Education

Course Schedule

Qualitative Analysis

Qualitative Research Summer Intensive

Carolina Inn
July 27, 2015 9:30 AM - 4:00 PM
July 28, 2015 9:30 AM - 4:00 PM
July 29, 2015 9:30 AM - 4:00 PM
July 30, 2015 9:30 AM - 4:00 PM
July 31, 2015 9:30 AM - 4:00 PM

ICPSR - Introduction to Mixed Methods Research

Kathy Collins

The term mixed methods research (MMR) refers to application and integration of qualitative and quantitative approaches at one or more stages of the research process. The purpose of this three-day interactive course is to introduce to new (e.g., doctoral students, junior faculty) and seasoned (i.e., limited experience conducting MMR) researchers an array of conceptual strategies and practical techniques for formulating, planning, and implementing a single MMR study or program of studies. We will discuss definitions of MMR, objectives, purposes and rationales for conducting a MMR study, writing MMR questions, and techniques for collecting, analyzing, and integrating qualitative and quantitative data. Frameworks and heuristics for developing a MMR design that fits the research question(s), selecting/constructing a mixed sampling design, and applying quality criteria throughout a MMR study will be emphasized. The course also will cover approaches for applying guidelines when reporting results and publishing tips for writing a MMR article. Interspersed throughout the course will be interactive small group activities to engage the participants in the iterative process of conducting MMR. These activities will be structured as breakout groups, and they will be followed by whole group discussion led by the presenter. Participants are encouraged to bring to the course their own MMR project, such as a dissertation prospectus, funding proposal, an idea for a single study, or plans for implementing a program of research.

Prerequisites: Prior experience with MMR is not a prerequisite. Extensive introductory course materials will be provided.

Fee: Members = $1300; Non-members = $2600

For registration details, click here.

Davis 3010

Dates: 8/5/15 - 8/7/15
Times: 9:00am - 5:00pm

ICSPR - Qualitative Research Methods

Paul Mihas
This workshop presents strategies for analyzing and making sense of qualitative data. Both descriptive and interpretive qualitative studies will be discussed, as will more defined qualitative approaches such as grounded theory, narrative analysis, and case study. The course will briefly cover research design and data collection but will largely focus on analysis. In particular, we will consider how researchers develop codes and integrate memo writing into a larger analytic process. The purpose of coding is to provide a focus to qualitative analysis; it is critical to have a handle on your coding practices as you move deeper into analysis. The course will present coding and memo writing as concurrent tasks that occur during an active review of interviews, documents, focus groups, and/or multi-media data. We will discuss deductive and inductive coding and how a codebook evolves, that is, how codes might emerge and shift during analysis. Managing codes includes developing code hierarchies, identifying code ?constellations,? and building multidimensional themes. The class will present memo writing as a strategy for capturing 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 use memos to build evocative themes and theory. Coding and memo writing are discussed in the context of data-driven qualitative research beginning with design and moving toward presentation of findings. One module of the course will be devoted to learning a qualitative analysis software package, ATLAS.ti. The methods discussed in the course will be applicable to qualitative studies in a range of fields, including the behavioral sciences, social sciences, health sciences, and business.

Fee: Members = $1300; Non-members = $2600

For registration details, click here.

Davis 3010

Dates: 8/10/15 - 8/14/15
Times: 9:00am - 5:00pm

Quantitative Analysis

Introduction to Structural Equation Models

Nick Wagner

This course has been rescheduled from 2/26/15 - 2/27/15 due to weather. Please note the time has changed as well.

This three-hour short course, offered over two mornings, provides a brief introduction to structural equation models (SEMs) for individuals who have little to no experience with the topic. Upon completion of the course, participants will have an introductory understanding of the major types of SEMs and the basic steps involved in their estimation. The majority of our time will be spent on concepts that aid the interpretation of SEMs in a research context such as basic terminology, fit indices, and model parameters. Basic examples of SEM estimation will be provided using Mplus. However, this course is not intended to be a hands-on introduction to SEM software. An understanding of matrix algebra is not necessary but participants should have a good handle on linear regression analysis.

