Past Short Courses

CARMA Classroom

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

Padova logo

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)

  1. "Introduction to Research Methods I: Measurement, Design, and Analysis" - Dr. Larry Williams
  2. "Introduction to Multilevel Analysis" - Dr. Robert Vandenberg
  1. "Qualitative Techniques: Case Method, Grounded Theory, Interviewing " - Dr. Tine Koehler
  2. "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.
Williams

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)


Vandenberg

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.
Koehler

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.


Landis

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

Qualitative Courses in Boston, Massachusetts, June 2018 - Two Sessions, Six Course Options

Hosted by Boston College

Session 1: June 11-13, Three Course Options | Session 2: June 14-16, Three Course Options

Complete Course Listing

Session 1

Monday June 11 (all day), Tuesday June 12 (all day), and Wed. June 13 (half day)

Session 2

Thursday June 14 (all day), Friday June 15 (all day), and Sat. June 16 (half day)

  1. "Introduction to Qualitative Methods/Ethnography" - Dr. Michael Pratt
  2. "Producing Discovery in Coding Qualitative Data" - Dr. Karen Golden-Biddle
  3. "Interviewing for Qualitative Research" - Dr. Ashley Mears
  1. "Advanced Qualitative Analysis" - Dr. Rhonda Reger
  2. "Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS)" - Dr. Anne Smith
  3. "The Craft of Inductive Qualitative Research" - Dr. Michel Anteby

Session 1: June 11-13, Three Course Options

Option #1: "Introduction to Qualitative Methods/Ethnography" - Dr. Michael Pratt, Boston College

Micheal Pratt

Course Description

The purpose of this workshop is to provide an introduction to qualitative methods by examining ethnography. Ethnographic approaches involve both study design and analysis, which makes them ideal for a beginner's class. However, where applicable we will also discuss parallels with case studies and grounded theory. The course will be comprised of three major sections: (a) designing a qualitative study; (b) skill building, including interviews, observation, and data analysis; and (c) writing and publishing your qualitative research. The course will combine readings, “tales from the field” / discussions regarding the unique tensions and challenges of doing qualitative/ ethnographic research, and hands-on learning. Participants are invited to bring samples of their own data to the session. However, no experience with qualitative methods is required prior to taking this course.

Required Software: None


Option #2: "Producing Discovery in Coding Qualitative Data" - Dr. Karen Golden-Biddle, Boston University

Karen Golden-Biddle

Course Description

This CARMA Short Course concerns the theory and craft of producing discovery in the analysis of qualitative data. The course will be conducted as a studio in which we work together to hone our understanding and analytic practice. It is comprised of three main sections, integrated across our 2½ days together:

  • Theory of discovery as abductive process. We will review recent work in organizational studies that conceives discovery as an abductive process and argues that the exclusive use of inductive (or deductive) logic in methodological practice is not yielding new ideas in our theorizing.
  • Craft of producing discovery in published qualitative analyses. We will closely examine how a few organizational researchers have produced discovery in qualitative analyses as articulated in their written accounts of methods in journal publications.
  • Producing discovery in our analyses of qualitative data. We will practice the doing of discovery in coding through our collective coding of a common data set. In addition, we will use our time together to discuss specific challenges in producing discovery in participants’ own data analyses. Although focused on individuals’ work, understanding actual challenges provide beneficial learning for the full community.
  • Required Software: None


    Option #3: "Interviewing for Qualitative Research" - Dr. Ashley Mears, Boston University

    Course Description

    Interviewing is a common method in sociology, and it is gaining popularity in business management, marketing, and health services research for the access it grants into people’s subjective experiences, meaning-making and accounting processes, and unspoken assumptions about social life. This workshop aims to provide an introductory “how to” of interview research, and in the process we examine the epistemology, conduct, and politics of qualitative methods. We will discuss interview projects from beginning to end, starting with the formation of the research question, orientation to theory, the nuts and bolts of sampling and conducting interviews, data analysis, and writing up results, in addition to ethical and practical considerations. We consider the benefits and limitations of interview methods across disciplines, and within sociology in particular, both long-standing and current debates on interview methodology. The course emphasizes hands-on learning through engagement with students’ own interests in interview projects, with an eye towards successful strategies for publishing interview research in top scholarly journals.

