Short Courses 2017-2018

Short Courses in Padova, Italy, July 16-21, 2018 – Two Sessions, Four Course Options

Hosted by University of Padova

Session 1: July 16-18 | Session 2: July 19-21

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses that complement one another. All courses in a session are taught concurrently, so a participant can take only one course per session.

Session 1 Monday July 16 (all day), Tuesday July 17 (all day), and Wed. July 18 (half day)	Session 2 Thursday July 19 (all day), Friday July 20 (all day), and Sat. July 21 (half day)
“Introduction to Research Methods I: Measurement, Design, and Analysis” – Dr. Larry Williams “Introduction to Multilevel Analysis” – Dr. Robert Vandenberg	“Qualitative Techniques: Case Method, Grounded Theory, Interviewing “ – Dr. Tine Koehler “Design and Analysis of Experiments” – Dr. Ron Landis

Session I: July 16-18, Two Course Options

Choose one of the following two course options to attend during Session I.

Option #1: “Introduction to Research Methods I: Measurement, Design, and Analysis” –
Dr. Larry Williams, University of Nebraska-Lincoln

Course Description

This short course provides an introduction to organizational and social science research. We begin with an empirical research model as a framework to discuss constructs, variables, and criteria for causality. We then consider various types of validity and techniques for their assessment, with an emphasis on construct validity and survey measures. Statistics for theory testing in experimental and non-experimental settings are presented, with an emphasis on experimental vs. statistical control. A series of assignments are used to illustrate course concepts, and basic analyses with SPSS are incorporated.

Required Software: SPSS (free trial version)

Option #2: “Introduction to Multilevel Analysis” –
Dr. Bob Vandenberg, University of Georgia

Course Description

The purpose of this short course is to provide in depth coverage of multilevel modeling using the MPlus statistical software package. The course is primarily introductory in nature. It starts with an overview of the conceptual underpinnings for undertaking multilevel studies in the first place. Examples of topics include aggregation issues, and similarity indices. Next, a considerable amount of time is spent on random coefficients multilevel modeling. There is a progression in the latter module from analyses used to test the assumptions for aggregation to complex ones involving mediation, cross-level interactions, and models in which there are variables only at the between and within levels of analyses. The examples illustrate both the random vector of means and of coefficients/slopes. None of the examples in this module are structural equation models using latent variables. The examples in this module incorporate observed variables only. About half of the time is spent by the instructor illustrating an example, and then the participants are given time to run models the other half of the time. Data will be provided, but the participant may want to bring his/her own data as well. Participants will be given a comprehensive handout with all the examples including syntax. Time permitting and if desired by the participants, the workshop will progress into structural equation multilevel modeling. The examples are the same as in the previous module except now most of the variables are modeled as latent variables.

Required Software: TBA

Session II: July 19-21, Two Course Options

Choose one of the following two course options to attend during Session II.

Option #1: “Qualitative Techniques: Case Method, Grounded Theory, Interviewing” –
Dr. Tine Koehler, University of Melbourne

Course Description

The purpose of this workshop is to provide an overview over some of the most often used qualitative research methods and techniques, i.e., case method, grounded theory, and interviewing. On the first two days, this course will provide attendees with an overview of the core tenets of case methodology and grounded theory method as well as with an introduction to different popular approaches to carry out these methodologies. Rather than advocating for one approach over others, we will discuss the strengths and weaknesses of several approaches and discuss their suitability to different research questions, settings, and samples. We will also discuss how these approaches may differ depending on the chosen ontology and epistemology of the researcher. On the third day, we will discuss interviewing as a data collection technique, specifically highlighting how interviewing has to be employed differently in different research paradigms and with different qualitative research methods.