Class handout

No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.

For more information, contact Nick Wagner


Davis 219
Dates: 3/26/15 - 3/27/15

Times: 1:00pm - 2:30pm

An Introduction to Designing Experiments

Lucy Martin

This short course will introduce participants to designing and conducting experiments. We will start with an introduction to experiments. What are the benefits and costs of experiments as an approach to research? What are the different types of experiments, and how can they be used to answer research questions in the social sciences? We will then discuss how to design a successful experiment. This will include discussion of deciding which type of experiment to use, treatment design, measurement concerns, sample selection, getting research permissions, and pre-analysis plans.

Registration Fees:

  • UNC Students - $10
  • Others - $15

    To Register, click here

    * 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 219

    Dates: March 30, 2015

    Times: 1:00pm - 3:00pm

    An Introduction to Implementing Experiments

    Lucy Martin

    This course will cover the basics of implementing an experimental design. It will include selecting a site and sample; piloting or pretesting the instrument; planning logistics for enumeration; training and supervising enumerators; and data management. The course will include time for participants to discuss how to apply these principles to their own experimental designs and contexts.

    Registration Fees:

  • UNC Students - $10
  • Others - $15

    To Register, click here

    * 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 219

    Date: April 6, 2015

    Times: 1:00pm - 3:00pm

    ICPSR - Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach

    Ken Bollen

    A powerful method for analyzing longitudinal data is Latent Trajectory Analysis (LTA). LTA allows each case in a sample to have individual trajectories ("latent curves" or "growth curves") representing change over time. In addition to mapping these trajectories, LTA allows researchers to examine the determinants of these trajectories or to relate the trajectories of one variable with those of another. The approach to LTA in this course draws on the strengths of structural equation modeling (SEM), and the primary goal is to introduce participants to the theory and application of LTA. The course begins with a conceptual introduction to LTA, a description of research questions that are well-suited for the technique, and a review of SEMs. The remainder of the course will cover the following topics: LTA models for a single variable with and without predictors of differences in trajectories; modeling nonlinear trajectories; the LTA model for multiple variables; the relation between the parameters governing the trajectories in two or more variables; incorporating predictors of multiple trajectories; and extensions to the LTA model. Participants should have prior training and experience with structural equation modeling and related software.

    Fee: Members = $1500; Non-members = $3000

    For registration details, click here.

    Davis 219

    Dates: 5/18/15 - 5/22/15
    Times: 9:00am - 5:00pm

    ICPSR - Growth Mixture Models: A Structural Equation Modeling Approach

    Sarah Mustillo

    The Growth Mixture Model (GMM) is an extension of the Latent Growth Curve Model (LGCM) that identifies distinct subgroups of growth trajectories and allows individuals to vary around subgroup-specific mean trajectories. Conventional growth modeling estimates a single mean intercept and slope for each individual and variance parameters around the mean intercept and slope. The GMM relaxes the assumption that all individuals are drawn from a single population with common parameters by using latent trajectory classes, resulting in separate intercepts, slopes, and variance parameters for each subgroup.

    This workshop will provide training in estimating GMMs to analyze growth trajectories. Key features of this model are that it can identify the number and form of distinct subgroups of growth trajectories, estimate the proportion of the population in each subgroup, and model predictors of the trajectories and predictors of class membership. In addition to the basic model, this workshop will cover several extensions, such as including a distal outcome predicted by the trajectories, multiple group GMMs , and parallel process or joint trajectory models.

    Fee: Members = $1300; Non-members = $2600

    For registration details, click here.

