Education

Course Schedule

Academic Holiday

Spring Break Begins

Conference Room
March 14, 2016 9:00 AM - 5:00 PM

Qualitative Analysis

NVivo Hands-on Workshop (Part 1)

Paul Mihas

This session will allow participants to work through a textual document in the PC version of NVivo, a software program for coding textual data such as interviews, focus groups, and field notes. It combines editable text and multimedia capabilities with searching and linking, as well as theory building. Text files can also be linked to graphics or audio files. The program provides an attribute system which can be used for coding demographic variables. It supports visual models, including the specification of types of links between objects in the model.

No registration required.

For further information, please contact Paul Mihas.


Davis 3010
February 16, 2016 2:00 PM - 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

Fee: Members = $1500; Non-members = $2800

Registrations details through ICPSR to be announced at a later date.


Davis 3010
Dates: July 6 - 8, 2016

Times: 9:00am - 4:30pm

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 = $1500; Non-members = $2800

Registrations details through ICPSR to be announced at a later date.


Davis 3010
Dates: August 3-5, 2016

Times: 9:00am - 4:30pm

Quantitative Analysis

An Introduction to Conducting Experiments in the Social Sciences

Steven G. Buzinski
This one-day course will provide a basic introduction to conducting experimental research in the social sciences. With an eye towards pragmatics, this course will teach participants about the experimental life cycle in social science, from research question to experimental design and implementation. Topics covered will include forming theory-grounded hypotheses, an overview of experimental methods, writing & submitting IRB applications, and using Qualtrics online survey software for experimental research. Participants should bring a laptop computer and research ideas.

Registration Fees:

  • UNC Students - $25
  • UNC Staff/Faculty/Others - $50

    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 3010
    Date: March 11, 2016

    Times: 10:00am - 4:00pm

    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: A calculus-based course in statistical inference, linear models, and survival analysis.

    Registration fees:

  • UNC Students: $25
  • UNC Faculty/Staff/Other: $50


    To Register, click here

    If you have questions, please contact Paul_Mihas@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
    Dates: March 23, 2016 and March 30, 2016

    Times: 1:00pm - 4:00pm

    Introduction to Structural Equation Models (SEM)

    Nick Wagner

    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: March 28 - 29, 2016

    Times: 2:00pm - 3:30pm

    ICPSR - Machine Learning for the Analysis of Text As Data

    Bruce Desmarais
    Quantitative analysis of digitized text represents an exciting and challenging frontier of data science across a broad spectrum of disciplines. From the analysis of physicians' notes to identify patients with diabetes, to the assessment of global happiness through the analysis of speech on twitter, patterns in massive text corpora have led to important scientific advancements. In this course we will cover several central computational and statistical methods for the analysis of text as data. Topics will include the manipulation and summarization of text data, dictionary methods of text analysis, prediction and classification with textual data, document clustering, and statistical topic models. Each method will be illustrated with hands-on examples using R and Python. Participants will develop an understanding of the challenges and opportunities presented by the analysis of text as data, as well as the practical computational skills to complete independent analyses.

    Fee: Members = $1600; Non-members = $3000

    Registrations details through ICPSR to be announced at a later date.


    Davis 219
    Dates: May 24-27, 2016

    Times: 9:00am - 4:30pm

    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 = $1700; Non-members = $3200


    Registrations details through ICPSR to be announced at a later date.


    Davis 219
    Dates: June 6 - 10, 2016

    Times: 9:00am - 4:30pm

    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 = $1700; Non-members = $3200

    Registrations details through ICPSR to be announced at a later date.


    Davis 219
    Dates: June 13 - 17, 2016

    Times: 9:00am - 4:30pm

    Data Matters: Data Science Short Course Series

    Details TBA
    Friday Center
    June 20, 2016 9:00 AM - 5:00 PM
    June 21, 2016 9:00 AM - 5:00 PM
    June 22, 2016 9:00 AM - 5:00 PM
    June 23, 2016 9:00 AM - 5:00 PM
    June 24, 2016 9:00 AM - 5:00 PM

    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 = $1500; Non-members = $2800

    Registrations details through ICPSR to be announced at a later date.