Option #2: “Design and Analysis of Experiments” –
Dr. Ron Landis, Illinois Institute of Technology

Course Description

This course will cover fundamental topics in experimental design and analysis. Participants should have had at least one introductory statistical methods course. This short course will begin with a review of t-tests, p-values, confidence intervals, and the basics of regression and ANOVA. The course will then move to coverage of (a) fixed-effects, completely randomized designs and (b) advanced topics such as factorial designs, random effects, blocking, split-plots, and the incorporation of covariates. For all topics, we will discuss the strengths and limitations of various designs, appropriate analyses using data collected from different designs, how we check underlying assumptions, consider statistical power, and how we should report our results. There will be significant opportunities of application of content through exercises and discussion.

Registration, Pricing, Advanced Registration Deadline

To register for 2018 CARMA Short Courses in Padova, Italy, you must first log in to your CARMA account (If you do not already have an account, please sign-up as a website user). Once you have logged in, and you are in the User Area, select “Purchase Short Course” on the right side of the page.

Non-member prices per course: *All prices are in US Dollars (USD)
• Faculty/Professional: $800.00
• Students: $600.00
CARMA Member prices per course
• Faculty/Professional: $400.00
• Students: $300.00

If your organization is not yet a member but would like to become one, please contact us directly at carma@unl.edu.

All participants are eligible for the following discount:
Register for both sessions, receive $75 off the total price.

Advanced Registration Deadline is June 11, 2018. After this date, a $75.00 fee will be added to all registrations.

Accomodations

Hotel Information

Hotel	Internet Address	Address	Rating
Hotel Galileo	www.hotelgalileopadova.it	via Venezia, 30 (Fair district)	three stars
Hotel Donatello	www.hoteldonatello.net	via del Santo, 102/104 (near Basilica del Santo)	four stars
Hotel Al Santo	www.alsanto.it	via del Santo, 147 (near Basilica del Santo)	three stars
Hotel Giotto	www.hotelgiotto.com	Piazzale Pontecorvo, 33 (near Basilica del Santo)	three stars
Hotel NH	www.nh-hotels.com	Via G.B. Pergolesi, 24 (Fair district)	four stars
Hotel Europa	www.hoteleuropapd.it	Largo Europa, 9 (city centre)	three stars

University Residence Options

Short Courses in Detroit, Michigan, June 2018 – Two Sessions, Twelve Course Options

Hosted by Wayne State University

Session 1: June 4-6, Six Course Options | Session 2: June 7-9, Six Course Options

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses that complement one another. All courses in a session are taught concurrently, so a participant can take only one course per session.

Complete Course Listing

Session 1 Monday June 4 (all day), Tuesday June 5 (all day), and Wed. June 6 (half day)

Session 2 Thursday June 7 (all day), Friday June 8 (all day), and Sat. June 9 (half day)

“Introduction to Meta-Analysis” – Dr. Ernest O’Boyle, Indiana University “Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling” – Dr. Robert Vandenberg “Introduction to Multilevel Analysis” – Dr. James LeBreton “Introduction to R” – Dr. Scott Tonidandel “Intro to Big Data and Data Mining with R” – Dr. Jeff Stanton “Intermediate Regression: Multivariate/Logistic, Mediation/Moderation” – Dr. Ron Landis

“Introduction to Structural Equation Methods” – Dr. Larry J Williams “Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions” – Dr. Robert Vandenberg “Advanced Multilevel Analysis” – Dr. James LeBreton “Multivariate Statistics with R” – Dr. Steve Culpepper “Analysis of Big Data” – Dr. Fred Oswald “Advanced Regression: Alternatives to Difference Scores, Polynomial and Response Surface Methods” – Dr. Jeff Edwards

Session 1: June 4-6, Six Course Options

Option #1: “Introduction to Meta-Analysis” –
Dr. Ernest O’Boyle, Indiana University

Course Description

This course provides the participant with knowledge concerning the major meta-analysis models used in research in organizational science and other sciences. The course also details all steps in conducting a systematic review. Thus, this course is not solely a statistics/methods course but provides the participant with knowledge needed to conduct a meta-analysis and systematic review consistent with the Meta­Analysis Reporting Standards (MARS). Free software is made available to the participants and hands-on practice in the software is incorporated into the course. The course also addresses emerging topics in meta-analysis and systematic reviews including meta-regression, meta-structural equation modeling, and publication bias.

