Education

Course Schedule

Short Courses Presented by the Odum Institute & the Research Hub @Davis Library

Academic Holiday

Memorial Day

Conference Room
May 29, 2017 9:00 AM - 5:00 PM

Qualitative Analysis

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 = $1500; Non-members = $2800

For registration details, click here.

Davis 3010
Dates: July 10 - 12, 2017

Times: 9:00am - 4:30pm

Qualitative Research Summer Intensive

Registration is now open through ResearchTalk. For more information and to register, click QRSI 2017

July 24-25 (Monday-Tuesday)

Two-day Courses

  • Coding and Analyzing Qualitative Data
  • Focus Groups: Tools for Inquiry, Pedagogy, and Social Advocacy/Activism
  • Foundational Principles of and Approaches to Mixed Methods Research
  • Introduction to Qualitative Research: From Principles to Practice
  • Oral History: Purpose, Praxis and Possibility
  • Qualitative Teamwork

July 26 (Wednesday)
One-day Courses

  • Building a Codebook and Writing Memos
  • Compassionate Interviewing
  • Doing Qualitative Research Online
  • Rapid Turn-Around Qualitative Research
  • Synthesizing Qualitative Data
  • Writing Stories: Researcher As Storyteller

July 27-28 (Thursday-Friday)
Two-day Courses

  • Doing Qualitative Research in the Era of Big Data: Basic Principles and Applications
  • Evocative Autoethnography: Writing Lives and Telling Stories
  • Implementation Research: Using Qualitative Research Methods to Improve Policy and Practice
  • Qualitative Research: Analyzing Life
  • "Sort and Sift, Think and Shift": Learning to Let the Data Guide Your Analysis
  • Writing Effective Qualitative and Mixed Methods Research Proposals

For more information, go to QRSI 2017


Carolina Inn
July 24, 2017 9:30 AM - 4:00 PM
July 25, 2017 9:30 AM - 4:00 PM
July 26, 2017 9:30 AM - 4:00 PM
July 27, 2017 9:30 AM - 4:00 PM
July 28, 2017 9:30 AM - 4:00 PM

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

For registration details, click here.

Davis 3010
Dates: August 2 - 4, 2017

Times: 9:00am - 4:30pm

Quantitative Analysis

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

Kenneth Bollen

A powerful method for analyzing longitudinal data is Latent Growth Curve Models (LGCM). LGCM allow each case in a sample to have individual trajectories ("latent curves" or "growth curves") representing change over time. In addition to mapping these trajectories, LGCM allow researchers to examine the determinants of these trajectories or to relate the trajectories of one variable to those of another. The approach to LGCM in this course draws on the strengths of structural equation models (SEM), and the primary goal is to introduce participants to the theory and application of LGCM. The course begins with a conceptual introduction to LGCM, a description of research questions that are well suited for the technique, and a review of SEM. The remainder of the course will cover the following topics: LGCM for a single variable with and without predictors of differences in trajectories; modeling nonlinear trajectories; the LGCM 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 LGCM.

Prerequisites: The workshop assumes that participants have prior training and experience with SEM software.

Fee: Members = $1700; Non-members = $3200

For registration details, click here.

Davis 219
June 05, 2017 9:00 AM - 4:30 PM
June 06, 2017 9:00 AM - 4:30 PM
June 07, 2017 9:00 AM - 4:30 PM
June 08, 2017 9:00 AM - 4:30 PM
June 09, 2017 9:00 AM - 4:30 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 three-day 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.

Prerequisites: Participants should be familiar with LGCMs. Familiarity with MPlus would be helpful but is not required.

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

For registration details, click here.

Davis 219
Dates: June 12 - 14, 2017

Times: 9:00am - 4:30pm

ICPSR - Machine Learning for the Analysis of Text As Data

Brice Acree
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, text reuse measurement, and statistical topic models. Each method will be illustrated with hands-on examples using R. 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. The R packages covered in this course include tm, lda, textreuse, glmnet and openNLP. One distinguishing focus of this course will be the use of text analytics for the reliable and valid development and testing of scientific theory. Most methods of text analysis have been developed with predictive or descriptive motivations. For each method we cover in the current course, we will review how the method has been and can be applied to draw theoretical inferences regarding processes surrounding text generation.

Fee: Members = $1700; Non-members = $3200

For registration details, click here.

Davis 219
Dates: June 19 - 23, 2017

Times: 9:00am - 4:30pm

ICPSR - Bayesian Latent Variable Analysis

Ryan Bakker

This workshop will focus on a variety of commonly used latent variable techniques from the Bayesian perspective. The Bayesian paradigm is, in many ways, superior to classic techniques for estimating models with latent variables. In this workshop we will cover the factor and IRT models and introduce a variety of new tools for estimating models with latent variables as either explanatory or outcome variables. Additionally, we will introduce a variety of graphical techniques for presenting results. While there is not an assumption that participants will be well-versed in Bayesian modeling, a basic understanding of the Bayesian framework will be beneficial.