    Davis 219

    Dates: 5/26/15 - 5/28/15
    Times: 9:00am - 5:00pm

    ICPSR - Multilevel Models: Pooled and Clustered Data

    Tom Carsey
    Multilevel models (also known as hierarchical linear models or mixed models) provide an extremely flexible approach to the analysis of a wide array of social science data. Multilevel modeling allows for the analysis of non-independent or "clustered" data that arise when studying topics such as siblings nested within families, students nested within classrooms, clients nested within therapists, or voters nested within media markets. Such models also accommodate data clustered or pooled across units and/or time periods – (e.g. 50 states measured annually over 30 years) – often labeled pooled time series data, time series cross sectional data, or panel data. Multilevel models are explicitly designed to analyze clustered or pooled data structures and can incorporate individual-level predictors, group-level predictors, and individual-by-group-level interactions. This course provides a general introduction to a variety of applications of multilevel modeling in the social sciences. Equal emphasis is placed on the underlying statistical model and on the estimation and interpretation of empirical data. The course explores methods for so-called robust standard errors in the face of clustered data, along with traditional methods for pooled time series and multi-level models. However, the primary goal is to demonstrate a general solution that encompasses these more narrowly focused approaches. We will consider models for continuous and categorical dependent variables. Time permitting, we will conclude with an introduction to Bayesian multi-level modeling. Each day will consist of a mixture of lectures and hands-on computer exercises and examples. We will include examples both in R and STATA. We will make substantial use of computer simulations to explore the statistical properties of multi-level models. Students should already be familiar with multiple regression and OLS. Some familiarity with Generalized Linear Models and Maximum Likelihood estimation is also helpful. No previous knowledge of Bayesian statistics is expected. Some prior exposure to R and STATA is helpful, but not required. The course will be taught at a level similar to the book Data Analysis Using Regression and Multi-level/Hierarchical Models, by Andrew Gelman and Jennifer Hill (2006), Cambridge University Press. Students are encouraged to bring their own data and projects with them to the class, as some lab time later in the course can be devoted to helping students address their specific needs.

    Fee: Members = $1500; Non-members = $3000

    For registration details, click here.

    Davis 219
    Dates: June 1-5, 2015
    Times: 9am - 5pm

    ICPSR - An Applied Introduction to Bayesian Analysis

    Jeff Harden

    The use of Bayesian methods has increased rapidly in many social science fields over the past two decades. Bayesian analysis is well-grounded in probability theory, can often better account for the complexity of social processes, can improve computational efficiency, and even allows researchers to systematically include their own expertise and/or qualitative evidence in the quantitative framework of a statistical model. This course will provide an introductory overview of Bayesian methods as they are applied to social science research. We will focus on the two complementary goals of learning the theory behind Bayesian inference as well as practical implementation of several common models in R. No prior knowledge of Bayesian methods is necessary. However, this is not simply a survey course. Students will walk away from the course with an understanding of how to apply Bayesian models to their own research as well as knowledge of what is going on “under the hood” with their results. Class time will be spent in lecture and working hands-on with example data in R.

    Fee: Members = $1300; Non-members = $2600

    For registration details, click here.

    Davis 219

    Dates: 8/3/15 - 8/5/15
    Times: 9:00am - 5:00pm

    ICPSR - Analyzing Social Networks: An Introduction

    Doug Steinley
    Network analysis focuses on relationships between or among social entities. It is used widely in the social and behavioral sciences, as well as in political science, economics, organizational studies, behavioral biology, and industrial engineering. The social network perspective, which will be taught in this workshop, has been developed over the last sixty years by researchers in psychology, sociology, and anthropology. The social network paradigm is gaining recognition in the social and behavioral sciences as the theoretical basis for examining social structures. This basis has been clearly defined and the paradigm convincingly applied to important substantive problems. However, the paradigm requires concepts and analytic tools beyond those provided by standard quantitative (particularly, statistical) methods. This five day workshop covers those concepts and tools. The course will present an introduction to concepts, methods, and applications of social network analysis drawn from the social and behavioral sciences. The primary focus of these methods is the analysis of relational data measured on groups of social actors. Topics include an introduction to graph theory and the use of directed graphs to study actor interrelations; structural and locational properties of actors, such as centrality, prestige, and prominence; subgroups and cliques; equivalence of actors, including structural equivalence, blockmodels, and an introduction to relational algebras; an introduction to local analyses, including dyadic and triadic analyses; and an introduction to statistical analyses, using models such as p1 and exponential random graph models. The workshop will use several common software packages for network analysis: UCINET, Pajek, NetDraw, and STOCNET