Required Software: R and Microsoft Excel

Option #2: “Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling” – Dr. Robert Vandenberg, University of Georgia

Course Description

The short course covers three advanced structural equation modeling (SEM) topics: (a) testing measurement invariance; (b) latent growth modeling; and (c) evaluating reciprocal relationships in SEM. The instructor uses the Mplus SEM software package throughout the workshop. To get maximum benefit from this short course, the participants should have the full version of Mplus loaded on their laptops
and bring the laptop with them to the course. The instructor lectures about half of the time with the remaining time devoted to having participants run examples with actual data provided by the instructor. Participants go home with usable examples and syntax. The measurement invariance testing section focuses on the procedures as outlined in the Vandenberg and Lance (2000) Organizational Research Methods article. Namely, we will cover the 9 invariance tests starting with the tests of equal variance-covariance matrices and ending with tests of latent mean differences. Other outcomes of covering these tests are how to use Mplus syntax, how to do multi-sample analyses, and also how to test hypothesized (a priori) group mean differences but using the latent means of the latent variables within each group. Thus, the first section accomplishes much more than the just the measurement invariance tests. The workshop then advances to operationalizing latent growth models within the SEM framework. Essentially, this is how to use one’s longitudinal data to actually capture the dynamic processes in one’s theory. Thus, it is very, very different than cross-sectional tests where one is stuck in one point in time. Again, this is what goes on at the surface level, but the participant will also be exposed to modeling how the change in one variable impacts change in another. We will also use mixed modeling. And at the end of it, I introduce the participants to latent profile modeling with latent growth curves. The final piece is the testing of models with feedback loops via an SEM-Journal article by Edward Rigdon (1995). We will go through his 4 different models and what they mean.

Required Software: MPlus (order the full version, try the free demo version)

Option #3: “Introduction to Multilevel Analysis” – Dr. James LeBreton, Pennsylvania State University

Course Description

The CARMA Introduction to Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct basic multilevel analyses. Emphasis will be placed on techniques for traditional, hierarchically nested data (e.g., children in classrooms; employees in teams). The first part of the course introduces issues related to multilevel theory (e.g., multilevel constructs; principles of multilevel theory building; cross-level inferences and cross-level biases). The second part of the course discusses issues related to multilevel measurement (e.g., aggregation; aggregation bias; composition and compilation models of emergence; estimating within-group agreement). The last part of the course focuses on the specification of basic 2-level models (e.g., children nested in classrooms; soldiers nested in platoons; employees nested within work teams) analyzed via multilevel regression (i.e., random coefficient regression; hierarchical linear model; mixed effects model). The R software package will be introduced, explained, and emphasized during this short course in preparation for the advanced short course offered in Session II. Participants who prefer HLM, SAS, SPSS, or MPlus (and have expertise with these programs) have the option of completing some assignments with these programs. Participants are encouraged to also bring datasets to the course and apply the principles to their specific areas of research. The course is best suited for faculty and graduate students who are familiar with traditional (i.e., single-level) multiple regression analysis, but have little (if any) expertise related to conducting multilevel analyses.

