Education

Course Schedule

Qualitative Analysis

MAXQDA Hands-on Workshop

Paul Mihas

This course will cover the capabilities of MAXQDA, a software program that supports qualitative data analysis and helps users systematically code, evaluate, and interpret texts. It is also a powerful tool for developing theories as well as testing theoretical hunches. Its features include coding, memo writing, matrix building, and map building.

No registration required.

For more information, please contact Paul Mihas.


Davis 3010
October 20, 2014 2:00 PM - 4:00 PM

Quantitative Analysis

A Beginner's Look at Multilevel Analysis

Paul Voss

This five-session short course provides an introduction to multilevel modeling for those who have no prior knowledge of the topic. It will also be useful for those with some experience in multilevel data analysis but a desire to develop a stronger theoretical grounding and improved understanding of different multilevel models and computer output. Multilevel modeling is used throughout the social, medical and other sciences to understand how response variables at one level of analysis can be influenced by variables from other levels in a data nested hierarchy. The R programming suite will be used to demonstrate the specification of various multilevel models.

To register, click: here


Davis 3010
October 29, 2014 2:00 PM - 4:00 PM
November 05, 2014 2:00 PM - 4:00 PM
November 12, 2014 2:00 PM - 4:00 PM
November 19, 2014 2:00 PM - 4:00 PM
December 03, 2014 2:00 PM - 4:00 PM

Bayesian Statistics: An Introduction for Social Scientists

Joe Ibrahim

This short course will be divided into 3 parts. The first part will discuss introductory principles in Bayesian inference, including the Bayesian paradigm, prior elicitation and computational methods. Also, Bayesian methods for linear models and generalized linear models will be discussed in detail.

The second part will examine models Bayesian methods for longitudinal data and survival models, and the third part will examine some special topics such as Bayesian model assessment and missing data. Several applications and case studies will be presented throughout the short course and SAS code as well as WinBUGS code will be provided throughout the course.

Prerequisites: Coursework in linear models and survival analysis would be helpful.

Registration fees:

  • UNC Students: $22
  • UNC Faculty/Staff: $40
  • Other: $50

    To register, click: here

    If you have questions, please contact Paul_Mihas@unc.edu

    Davis 219
    November 11, 2014 12:30 PM - 2:30 PM
    November 12, 2014 12:30 PM - 2:30 PM
    November 18, 2014 12:30 PM - 2:30 PM

    Spatial Analysis

    Applied Spatial Regression Analysis

    Paul Voss

    This six-session 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. Spatial data typically are characterized, first, by heterogeneity, arising (1) from often highly diverse units of analysis (in terms both of geographic area and population size) and (2) from large-scale, long-distance regional differentiation (where social and economic activities are distributed across the landscape in somewhat homogeneous regions that stand out as different from neighboring regions). Second, spatial data usually are characterized by localized, small-scale, inter-unit dependence, arising from a host of mechanisms operating in space that serve to make individual units of analysis very much like other units in their neighborhood. These two factors (spatial heterogeneity and spatial dependence) conspire to bring our traditional regression results into violation of the strict assumptions underlying the standard linear regression model. Thus, when analyzing spatial data, it is paramount, first, to know how seriously the assumptions of the regression model are violated, and, second, what to do about it. This short course will provide a brief orientation to these important issues. Two analysis software packages will be used: OpenGeoDa and R.

    This course is now closed for registration.


    Davis 3010
    September 17, 2014 2:00 PM - 4:00 PM
    September 24, 2014 2:00 PM - 4:00 PM
    October 01, 2014 2:15 PM - 4:00 PM
    October 08, 2014 2:00 PM - 4:00 PM
    October 15, 2014 2:00 PM - 4:00 PM
    October 22, 2014 2:00 PM - 4:00 PM

    QGIS

    Scott Madry

    This will be the first of two, 2-hour hands-on workshops using the QGIS and GRASS open source GIS packages. 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, and includes vector, raster, georegistration, cartographic production and other capabilities, all using ESRI shapefiles as the basic data structure.

    There is no fee for this course.

    No registration required.


    Davis 247
    October 21, 2014 2:00 PM - 4:00 PM

    GRASS GIS

    Scott Madry

    The second 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. In the second hour we will use GRASS in its stand-alone configuration.

    Extensive, hands-on exercises that can be continued after the workshops will be made available, as well as information on how to download the software and training datasets, and other resources.

    No registration required.


    Davis 247
    November 06, 2014 2:00 PM - 4:00 PM

    Survey Research

    Introduction to Focus Groups

    Emily Geisen and Amanda Wilmot

    Focus 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

    This course will count as 7.0 CPSM short course credit hours.

    To Register, click here

    For more information, please contact Jill_Stevens@unc.edu.