    Fee: Members = $1500; Non-members = $3000

    For registration details, click here.

    Davis 219

    Dates: 8/10/15 - 8/14/15
    Times: 9:00am - 5:00pm

    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.


    To register, click: here

    Davis 219
    Dates: 2/25/15, 3/4/15, 3/18/15, 3/25/15, 4/1/15, 4/8/15
    Time: 2pm - 4pm

    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. UNC students, faculty, and staff will need to show their UNC OneCard.


    Davis 247

    Date: March 3, 2015

    Time: 11:00am - 1:00pm

    Introduction to ArcGIS Software

    Philip McDaniel

    This course was originally scheduled for 2/23/15. It will now be held on 3/4/15, in Room 3010.

    This 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 3010
    Date: March 4, 2015

    Time: 2:00pm - 4:30pm

    ArcGIS: Mapping Census Data

    Philip McDaniel
    This 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 219
    Date: March 6, 2015

    Time: 2:00pm - 4:30pm

    ICPSR - Introduction to Spatial Regression Analysis

    Elizabeth Root
    Spatially-referenced data add important contextual and locational information to the social and behavioral sciences, such as sociology, anthropology, political science, economics and public health. A geographic or spatial-analytic framework, which will be taught in this workshop, can be used to explore the importance of spatial relationships in a variety of social and behavioral processes. However, spatial data and spatial relationships necessitate the use of analytic tools beyond those provided by standard statistical methods. This 5-day course introduces the spatial-analytic framework, and explores the range of issues that generally must be dealt with when analyzing spatial data. The role of spatial autocorrelation in spatial data sets is a central focus of this course. Throughout the course we will address the following questions: how and why does spatial autocorrelation arise; how is it measured and understood; how does it relate to issues of spatial heterogeneity and spatial dependence; and how should it inform the specification and estimation of regression models. Specific modeling techniques include: indices of spatial autocorrelation (Moran's I, Geary's C, LISA), spatial regression models (SAR and SARAR), and geographically weighted regression (GWR).

    The course is structured around a combined lecture format (mornings) and computing lab exercises (afternoons). The focus of the course is on spatial statistical analysis, not Geographic Information Systems (GIS). Hands-on application of statistical methods in afternoon lab sessions will enable participants to pursue a broad range of social and behavioral science research topics. Software emphasis will be given to GeoDa and R for exploratory spatial data analysis and modeling. Some acquaintance with this software is helpful but is not a prerequisite. Detailed R code will be provided and discussed in labs, and we will lean how to interpret, visualize and map model output.

    Prerequisites for maximizing learning in this course are a solid grounding in standard multivariate regression techniques and a minimal level of comfort with matrix notation and algebra.

    Fee: Members = $1300; Non-members = $2600

    For registration details, click here.

    Davis 219

    Dates: June 8 - 10, 2015

    Times: 9:00am - 5:00pm

    Survey Research

    Introduction to Focus Groups

    Emily Geisen
    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

    To Register, click here

    This 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 219
    March 05, 2015 9:00 AM - 4:30 PM

    Social Media's Role in Survey Research

    Joe Murphy

    This course covers the active use of social media throughout the survey lifecycle, including questionnaire design and testing, subject recruitment, respondent tracking and longitudinal panel retention, and use as supplementary or proxy data. It includes examples of analysis of social media data to supplement or as an alternative to survey research, highlighting the topics, methods, and ethical considerations that accompany this growing area of research. We end with considerations for the role of social media in public opinion research in the future as this area of research evolves.

