Education

Course Schedule

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 = $1300; Non-members = $2600

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

For registration details, click here.

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

For registration details, click here.

Davis 3010
Dates: August 3-5, 2016

Times: 9:00am - 4:30pm

Quantitative Analysis

ICPSR - Machine Learning for the Analysis of Text As Data

TBA
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

For registration details, click here.

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


For registration details, click here.

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

For registration details, click here.

Davis 219
Dates: June 13 - 17, 2016

Times: 9:00am - 4:30pm

Data Matters: Data Science Short Course Series

Data Matters is a week-long series of one and two-day courses aimed at professionals in business, research, and government. The short course series is sponsored by the National Consortium for Data Science in collaboration with RENCI and the Odum Institute for Research in Social Science at UNC-Chapel Hill. Organizations struggling to stay afloat in the data deluge, those grappling daily with large, complex data, and anyone who wants to capitalize on the opportunities of big data should consider attending the short course series.

Highly qualified instructors from across the country teach courses on topics such as information visualization, data curation, R, health informatics, open data, machine learning, social network analysis, and more.

For listing of course offerings and registration information, click here.

Friday Center
June 20, 2016 10:00 AM - 4:45 PM
June 21, 2016 10:00 AM - 4:45 PM
June 22, 2016 10:00 AM - 4:45 PM
June 23, 2016 10:00 AM - 4:45 PM
June 24, 2016 10:00 AM - 4:45 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

For registration details, click here.

Davis 219
Dates: July 11 - 13, 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

For registration details, click here.

Davis 3010
Dates: August 8-12, 2016

Times: 9:00am - 4:30pm

Introduction to Structural Equation Models (SEM)

Marie E. Camerota
TBA
Davis 219
Dates: Monday, September 19, and Wednesday, September 21, 2016

Times: 10:00am - 12:00 pm

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

    Registration will open 60 days before 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: September 8, 2016

    Times: 9:00am - 4:30pm

    Executing Your Survey Research Project

    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, Sept. 14: 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, Sept. 21: 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, Sept. 28: 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, Oct. 5: 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, Oct. 12: 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, Oct. 19: 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, Oct. 26: 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

    Registration will open 60 days prior to the first 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.


    Davis 219
    Dates: Sept. 14, 21, 28, Oct. 5, 12, 19, & 26, 2016

    Times: 2:00pm - 4:00pm

    Designing Web Surveys

    Mick Couper
    The focus of this course is on the design of Web survey instruments. The course will focus on the appropriate choice and design of input tools (e.g., radio buttons, check boxes, drop boxes, text fields), including new features in HTML 5 and additional tools such as sliders. The course will also address layout and formatting of the instrument, including alignment of questions and response options, typeface, background color, and the design of grids or matrix questions. The design implications of browser-based mobile Web surveys will also be addressed. The course will draw on empirical results from experiments on alternative design approaches as well as on practical experience in the design and implementation of Web surveys. The course will not address the technical aspects of Web survey implementation (such as hardware, software, or programming) and will also not focus on question wording, sampling, or recruitment issues. The course will equip participants with the knowledge needed to make appropriate Web survey instrument design choices.

    The Instructor: Dr. Mick P. Couper, from the University of Michigan and the Joint Program in Survey Methodology, is the leading authority on web survey design in the U.S. He is the author of Designing Effective Web Surveys (Cambridge, 2008), and co-author (with Roger Tourangeau and Frederick Conrad) of The Science of Web Surveys (Oxford, 2013), and has done extensive research on web survey design and implementation.

    Registration Fees:

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

    This course will count as 7.0 short course credit hours.

    Registration will open 60 days prior to class.

    * Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
    * Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.

    Davis 219
    Date: September 22, 2016

    Times: 9:00am - 4:30pm

    An Overview of Topics in "Big Data": Unpacking Data Science for Beginners

    Cliff Lampe

    This course is a one-day introduction to “Big Data” as method of conducting research. The course will cover a range of issues, including: • Characteristics of data that is collected through these techniques. For example, when is scale of data important, vs. the nonreactive nature of the data. • Common methods for obtaining datasets for “Big Data” • Epistemological approaches for using data, including the inductive nature of many data analytic techniques. • Comparison of data analytic techniques with other forms of research. • Exploration of a variety of tools that are commonly used in Big Data research. • Common analytical techniques in data science. People who take this course will be able to define the pros and cons of data science as a research method, understand common terms related to Big Data techniques, and identify research questions that are appropriate to these techniques. It’s impossible to give a very technical training in a one day class, so while we’ll cover where one can go to learn more, this class will not delve deeply into technical aspects of big data. Given the nature of the instructor’s research, the class will focus on data mined from social media sites, which is one of the most common sources for data analytic approaches. Any person with a solid background in research methods will benefit from this course.