    Required Software: None


    Session 2: June 14-16, Three Course Options

    Option #1: "Advanced Qualitative Analysis" - Dr. Rhonda Reger, University of Missouri

    Rhonda Reger

    Course Description

    In this course, students will be exposed to research methods currently used in macro-level management fields, specifically in strategic management, organization theory and entrepreneurship. This course assumes limited prior knowledge of qualitative methods, but will still provide a deep grounding in several advanced qualitative methods and text analysis as applied in management research. Methods covered include comparative case study research, content analysis, discourse analysis, rhetorical analysis, sentiment analysis (also called tenor or tone analysis), and the construction of dictionaries. The course will be interactive with discussion of exemplar papers that showcase each of these methods. Students will also be given the opportunity to “pilot test” the methods by interviewing each other and content analyzing a small sample of text. A focus of this workshop will be on matching methods to research questions and the interests and strengths of the research team.

    Required Software: LIWC2015 (30 day rental available for $9.95; purchase for $89.95 from Linguistic Inquiry and Word Count)


    Option #2: "Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS)" - Dr. Anne Smith, University of Tennessee, Knoxville

    Course Description

    Analyzing textual data can be approached inductively or deductively, depending on the selected methodological approach of the research project. In this workshop, we will discuss and undertake hands-on text analysis exercises. Top down or a more confirmatory approach to text analysis will cover topics such as: dictionary application (e.g., Zavyalova, et al., 2016 & LIWC dictionary; Short et al., 2009); dictionary creation (Franco, Alexander, & Smith, working paper; Short et al., 2010); template utilization (Crabtree & Miller, 1999; King, 2004); and collocation analysis (Gephart, 1997). Students will be working with textual data to explore these techniques. Bottom up or more exploratory approach will include an in vivo, manual coding exercise and a demonstration of coding techniques using computer aided qualitative data analysis software (CAQDAS). No prior knowledge of software or text analysis is required.

    Required Software: CAQDAS


    Option #3: "The Craft of Inductive Qualitative Research" - Dr. Michel Anteby, Boston University

    Course Description

    Qualitative inductive research projects are like gems that need polishing and the craft of polishing them to uncover a “theoretical contribution” can partly be learned. This course is designed to help participants polish their “gems” in the making and sharpen their emerging contributions. The common denominator for participants is that they be engaged in research projects reliant primarely on qualitative data (e.g., archives, interviews, and/or field observations). Each participant must be prepared to share a draft analytical field memo, paper or chapter from their ongoing research. Like in an art studio, the goal is to provide participants with constructive feedback on their works in progress.

    Required Software: None


    Session Structure and Course Sequences

    • Courses within a Session are offered concurrently, so it is only possible to attend one course in Session I and one course in Session II.
    • You may attend up to two courses, as long as they are being offered in separate Sessions (see above).
    • Some courses are set up to be an optional sequence. You do not have to take these courses as a sequence, you may take one or both courses, or mix and match them to meet your research needs.

    Available sequences are:

    Session I (June 11-13) Session II (June 14-16)
    Course Sequence Intro to Qualitative Methods/Ethnography, Dr. Michael Pratt Advanced Qualitative Analysis, Dr. Rhonda Reger

    Session I (June 11-13) Session II (June 14-16)
    Course Sequence Producing Discovery in Coding Qualitative Data, Dr. Karen Golden-Biddle Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS), Dr. Anne Smith

    Registration, Pricing, Advanced Registration Deadline

    To register for 2018 CARMA Short Courses at Boston College, you must first log in to your CARMA account (If you do not already have an account, click here to create one). Click here to be brought to the login page. Once you have logged in, and you are in the User Area, select "Purchase Short Course" on the right side of the page.