    Examples of issues that will be discussed include:

    • defining social media for the purposes of determining its potential role within survey research
    • the motivation for tapping this source of behavioral and attitudinal measurement
    • the availability and quality considerations inherent in social media data analysis
    • current uses and evaluations of social media in research
    • the legal and ethical issues that must be considered when considering social media as a resource in research
    • challenges and questions on the road ahead in developing best practices for social media in survey research, including validation of social media data; addressing coverage, sampling, and differential access challenges; designing better integrations of surveys and social media; leveraging the unique features of social media; and continuing to refine the understanding and guidance on privacy and ethics.

      THE INSTRUCTOR

      Joe Murphy is Director of the Program on Digital Technology and Society within RTI International’s Survey Research Division. His research focuses on the development and application of new technologies and modes of communication to improve the survey research process. His recent work has centered on the use and analysis of social media to supplement survey data, with a detailed focus on Twitter. Mr. Murphy also investigates optimal designs for mobile data collection platforms, data visualization, crowdsourcing, and social research in virtual worlds. He is a demographer by training and survey methodologist by practice.


      Registration Fees:
    • CPSM Students - $20
    • UNC Students - $35
    • Other - $45

      To Register, click here

      This course will count as 4.0 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 219
      March 19, 2015 1:00 PM - 5:00 PM

      Designing and Conducting Surveys of Businesses and Organizations

      Diane Willimack
      This course provides an overview of methodological issues associated with the use of surveys to collect data from organizations. We will identify key differences between household surveys and organizational surveys, emphasizing organizational behaviors and attributes that affect survey response. We will demonstrate an approach to survey design that utilizes understanding and consideration of this organizational context when developing, adapting, and implementing data collection instruments and procedures. This course will include topics related to survey planning, questionnaire design and pretesting, data collection modes, and communication and response improvement strategies.

      This integrated approach to surveys of businesses and organizations is the subject of a new book in the Wiley Series in Survey Methodology, entitled Designing and Conducting Business Surveys, written by Ger Snijkers, Gustav Haraldsen, Jacqui Jones, and Diane K. Willimack.


      Registration Fees:

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

      To Register, click here
      This course will count as 7.0 CPSM short course credit hours.

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

      * 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 219
      April 09, 2015 9:00 AM - 4:30 PM

      Statistical Computing

      Introduction to R for Social Scientists

      Mark Yacoub

      This class was originally scheduled for February 16-17, 2015. Due to weather, it is NOW March 16 - 17, 2015. Room location is now changed to Davis Rm. 219

      This is a two-day course on R, an open-source programming language for statistical analysis and graphics. It provides the analyst with a wide variety of tools commonly used in statistical modeling with more flexible, objected-oriented facilities than other programs like Stata or SAS. This course is designed for those with little or no R experience. It will cover basic syntax and data loading, model estimation, loading and using written packages (including a sampling of popular packages), graphical presentation of model results, and Monte Carlo simulation. After completing the course you will know enough to be able to (1) conduct a typical statistical analysis for your own research and (2) search for the things you don't know in an efficient manner.

      No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.


      Davis 219
      Dates: Rescheduled to March 16-17, 2015
      Times: 2:30pm - 5:00pm

      MPlus

      Mplus is a modeling program that integrates random effect, factor, SEM and latent class analysis in both cross-sectional and longitudinal settings and for both single-level and multi-level data. As such, this short course will only scratch the surface of Mplus' capabilities. The basic structure of the program and how it can be modified will be taught in a hands-on way in the Odum Institute Computer Lab.

      Rescheduled from March 20, 2015. Will now be held March 27, 2015.

      No registration required. UNC students, faculty, and staff will need to show their UNC OneCard.


      Davis 219
      Date: March 27, 2015

      Time: 10:00am - 12:00pm