• Module 1: Multilevel Theory: Constructs, Inferences, and Composition Models
• Module 2: Multilevel Measurement: Aggregation, Aggregation Bias, & Cross-Level Inference
• Module 3: Multilevel Measurement: Estimating Interrater Agreement & Reliability
  • Examples using R
  • Examples using SPSS Software (time permitting)
• Module 4: Multilevel Measurement and Multilevel Modeling: A Simple Illustration of Analyzing Composite Variables in Hierarchical Linear Models
  • Examples using R
  • Examples using SPSS Software (time permitting)
• Module 5: Review of the 2-Level Model and Final Q & A

• Other topics (only if time permits) might include:
  • Extension of the 2-level model to the study of growth and change (i.e., growth model)
  • Different centering/scaling stragies (e.g., group-mean centering vs. grand-mean centering)

Required Software: R (
download here
), RStudio (
download here
)

Option #4: “Introduction to R” – Dr. Scott Tonidandel, Davidson College

Course Description

This course will provide a gentle introduction to the R computing platform and the R-Studio interface. We will cover the basics of R such as importing and exporting data, understanding R data structures, and R packages. You will also learn strategies for data manipulation within R (compute, recode, selecting cases, etc.) and best practices for data management. We will work through examples of how to conduct basic statistical analyses in R (descriptive, correlation, regression, T-test, ANOVA) and graph those results. Finally, we will explore user-defined functions in R and lay the groundwork for understanding how to perform more complex analyses presented in later CARMA short courses.

Required Software: R (download here), R Studio (download here)

Option #5: “Intro to Big Data and Data Mining with R” – Dr. Jeff Stanton, Syracuse University

Course Description

Big data has been a buzzword for several years both in academia and industry. Although the term is vague and is certainly overused, it does encompass some interesting new ideas and unfamiliar analytical techniques. Notable among these is “data mining,” a family of analytical methods for clustering, classifying, and predicting that go a step beyond the statistical methods used by many social science researchers. In this short course, we will discuss the dimensions of big data and the conceptual steps involved in data mining. Students are welcome to bring their own data sets for experimentation on their own, but this is not required.

We will use the open source statistical processing language, R, for most of the work we do in the course. Extensibility is the hallmark of R; its system of add-on packages provides access to an unequaled range of analytical tools and techniques. You do not have to be an expert in R to take this course, although you will find the course easier if you also take the introduction to R offered by CARMA earlier in the week. Prior to the course, I will ask students to install R on their personal computers and review the first few chapters of my free eTextbook, An Introduction to Data Science. Depending on the interests and preferences of the students, we also use the Rattle or R-Studio graphical user interfaces.

The ideal student will have an interest in using R, knowledge of some basic descriptive and inferential statistics, and some curiosity about exploring alternative, empirically driven strategies for analysis of large data sets. No prior experience with data mining is required and students who participate successfully in this short course can expect to learn enough about data mining to begin experimenting with these tools in research or teaching.

Required Software: R (download here), R Studio (download here)

Option #6: “Intermediate Regression: Multivariate/Logistic, Mediation/Moderation” – Ron Landis, Illinois Institute of Technology

Course Description

This short course will begin with a brief review of linear regression, followed by consideration of advanced topics including multivariate regression, use of polynomial regression, logistic regression, and the general linear model. We will pay particular attention to using regression to test models involving mediation and moderation. For all topics, examples will be discussed and assignments completed using either data provided by the instructor or by the short course participants.

Required Software: R (download here) or SPSS (free trial version)

Session 2: June 8-10, Six Courses

Option #1: “Introduction to Structural Equation Methods” –
Dr. Larry J Williams, University of Nebraska

Course Description

The Introduction to Structural Equation Methods Short Course provides (a) introductory coverage of latent variable techniques, including confirmatory factor analysis and structural equation methods with latent variables, (b) discussion of special issues related to the application of these techniques in organizational research, and (c) a comparison of these techniques with traditional analytical approaches. This Short Course will contain a balance of lecture and hands-on data analysis with examples and assignments, and emphasis will be placed on the application of SEM techniques to organizational research problems. Participants will:

• develop skills required to conduct confirmatory latent variable data analysis, based on currently accepted practices, involving topics and research issues common to organizational research.
• learn the conceptual and statistical assumptions underlying confirmatory latent variable analysis.
  learn how to implement data analysis techniques using software programs for confirmatory modeling. Special emphasis will also be placed on the generation and interpretation of results using the contemporary software programs LISREL, MPlus, and LAVAAN.
• learn how latent variable techniques can be applied to contemporary research issues in organizational research.
• learn how the application of current latent variable techniques in organizational research differs from traditional techniques used in this literature
• complete in-class exercises using their preferred package (LISREL, MPlus, and LAVAAN)

Required Software: LISREL (free trial edition), MPlus or LAVAAN

Note: For those without current access to an SEM package, LISREL has a free trial edition that you should download no earlier than 1 week before class. The MPlus demo is not adequate for this course. LAVAAN is implemented with R, which is free.

Option #2: “Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions” – Dr. Robert Vandenberg, University of Georgia

Course Description

The workshop covers three advanced structural equation modeling (SEM) topics: (a) multilevel modeling; (b) latent interactions; and (c) dealing with missing data in SEM applications. The instructor uses the Mplus SEM software package throughout the workshop. To get maximum benefit from this short course, the participants should have the full version of Mplus loaded on their laptops
and bring the laptop with them to the course. The instructor lectures about half of the time with the remaining time devoted to having participants run examples with actual data provided by the instructor. Participants go home with usable examples and syntax. The multilevel modeling section starts out using observed variables only, and no latent variables. Parallels are drawn in this approach and the other packages such as HLM. The main purpose here, though, is to teach participants the basics of multilevel modeling such as aggregation, cross-level interactions and cross-level direct effects. The workshop advances to using latent variables in a multi-level environment. Particular focus will be on multilevel confirmatory factor analysis whereby separate measurement models are estimated at both the within and between levels. The topic then switches to multilevel path modeling with emphasis on between vs. within modeling, and the estimation of cross-level interaction and direct effects among latent variables. The latent interaction section focuses on specifying interactions among latent variables in SEM models. This section starts out with a review of basic interaction testing within a regression environment (using Mplus). From this foundation, participants will move into specifying interactions among latent variables and how to test hypotheses with interactions. And from this point, the workshop will move into moderated-mediation but from the SEM perspective. The final segment of the short course deals with missing data. A great deal of time at the beginning is spent on missing data patterns and why they occur. The workshop then moves into the old methods of dealing with missing data such as listwise and pairwise deletion, and mean or regression based imputation. The disadvantages of those methods are discussed. We then move into covering the newer methods for dealing with missing data, multiple imputation, and full information maximum likelihood. Participants will be showed how to utilize the latter methods in Mplus.

Required Software: MPlus (order the full version, try the free demo version)

Option #3: “Advanced Multilevel Analysis” – Dr. James LeBreton, Pennsylvania State University

Course Description

The CARMA Advanced Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct more advanced multilevel analyses. Emphasis will be placed on techniques for longitudinal data. The R software package will be introduced, explained, and used throughout this short course. The topics covered in this course include specifying and analyzing basic, 2-level, models (e.g., individuals nested in teams; repeated observations nested in individuals), as well as, more advanced 3-level models (e.g., individuals nested in teams that are nested in organizations; repeated observations nested in individuals that are nested in teams). Other topics include: multilevel mediation and the analysis of dyadic data. Exercises using real-world data, are conducted in R. Participants who prefer HLM, SAS, SPSS, or MPlus (and have expertise with these programs) will have the option of completing some assignments with these programs. Participants are encouraged to also bring datasets to the course and apply the principles to their specific areas of research. The course is best suited for faculty and graduate students who have at least some foundational understanding of issues related to multilevel data and how to analyze simple, 2-level, models.