    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 - 06/11/18
    Faculty $975 $1,800 $575 $1,000
    Late Registration
    05/05/18 - 06/11/18
    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 Boston College residence hall for the duration of their Short Course(s). We have a limited number of residence hall rooms available. If you attempt to reserve a room and they are booked out, please email us to be placed on our waiting list. We will notify you if we will be able to make more rooms available.

    Length of Stay Information:

    Check-in/check-out dates are NOT flexible. If you arrive in Boston prior to check-in or plan to stay later than check-out, you will be responsible for finding accommodations.

    Check-In Check-Out Length of Stay
    Session I Attendees Sunday, June 10 Wednesday, June 13 3 nights
    Session II Attendees Wednesday, June 13 Saturday, June 16 3 nights
    Both Session I and II Attendees Sunday, June 10 Saturday, June 16 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 $377.25 per person $704.25 per person
    Shared, Two Person Room $213.75 per person $377.25 per person

    Included with purchase of room: access card and full linen service (a made bed along with towels, soap, a cup, tissues, rolls of toilet paper, and trash barrels with liners). Rooms are air conditioned.

    Schedule for On-Campus Housing Registration:

    As noted above, we have a limited number of rooms available. They will be allocated on a first come first serve basis. Therefore, you should reserve as quickly as you can, and email us to be placed on the waiting list if rooms are already full.

    Housing Cancellation Policy:

    Boston College’s policies require a financial commitment from CARMA at the time of reservation and they do not provide refunds to us. Therefore, CARMA is not able to provide refunds to our participants.

    How to Reserve a Residence Hall Room:

    1. Login to your CARMA Website User Account, or create one.
    2. Once logged in, you are brought to the “User Area”.
    3. Select “Purchase Short Courses” on the right side of the webpage.
      • Select “On-Campus Housing, Boston College” from drop-down menu
      • 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: 2018BC
        • 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.
    4. Continue on to pay
    5. If the option to purchase a room is not showing up, they may be sold out.
      * email carma@unl.edu to be placed on the waiting list, we still may be able to secure you a room!

    Listing of Nearby Hotels.

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)

  1. "Introduction to Meta-Analysis" - Dr. Ernest O’Boyle, Indiana University
  2. "Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling" - Dr. Robert Vandenberg
  3. "Introduction to Multilevel Analysis" - Dr. James LeBreton
  4. "Introduction to R" - Dr. Scott Tonidandel
  5. "Intro to Big Data and Data Mining with R" - Dr. Jeff Stanton
  6. "Intermediate Regression: Multivariate/Logistic, Mediation/Moderation" - Dr. Ron Landis
  1. "Introduction to Structural Equation Methods" - Dr. Larry J Williams
  2. "Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions" - Dr. Robert Vandenberg
  3. "Advanced Multilevel Analysis" - Dr. James LeBreton
  4. "Multivariate Statistics with R" - Dr. Steve Culpepper
  5. "Analysis of Big Data" - Dr. Fred Oswald
  6. "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: http://miktex.org/ , for OS X http://www.tug.org/mactex/, for Ubuntu/Debian (Linux): apt-get install texlive or http://www.tug.org/texlive/ )

    Option #5: "Analysis of Big Data" - Dr. Fred Oswald, Rice University

    Oswald

    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. 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.

2018 CARMA Short Courses in Columbia, South Carolina

Courses hosted by University of South Carolina

January 4-6, 2018 – Four Courses

Courses are offered concurrently so you may only select one to take for the duration of the Session.