Fee: Members = $1700; Non-members = $3200

For registration details, click here.

Davis 219
Dates: July 17 - 21, 2017

Times: 9:00am - 4:30pm

ICPSR - Statistical Graphics

William Jacoby

This workshop will cover strategies for obtaining visual displays of quantitative information. We will discuss ways to, quite literally, look at data and statistical models in pictorial form. This is important because graphical representations avoid some of the restrictive and simplistic assumptions that often are encountered in empirical analyses. The first day of the workshop will present graphical displays for univariate, bivariate, multivariate, and categorical data. The second and third days will be devoted to producing graphical displays using tools available in the R statistical computing environment.

The workshop will focus primarily on the "lattice" package, although we will also examine some of the other graphical packages that exist within R. No previous experience with R is necessary to take this workshop! The second day will begin with a brief session on "Just Enough R for Graphics." The instructor will provide a variety of substantive examples and datasets to illustrate the various graphical techniques that are available. But workshop participants are also encouraged to bring their own datasets for constructing graphical displays that are tailored to their particular needs and interests.

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

For registration details, click here.

Davis 219
Dates: July 31 - August 2, 2017

Times: 9:00am - 4:30pm

Spatial Analysis & Mapping

Applied Spatial Regression Analysis

Paul Voss

This 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 - $50
  • All Others - $100

    To Register, click here. No registrations will be accepted on or after March 27, 2017.

    * 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 27 & 29, 2017

    Times: 1:30pm - 4:00pm

    Survey Research

    Cognitive Interviewing: A Hands-On Approach

    Gordon Willis

    National Cancer Institute, National Institutes of Health
    Joint Program in Survey Methodology, University of Maryland/University of Michigan

    Cognitive interviewing has become a very popular method for pretesting and evaluating survey questionnaires. The current approach favored by Federal laboratories and private research institutions mainly emphasizes the use of intensive verbal probes that are administered by specially trained interviewers to volunteer respondents, often in a laboratory environment, to delve into the cognitive and socio-cultural processes associated with answering survey questions. Based on this information, the evaluator makes judgments about where questions may produce difficulties in a number of subtle ways, due to cognitive demands they impose, cultural mismatches, or other shortcomings. The short-course will cover the basic activities involved in arranging a cognitive testing project, and will focus on the specifics of how to conduct verbal probing. Although an introduction to theory and background perspective is included, the course will focus on the application and practice of cognitive interviewing techniques, as these are targeted toward both interviewer-administered (face-to-face or telephone) and self-administered (paper and web/internet) surveys. Participants will practice the conduct of cognitive interviews across modes, and will evaluate their results by judging where questions have failed, and what one might do to revise them. The course aims to provide a working familiarity with cognitive techniques, so that students will be able to begin conducting cognitive interviews on their own.

    THE INSTRUCTOR
    Gordon Willis is a questionnaire design and pretesting specialist with affiliations at the National Institutes of Health, the Uniformed Services University of the Health Sciences (USUHS), and the University of Maryland. 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." His research interests include 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.

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

    Registration Fees:

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

    To Register, click here. Registrations will not be accepted on or after April 4, 2017.


    * 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, 2017

    Times: 9:00am - 4:30pm

    Social Media's Role in Survey Research

    The ubiquity of social media in the world today presents new opportunities and challenges when it comes to social research. This course considers the use of social media in survey research. Throughout the survey lifecycle (questionnaire design and testing, subject recruitment, respondent tracking and longitudinal panel retention), social media platforms offer some new ways to reach respondents at a time when traditional methods have seen declining participation. Social media data can also be considered as supplementary or proxy data for surveys. This course will present specific examples of the use social media in survey research, highlighting the topics, methods, and ethical considerations that accompany this growing sub-discipline. 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 a senior survey methodologist at RTI International. 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. Registrations will not be accepted on or after April 9, 2017.

    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
    Date: Changed to April 13, 2017

    Times: 9:00am - 1:00pm

    Conducting Cross Cultural Surveys – Challenges and Best Practices

    Emilia Peytcheva

    This course will provide an introduction to survey research methods for designing multinational and multicultural surveys. It begins with an overview of the field of comparative surveys. This will summarize their history and discuss some unique design features and implementation challenges inherent in their design and implementation.

    The second section discusses quality and risk management frameworks for comparative surveys. It will present some tools for monitoring quality processes and outcomes, and will reference the new Guidelines for Comparative Surveys (ccsg.isr.umich.edu). The third section of the course focuses on issues in study design, considering organizational structure, data collection infrastructure and management, and cost and quality tradeoffs.