• • Module 1: 2-Level Mixed Models: Cross-Level Main Effects & Interactions
    • Examples using R
  • Module 2: Analyzing Change and Growth: 2-Level Growth Model
    • Examples using R
  • Module 3: 3-level Models
    • Examples using R
  • Module 4: Multilevel Mediation
    • Examples using R
  • Module 5: Analyzing Dyadic Data
    • Examples using R
    • Other topics (only if time permits) might include:
      • Multilevel Models for Non-Normal Outcome Variables
      • Bayes Estimates in R
      • Discontinuous Growth Models

Required Software: R (
download here
), RStudio (
download here
)

Option #4: “Multivariate Statistics with R” – Dr. Steven Culpepper, University of Illinois Urbana-Champaign

Course Description

This course continues the introduction to R from the first session by covering advanced topics related to multivariate statistics. We will cover topics related to data management for multivariate data and will provide an overview of plotting and visualizing multivariate data in R. Specific learning outcomes include learning how to conduct analyses involving:

• • • Multiple regression and diagnostics
    • Exploratory factor analysis and principal components
    • Multivariate regression, canonical correlation, and MANOVA
    • Topics in statistical computation (e.g., bootstrapping, Monte Carlo simulation)
    • Structural equation modeling with the lavaan package
    • Reproducible research for quantitative reports

The session will provide participants with some discussion of necessary background knowledge and practical exercises.

Required Software: R (download here), R Studio (download here), and tex (for Windows:
https://miktex.org/
, for OS X https://www.tug.org/mactex/, for Ubuntu/Debian (Linux): apt-get install texlive or
https://www.tug.org/texlive/
)

Option #5: “Analysis of Big Data” – Dr. Fred Oswald, Rice University

Course Description

This short course provides students with hands-on skills for developing and running predictive models for relevant to ‘big data’ in organizations. A range of predictive models will be covered: e.g., lasso and elastic net regression, random forest, stochastic gradient boosted trees, and support vector machines. R and all required R packages need to be set up on your laptop beforehand; the instructor will provide set-up instructions and guidance in advance; other data, materials, and assignments will be provided by the instructor (code, files).

Required Software: TBA

Option #6: “Advanced Regression: Alternatives to Difference Scores, Polynomial and Response Surface Methods” – Dr. Jeff Edwards, University of North Carolina

Course Description

For decades, difference scores have been used in studies of fit, similarity, and agreement in organizational research. Despite their widespread use, difference scores have numerous methodological problems. These problems can be overcome by using polynomial regression and response surface methodology to test hypotheses that motivate the use of difference scores. These methods avoid problems with difference scores, capture the effects difference scores are intended to represent, and can examine relationships that are more complex than those implied by difference scores.

This short course will review problems with difference scores, introduce polynomial regression and response surface methodology, and illustrate the application of these methods using empirical examples. Specific topics to be addressed include: (a) types of difference scores; (b) questions that difference scores are intended to address; (c) problems with difference scores; (d) polynomial regression as an alternative to difference scores; (e) testing constraints imposed by difference scores; (f) analyzing quadratic regression equations using response surface methodology; (g) difference scores as dependent variables; and (h) answers to frequently asked questions.

Required Software: SPSS (free trial version) or STATA

Registration/Pricing/Deadlines

To register for 2018 CARMA Short Courses at Wayne State University in Detroit, you must first log in to your CARMA account (If you do not already have an account, please sign-up as a website user). Once you have logged in, and you are in the User Area, select “Purchase Short Course” on the right side of the page. You will then be brought to a page in which you can select your course(s) and continue on to pay for them.

Pricing Dates *		Non-Member 1 Course	Non-Member 2 Courses ***	Member 1 Course **	Member 2 Courses ** | ***
Advanced Registration 01/20/18 – 03/09/18	Faculty	$800	$1,525	$400	$725
Advanced Registration 01/20/18 – 03/09/18	Student	$600	$1,125	$300	$525
Normal Registration 03/10/18 – 05/04/18	Faculty	$900	$1,725	$500	$925
Normal Registration 03/10/18 – 05/04/18	Student	$700	$1,325	$400	$725
Late Registration 05/05/18 – 06/04/18	Faculty	$975	$1,800	$575	$1,000
Late Registration 05/05/18 – 06/04/18	Student	$775	$1,400	$475	$800

* – To receive these prices, you must complete your registration during the dates specified.