    Davis 219
    10/23/14 9am-5pm

    Inferential Issues in Web Surveys

    Mick Couper

    There are many different ways that samples can be obtained for online surveys. These include open invitation surveys of volunteers, intercept surveys, opt-in or access panels, Amazon’s Mechanical Turk, Google Consumer Surveys, list-based samples, and the like. In most cases, the goal is to make inference to some large population. The different approaches to selecting samples and inviting respondents to complete a survey vary in their inferential properties. Threats to inference include sampling error, coverage error, and nonresponse error. In addition to selection methods, a variety of adjustment methods, such as weighting, propensity score adjustment and matching, are being used to mitigate the risk of inferential errors. The course will focus on the assumptions behind the different approaches to inference in Web surveys, the benefits and risks inherent in the different approaches, and the appropriate use of a particular approach to sample selection in Web surveys. The course has a conceptual rather than statistical focus, but a basic understanding of statistics will be helpful. This course is suitable for people who are considering conducting a Web survey for data collection or analyzing data from an existing Web survey.

    Registration Fees:

  • CPSM Students - $30
  • UNC Students - $45
  • Other - $60

    This course will count as 7.0 CPSM short course credit hours.

    To Register, click here

    Davis 219
    11/13/14 9am - 5 pm

    Introduction to Survey Management

    Lisa Thalji

    This course will focus on the application of project management principles and techniques to the management of survey research projects. At the conclusion of the course participants will have a basic understanding of:

    * The principles of project management as applied to survey research
    * How to plan a survey project
    * How to implement the plan and manage the work
    * How to manage the project budget
    * How to manage the project contract

    The course will cover a broad range of survey management topics, including: proposal preparation, Work Breakdown Structures, Gantt charts, organization charts, staffing, budgeting, management tools to monitor the work, earned value analysis, and types of survey contracts. Course participants will receive a workbook containing all material presented in class.

    Registration Fees:

  • CPSM Students - $30
  • UNC Students - $45
  • Other - $60

    This course will count as 7.0 CPSM short course credit hours.

    To Register, click here

    Davis 219
    11/20/14 9am - 5pm

    Statistical Computing

    SAS

    Chris Weisen

    This is a four-part course that does NOT require registration. 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.

    There is no fee for this course.

    No registration required.

    Davis 3010
    10/20/14 - 10/23/14 11am - 1pm

    Other

    Introduction to Census Concepts

    Michele Matz Hayslett

    Do 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, please attend this class before coming to the data access classes on the 25th since this information is critical to being able to pull the data you need. We will compare and contrast content and methodology of the decennial census long form and the ACS, and review Census terminology and geographies.
    Lecture and Discussion - 2 hours

    To register for this course, click here

    Davis 219
    10/29/14 9-11am

    Basic Census Data Access

    Michele Matz Hayslett

    Hands-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; the Summary File Retrieval Tool, the Bureau’s free tool for accessing small geographic level ACS data; 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. Hands-on - 3 hours

    To register for this course, click here

    Davis 219
    10/29/14 1-4pm

    Advanced Census Data Access

    Michele Matz Hayslett

    Hands-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 Census Research Data Center (TCRDC). The first two tools are freely available and focus on census data (U.S. for DataFerrett; international for iPUMS/TerraPopulus); researchers must apply to the Census Bureau (or other federal agency, e.g., the Centers for Disease Control) for access to the TCRDC in order to utilize survey microdata. TCRDC staff will present this portion of the class. Hands-on - 3 hours

    To register for this course, click here

    Davis 219
    10/30/14 9am - 12p

    Causal Inference

    Pushpendra Rana

    This short course will provide a brief orientation to the counterfactual-based inference in observational studies where treatment assignment is non-random. The course will seek to answer causal questions such as “what is the impact of a single intervention A, such as a new climate change policy, on a single outcome Y, such as carbon emissions?”

    Counterfactual analysis of causation does not require full specification of all causes and only require data to be balanced with respect to treatment (intervention) assignment. Randomization of the treatment assignment is expected to exclude all alternative causes and balance potential confounders to establish secure causal claims with certainty. However, in field observation studies (mostly in social or medical sciences), randomization is very difficult to achieve due to the non-experimental nature of the treatment where treatments are observed rather than assigned. In such studies, matching based methods are now widely used to invoke randomization and to make causal claims.

    Morning session will focus on conceptual understanding of the counterfactual-based causal inference and afternoon computer practical will include step-by-step implementation of one of the matching methods – propensity score matching – in R. After completing the short course, you will know enough to (1) explain counterfactual conception of causal inference and (2) conduct a propensity score matching for your own research. Some basic knowledge of regression (linear and logistic) and R would be highly beneficial.

    To register, click: here

    Davis 3010
    11/7/14, 10am - 4pm