    The second half of the course addresses instrument design for comparative surveys. It opens with a discussion of issues in defining objectives, identifying constructs, developing questions, and monitoring design process quality that are particular to the field of comparative surveys. It will also cover some technical challenges in crafting the questions into a survey instrument; visual display of text in various languages, placement of response categories and instructions, use of color, screen density, and other features of contemporary survey instruments will be reviewed from a multilingual and multicultural context. The links between design and mode considerations are also covered. The course concludes with a module on question adaptation and translation focusing on the critical role that version production often plays.

    Examples will be drawn from demographic and social indicator surveys, attitudinal surveys, health and education surveys, and quality of life surveys.

    THE INSTRUCTOR
    Emilia 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.

    This course will count as 4.0 CPSM Short Course credit hours.

    Registration Fees:

  • CPSM Students - $30
  • UNC Students - $40
  • 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 219
    Date: October 5, 2017

    Times: 1pm - 5pm

    Issues in the Analysis of Complex Sample Survey Data

    Brady West
    This one-day short course will provide participants with an introductory overview of issues frequently encountered when conducting secondary computer analyses of data collected from sample surveys with complex multi-stage designs (e.g., PSID, NHANES, NCS), including design-based weight determination, software choice, and proper analysis methods. The workshop is not intended for participants looking to design a survey, but rather for participants who have a desire to analyze complex sample survey data. TOPICS:

    • Recognizing a sample with a complex design, and sampling error calculation models
    • Calculation of survey weights based on sample designs / non-response / post-stratification
    • Calculation of new weights for subgroups / longitudinal analyses
    • Weighted vs. unweighted analyses
    • Variance estimation, and calculation of correct confidence intervals for population parameters
    • Hypothesis Testing based on sample estimates
    • Design Effects
    • Software packages capable of complex sample survey data analysis
    • Common analysis methods (linear modeling, descriptive statistics), interpretation of results
    • Using weights in regression modeling, and model-based approaches
    • Examples using software programs to analyze real survey data

    THE INSTRUCTOR
    Brady T. West is a Research Associate Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research (ISR) on the University of Michigan-Ann Arbor campus. He also serves as a Statistical Consultant on the University of Michigan Consulting for Statistics, Computing, and Analytics Research (CSCAR) Team. He earned in his PhD from the Michigan Program in Survey Methodology in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures, now in its second edition (Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition, Chapman Hall/CRC Press, 2014), and he is a co-author of Applied Survey Data Analysis, also now in its second edition.

    Registration will open 60 days prior to the class date.


    CPSM Students: $40
    UNC Students: $65
    Other: $90


    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: 10/26/17

    Times: 9:00am - 4:30pm

    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.

    THE INSTRUCTOR
    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.

    Registration Fees:

  • CPSM Students - $40
  • UNC Students - $65
  • Other - $90

    Registration will open 60 days prior to the class date.

    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: 11/9/2017

    Times: 9:00am - 4:30pm

    Nonresponse from the Total Survey Error Perspective: An Overview

    Paul Biemer
    The Total Survey Error (TSE) paradigm embodies the best principles, strategies, and approaches for minimizing the survey error from all sources within time, costs, and other constraints that can be imposed on the survey. This approach can be viewed as resting on the four pillars of survey methodology: survey design, implementation, evaluation, and data analysis. This course provides an overview of the TSE paradigm as it applies to one critical source of error: nonresponse. Structured around these four pillars, the course presents the best methods and lessons learned for dealing with nonresponse in survey, data collection, data analysis and evaluation. The survey focuses particularly on the interactions of response mechanism with other error sources and how nonresponse interventions can lead to unintended consequences for TSE.

    THE INSTRUCTOR
    Paul 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.

    This class will be count for 4.0 CPSM short course credit hours.

    Registration Fees:

  • CPSM Students - $30
  • UNC Students - $40
  • 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 219
    Date: 11/16/17

    Times: 9:00am - 1:00pm

    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 normally 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: 3/27/2017 - 3/30/2017

    Times: 3:00pm - 5:00pm

    Other

    Data Wrangling

    Lorin Bruckner, Matt Jansen, Tim Ronan, Kayla Seiffert
    Do you have data that you need to clean up and manipulate? Want to learn R? This two-day workshop will help you start working with data in R. (Familiarity with R is not required.)

    Day 1 (March 25, 2017)

    • Basic data formatting and cleanup
    • Programming best practices
    • Basic R Programming and RStudio
    Day 2 (April 1, 2017)
    • Advanced R Programming
    • Data visualization with R’s ggplot2 library
    Register here
    Mitchell Hall Room 005
    March 25, 2017 9:00 AM - 4:00 PM
    April 01, 2017 9:00 AM - 4:00 PM