** – Member prices reflect a 50% discount that you receive if you are student/faculty at an organization that is a CARMA member.

*** – These prices reflect a discount in which you register for 2 courses and receive $75 off.

Find out if your organization is a CARMA Consortium Webcast Member. (US and Canada institutions only).

If your organization is not yet a member but would like to become one, you can find purchasing and renewal information here.

Lodging: On-Campus Housing

CARMA Short Course participants have the option to pay a fee and stay on-campus in a Wayne State University residence hall for the duration of their Short Course(s). The residence hall is within 2 blocks of the Prentis Building (where all Short Courses are held), and is a safe and affordable option. Rooms are available for under $25 a night. View photos of the housing facilities as well as room features.

Length of Stay Information:

Note that the following dates are NOT flexible. If you will be arriving in Detroit prior to Sunday June 4 or staying later than the dates outlined below, you will be responsible for your own accommodations.

	Check-In	Check-Out	Length of Stay
Session I Attendees	Sunday, June 3	Wednesday, June 6	3 nights
Session II Attendees	Wednesday, June 6	Saturday, June 9	3 nights
Both Session Attendees	Sunday, June 3	Saturday, June 9	6 nights

Price Information:

Your price will depend on 1) if you are staying for one session or two sessions 2) if you want a private room or a shared room with another individual.

	One Session (3-Night Stay)	Both Sessions (6-Night Stay)
Individual Room	$99.32 per person	$175.76 per person
Shared, Two Person Room	$82.16 per person	$141.44 per person

Included with purchase of room: access card and linen packet (1 pillow, 1 blanket, 1 sheet set, 1 bath towel, 1 washcloth)

FIRM Deadline for On-Campus Housing Registration:

All reservations MUST be made by May 14, 2018. No additions or cancellations can be made after this date.

Note: In previous years, we have been able to add latecomers to the housing roster. Due to a change in
WSU’s Residence Hall management company, this IS NO LONGER POSSIBLE. If you register for courses past the housing registration
deadline you will have to find a different method of accommodation, there will be no exceptions.

Housing Cancellation Policy:

If an emergency arises and you are unable to attend, we will refund you the full amount you paid for housing as long as
we are notified prior to May 14, 2018. The money we receive for housing registration is given directly to
Wayne State University therefore we do not have the ability to issue a refund after this date.

How to Reserve a Residence Hall Room:

1. 1. 1. Login to your CARMA Website User Account, or create one.
      2. Select “Purchase Short Course” on right side of webpage
      3. Select “On-Campus Housing, Wayne State University” from drop-down menu
      4. For Session I, Session II, or both Sessions, select either an Individual Room or Shared Room
         • If purchasing shared room, enter in the discount code FOR EACH Session you select to adjust the price: 2018WSU
         • If you have a specific roommate request: BOTH participants need to purchase housing within their own CARMA website user accounts,
           and both need to email carma@unl.edu stating who their requested roommate is.
      5. Continue on to pay

Hotel Accommodations

If you would prefer to stay in a hotel, we have a few recommendations for you:

• • The Inn on Ferry Street. This is a boutique hotel located just 3 blocks from where instruction will be held on campus. It is located in a historic district in Detroit. Click here for more information. Note: There is a limited set of rooms available at the Inn on Ferry Street. Therefore we recommend you make a reservation immediately if you would like to stay there.
  • There are other lodging options located in Downtown Detroit, about two or three miles from Wayne State University (the site of the CARMA short courses). CARMA is not working with or endorsing these hotels, we are simply recommending them based on their proximity and the fact that they offer complimentary shuttle services to Wayne State University.
    • 
      Doubletree Suites by Hilton Hotels Detroit Downtown – Fort Shelby
      
    • 
      Holiday Inn Express Detroit Downtown