For the slides, click?here.Understanding Data Management Plans and Funder RequirementsNovember 9, 2010: 12 p.m.–1 p.m.?14 Manning HallAs funding agencies move to require data management plans in all proposals, planning for research data management is not only good practice, but increasingly a necessity. In this session, we will discuss key considerations for managing research data, examine the details of the recently released NSF data management plan requirements, and identify campus and other resources to assist researchers in preparing data management plans.Click?here?for the online presentation.For the slides, click?here.Data Management Resources at UNC: The Dataverse Network and the Carolina Digital RepositoryNovember 16, 2010: 12 p.m.–1 p.m.?14 Manning HallUNC has an abundance of data and information management resources. In this session, we will describe some of the local repository resources and help researchers decide which are most appropriate for their application. Details of services provided by the Carolina Digital Repository and the Dataverse Network will provide insight into these services.Click?here?for the online presentation.For the slides, click?here.Creating Your Own Dataverse Network (DVN)Click?here?to watch a Mediasite presentation on how to create your own DVN.(Recorded in March, 2009)Quantitative CoursesThe Odum Institute is now posting short video classes, video presentations from classes held at Odum,?and video presentations regarding information on our services and tips on using specialized software.??Please note that these are best-viewed using Internet Explorer and may not run smoothly on older versions of Firefox.Logistic RegressionClick?here?to view overview course on logistic regression. Instructor: Cathy Zimmer. Length of course: 1.5 hours.Viewing Presentations in MacPresentations will only play on Macs using OS X 10.4.8 or later.Other CoursesStatistical Discrepancies among Sources: Unemployment RatesClick?here?to view video.Recorded on April 23, 2010: 12-12:30 p.m.Data discrepancies among different sources for the same statistic can create serious problems for policymakers. Which data source should they use? This investigation focuses on differences in 2008 annual unemployment rates for data from the Bureau of Labor Statistics (BLS) and the U.S. Census. The study explains how the BLS and Census have different unemployment data while still citing the same source, the Local Area Unemployment Statistics program. The project then compares these data to Current Population Survey unemployment data. To provide policymakers with the best data, it is important to clarify why there are differences in data and make the best data readily available for the public. This is particularly important for policymakers and analysts because different data may support trends that do not exist.Initial research questions: To understand the differences in the unemployment data, there are several research questions.Where do discrepancies exist between data sources for the same statistic?What are the differences in the sources’ methodologies creating the discrepancies in the data?Are these differences meaningful? How? In what way are they affecting the data?Why is this important for policy makers?Click?here?to download the paper. Click?here?for the appendix.This investigation was completed by students in Dr. Gail Corrado’s Public Policy 698 course. Members of the PLCY 698 course undertake various policy-related projects with local clients for an entire semester, aspiring to achieve solutions for real world problems. The principal investigators for this study are Natassia Rodriguez and Sam Wurzelmann. The PLCY 698 co-investigators are Alicia Heaney, Iris Lattimore, Jonathan Mauney, Cindy Ngwalla. The PLCY 325 (a weekly policy development clinic) co-investigators are Matt Clark, Brittany Johnson, and Brandy Price. Class advisors for the project are Dr. Gail Corrado and Shanyce Campbell. Odum Institute liaisons include Jonathan Crabtree, Ed Bachmann, and Andrew Munn.PAST COURSE DESCRIPTIONS(so many)CSSP TRAININGPainting a Moving Trainer"Painting a Moving Trainer"Dr. Harold Kudler?Friday, March 19, 2010?Click?here?to access the recording. Note that this is not a live broadcast.Click?here?to download a zip file of the presentation (with notes). ................
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