    Davis 219
    Dates: July 13 - 15, 2016

    Times: 9;00am - 4:30pm

    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 = $1700; Non-members = $3200

    Registrations details through ICPSR to be announced at a later date.


    Davis 3010
    Dates: August 8-12, 2016

    Times: 9:00am - 4:30pm

    Survey Research

    Executing Your Survey Research Project

    Katie Clark and Teresa Edwards

    This workshop series will provide guidance to participants conducting survey research for their dissertation, thesis, or other project. Each week the workshop will focus on a topic and provide instruction, group discussion and an opportunity for participants to complete a worksheet or review handouts. The worksheets and handouts are tangible products that will help guide participants to execute their survey research. The series will provide information that can be applied to web or paper surveys. There are no prerequisites to this workshop series and participants are encouraged to bring any materials they have already developed for their project.

    Workshop 1, Jan 13: Creating a Timeline for Success and a Data Analysis Plan
    A comprehensive timeline helps the researcher set short and long-term goals to keep the project on track. The data analysis plan drives key decisions in survey design and implementation and should be developed at the beginning of the project. In this session participants will learn about important elements of these items and draft, review, and/or receive feedback on their own timeline and data analysis plan.

    Workshop 2, Jan 20: Participant Engagement and Data Management Plans
    This session focuses on ways to engage your participants to yield higher response rates and procedures to collect and manage survey data. How do the words and materials you use to recruit respondents affect cooperation rate and data quality? What options are available to recruit participants? What are the pros and cons of different contact and data collection modes (e.g. email, postal letter, phone calls, text messages. How will you store and manage your data once they are collected? In this session participants will draft, review, and/or receive feedback on their data collection protocol and data management plan.

    Workshop 3, Jan 27: Questionnaire Development
    Good question development is the heart of a quality survey. We will review principles for question development, what should be considered when moving from a paper to a web survey, and tips for using previously designed scales. Participants will draft, review, and/or receive feedback on their survey questions.

    Workshop 4, Feb 3: Qualtrics Overview
    Qualtrics is an online survey software program available free of charge to UNC students, faculty, and staff. A free trial version is available to the public. This session covers programming surveys, assigning variable names and code numbers, distributing survey invitations and reminders, and exporting data for analysis. Participants will also receive an overview of advanced Qualtrics functionalities.

    Workshop 5, Feb 10: Paper Surveys and Testing Surveys (web and paper)
    There is more to designing an effective paper survey than typing up your questions and selecting ‘Print.’ This session provides a brief tutorial on designing and distributing paper surveys, followed by discussion of the important steps of testing surveys. We will review testing methods including cognitive interviewing, usability testing, observation, data review, and piloting. Participants will draft, review, and/or receive feedback on their plans for testing their surveys.

    Workshop 6, Feb 17: Institutional Review Board and Protecting Human Subjects
    Most survey research in an academic setting requires approval from an Institutional Review Board (IRB) whose purpose is to protect the rights and well-being of research subjects. This session quickly reviews the purpose and function of an IRB and the UNC ethics training requirements for researchers before turning to the nuts and bolts of completing the UNC IRB Application for a survey project. Participants will receive detailed guidance on completing their own IRB application in ways that maximize efficiency and minimize processing delays.

    Workshop 7, Feb 24: Preparing Data for Analysis and Archiving
    This session focuses on data cleaning, preparation for analysis and archiving survey data for long-term preservation. Participants will learn how to keep a log for data that need to be cleaned, how to prepare data for commonly used analysis packages, and options for archiving data.

    If you have any questions, please contact Teresa Edwards at teresa_edwards@unc.edu or Katie Clark at kclark372@gmail.com

    Registration is now closed.


    * 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.


    Davis 219
    January 13, 2016 2:00 PM - 4:30 PM
    January 20, 2016 2:00 PM - 4:30 PM
    January 27, 2016 2:00 PM - 4:30 PM
    February 03, 2016 2:00 PM - 4:30 PM
    February 10, 2016 2:00 PM - 4:30 PM
    February 17, 2016 2:00 PM - 4:30 PM
    February 24, 2016 2:00 PM - 4:30 PM

    Multiple Imputation: Methods and Applications

    Jerry Reiter

    Multiple imputation offers a general purpose framework for handling missing data, protecting confidential public use data, and adjusting for measurement errors. These issues are frequently encountered by organizations that disseminate data to others, as well as by individual researchers. Participants in this workshop will learn how multiple imputation can solve problems in these areas, and they will gain a conceptual and practical basis for applying multiple imputation in their statistical work. Topics include the pros and cons of various solutions for handling missing data; the motivation for and general idea behind multiple imputation; methods for implementing multiple imputation including multivariate modeling, conditional modeling, and machine learning based approaches; methods for checking the adequacy of imputations via graphical display and posterior predictive checks; and applications of multiple imputation for scenarios other than missing data.