    Instructor:

    Cliff Lampe is an Associate Professor in the School of Information at the University of Michigan. His work is on the effects of social media use by individuals, groups and organizations with a focus on positive outcomes. He publishes in the the fields of Human Computer Interaction, and Communication Science. In his research, Dr. Lampe has examined interaction on multiple social media platforms, and has frequently used “big data” techniques to study interactions on those platforms. With a background of research at the Institute of Social Research at Michigan, Dr. Lampe has also been recently collaborating on a series of projects that look at the comparison of data analytic techniques and survey measurement in terms of a variety of research goals.

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

    Registration Fees:

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

    Registration will open 60 days prior to the class date.


    * Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
    * Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.

    Davis 219
    Date: October 6, 2016

    Times: 9:00am - 4:30pm

    Introduction to Cognitive Interviewing

    Teresa Edwards
    This course will provide an overview of cognitive interviewing as a technique for developing and/or testing survey questions. We will briefly discuss participant recruitment and other planning details before turning to development of an interview guide, discussion of think-aloud and probing techniques, selection of probes, trade-offs of concurrent vs. retrospective probing, and how to choose the best techniques for particular situations. We will use demonstrations and exercises to give participants experience using the technique.

    Registration Fees:

  • CPSM Students - $20
  • UNC Students - $35
  • Other - $40
    This course will count as 7.0 short course credit hours.

    Registration will open 60 days prior to class.

    * Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
    * Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.

    Davis 219
    Date: October 25, 2016

    Times: 9:00am - 1:00pm

    New Technologies in Surveys

    Michael Link
    Rapid advancements in communications and database technologies are changing the societal landscape across which public opinion and survey researchers operate. In particular, the ways in which people both access and share information about attitudes, opinions, and behaviors have gone through perhaps a greater transformation in the last decade than in any previous point in history and this trend appears likely to continue. This course examines some of the research findings to date with respect to the use of mobile and social media platforms as vehicles for collecting information on attitudes, opinions and behaviors. For each area, we will explore current applications, known best practices, and cautions, including smartphones (for surveys, GPS, and visual data collection) and social network platforms (surveys and other forms of information). Examples will be provided from several topic areas, including assessment of political attitudes, health-related studies, and consumer research. The final section of the course delineates some of the more fruitful areas for on-going research to improve our understanding of these technologies and the role they can play in assessing public opinion.

    Registration Fees:

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

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

    Registration will open 60 days prior to the class date.

    * Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
    * Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.

    Davis 219
    Date: November 10, 2016

    Times: 9:00am - 4:30pm

    Weighting Survey Data

    Paul Biemer
    This course is an introduction to the basic concepts for weighting survey data. It begins by defining the goals of weighting including weighting as a correction for differential selection probabilities, non-response and non-coverage. The course covers the process of developing weights for stratified two-stage sampling including computing design weights and methods for nonresponse adjustments and frame coverage error adjustments. Additional topics may include the effect of weighting on variance of the estimates and extreme weights.

    INSTRUCTOR
    Paul P. Biemer is Distinguished Fellow, Statistics, at RTI International and Associate Director for Survey Research and Development for the Odum Institute at the University of North Carolina at Chapel Hill. He received his Ph.D. in Statistics from Texas A&M University and has taught at the University of Maryland (Joint Program in Survey Methodology), University of Michigan (Summer Institute) and George Washington University (Statistics Department). He was formerly Head of the Department of Experimental Statistics and Director of the Statistics Center at New Mexico State University and has also worked for the Bureau of the Census where he was Assistant Director for Statistical Research. His research has examined the relationships between survey design and survey error, statistical methods for assessing survey errors, particularly measurement errors and methods for the analysis of survey data. His articles have been published in numerous scholarly journals. His book, Introduction to Survey Quality, and several edited volumes including Measurement Errors in Surveys, have been published by John Wiley & Sons.

    Registration Fees:

  • CPSM Students - $20
  • UNC Students - $35
  • Other - $40
    This course will count as 7.0 short course credit hours.

    Registration will open 60 days prior to class.

    * Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
    * Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.

    Davis 219
    Date: November 17, 2016

    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 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: September 12 - 15, 2016

    Times: 11:00am - 1:00pm

    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: October 31 - November 3, 2016

    Times: 3:00pm - 5:00pm