    Instructor Bio:
    Jerry Reiter is Professor of Statistical Science at Duke University. He also serves as Deputy Director of the Information Initiative at Duke. He received a PhD in statistics from Harvard University in 1999, and a BS in mathematics from Duke University in 1992. His main research areas include methods for handling missing data, for protecting confidentiality in public use data, and for integrating information from multiple sources. He is a Fellow of the American Statistical Association, and recipient of the Gertrude M. Cox Award and the Youden Award.

    Registration Fees:

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

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


    This class is now FULL with a waitlist. To be added to the waitlist, 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: February 11, 2016

    Times: 9:00am - 4:30pm

    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, 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

    * 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: March 3, 2016

    Times: 9:00am - 4:30pm

    Designing Multi-Item Scales

    Bob DeVellis
    This course provides in introduction to developing instruments with multiple items to measure a single construct. Examples include measures of various social and psychological variables that might be assessed in health, marketing, journalism, or other research areas. Participants will also be encouraged to suggest content areas for discussion. After a brief theoretical introduction, we will turn to practical issues such as when a multi-item scale is (or isn’t) appropriate, determining the number and content of items in the scale, what type and how many response options should be offered, whether scales should include both “negative” and “positive” items, whether the parts of a subscale should be grouped or scattered, and other common concerns in scale development. Dr. DeVellis will use real-life examples to demonstrate the scale development process. There will be ample opportunity for questions and discussion. Instructor: Robert DeVellis, Research Professor, UNC Gillings School of International Public Health and author of Scale Development: Theory and Applications 3rd ed. published 2012 by Sage Publications.

    This course will count as 4.0 CPSM short course credit hours.


    Registration Fees:

  • CPSM Students - $20
  • UNC Students - $35
  • Other - $45

    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: March 24, 2016

    Times: 12:30pm - 4:30pm

    Introduction to Focus Groups

    Emily McFarlane 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
    Date: April 7, 2016

    Times: 9:00am-4:30pm

    Statistical Computing

    SAS

    Chris Wiesen

    This 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 3010
    Dates: February 8 - 11, 2016

    Times: 3:00pm - 5:00pm

    MPlus

    Rosemary Russo
    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.

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


    Davis 219
    Date: February 16, 2016

    Times:10:00am - 12:00pm

    SPSS

    Brooke Magnus

    Part 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 3


    Davis 219
    Dates: Feb. 29, March 1, March 2, 2016

    Times: 2:00pm - 3:30pm

    Other

    Collecting and Analyzing Textual Data

    Kelsey Shoub
    This 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, formatting, and processing text. 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. Models in each of these families require varying amounts of information be provided. 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 R

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

    Davis 219
    Dates: February 8 and February 15, 2016

    Times: 2:00pm - 4:30pm

    Introduction to LaTeX

    Mark Yacoub

    This is a two-day course on LaTeX, an open-source markup language/document preparation system widely used in academia to produce high-quality typesetting. In addition to producing beautiful-looking documents, slideshows, and posters, LaTeX can make many features of the manuscript-writing process--the bibliography, insertion of figures and tables, and all those requirements that the Graduate School or journals require--quick and easy. This course is designed for those with little or no LaTeX experience. It will cover basic syntax, loading and using written packages (including a sampling of popular packages), graphics, style files, creating a bibliography, making slide shows and posters, and integrating LaTeX and output from statistical software like R or Stata.

    After completing the course you will know enough to be able to (1) pronounce "LaTeX" correctly, (2) create a basic document, slideshow, or poster, and (3) 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 3010
    Dates: February 10 - 11, 2016

    Times: 10:00am - 12:30pm