Short Course Topics, Instructors, and Summaries

Course: "Intermediate SEM, Model Evaluation"

Dr. Larry Williams, University of Nebraska-Lincoln

Dr. Larry Williams, University of Nebraska-Lincoln, College of Business Administration

Course Description - This course is aimed at faculty and students with an introductory understanding of structural equation methods who seek a better understanding of the challenging process of making judgments about the adequacy of their models. Those who attend should have experience in fitting structural equation models with software such as LISREL, MPlus, EQS, or AMOS. This experience requirement can be met by completion of the Introduction to SEM Short Course. Attendees will be expected to bring their own laptop computers installed with their SEM software, and they should also know how to import data from an SPSS save file into their SEM software program. Attendees will learn out to interpret and report results from SEM analyses, and how to conduct model comparisons to obtain information relevant to inferences about their models, as well as advantages and disadvantages of different approaches to model evaluation. Attendees are encouraged to bring their own data for use during parts of the short course.

The course will consist of five sections, with each section having a lecture and lab component using exercises and data provided by the instructor:
• Review of model specification and parameter estimation
• Overview of model evaluation
• Logic and computations for goodness-of-fit measures
• Analysis of residuals and latent variables
• Model comparison strategies

Required Software: Your preferred SEM software package, SPSS (free trial edition), MPlus or Amos


Course - “Introduction to Multilevel Analysis”

Dr. Paul Bliese, University of South Carolina

Dr. Paul Bliese, University of South Carolina

Course Description - The CARMA Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses. The course covers within-group agreement, nested 2-level multilevel modeling and growth modeling. All practical exercises are conducted in R. Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research.



Course - “Introduction to Longitudinal Analysis”

Dr. Rob Ployhart, University of South Carolina

Dr. Rob Ployhart, University of South Carolina

Course Description - Nearly all phenomena studied within the organizational and social sciences evolve, transform, or change over time. Unfortunately, there is still little research that explicitly adopts a longitudinal perspective. This neglect is due to theoretical, methodological, and analytical challenges. First, most theories offer little insight into how and why change occurs. Second, there are a variety of design and measurement complexities that are unique to longitudinal designs. Finally, a number of different analytical approaches can be used to model the same data, yet there is little guidance for identifying which approach is most appropriate in a given situation. The purpose of this workshop is to introduce scholars to longitudinal research. We will first discuss theoretical and conceptual issues that must be addressed when developing a longitudinal study. We will next consider how to design a longitudinal study, including how to anticipate and reduce the common problems that nearly always occur (e.g., attrition). We will conclude by spending considerable time reviewing and using different longitudinal analytic methods, including repeated measures GLM, random coefficient growth models, and latent growth models. Students are strongly encouraged (but not required) to bring their own datasets to be modeled during the workshop.


Course: "Introduction to Text Analysis"

Dr. Anne Smith, University of Tennessee

Dr. Anne Smith, University of Tennessee

Course Description Analyzing textual data can be approached inductively or deductively, depending on the selected methodological approach of the research project. In this workshop, we will discuss and undertake hands-on text analysis exercises. Top down or a more confirmatory approach to text analysis will cover topics such as: dictionary application (e.g., Zavyalova, et al., 2016 & LIWC dictionary; Short et al., 2009); dictionary creation (Franco, Alexander, & Smith, working paper; Short et al., 2010); template utilization (Crabtree & Miller, 1999; King, 2004); and collocation analysis (Gephart, 1997). Students will be working with textual data to explore these techniques. Bottom up or more exploratory approach will include an in vivo, manual coding exercise and a demonstration of coding techniques using computer aided qualitative data analysis software (CAQDAS). No prior knowledge of software or text analysis is required.

Required Software: CAQDAS


Registration, Pricing, Advanced Registration Deadline

To register for 2018 CARMA Short Courses at the University of South Carolina, 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

• CARMA Members Faculty/Professional: $400.00
• CARMA Members Students: $300.00

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, please contact us directly at carma@unl.edu

Individuals that are members of the Southern Management Association receive the 50% membership discount on Short Course fees, as well an additional 25% off of Short Course fees. For more information on how to redeem this discount, sign into SMA's website.

Accommodations/Overnight Lodging Suggestions

Courtyard Columbia Downtown at USC
Address: 630 Assembly St (approximately 5 minute walk to Business School)
Phone: (803) 779-7800

Hilton Columbia Center Hotel
Address: 924 Senate St (approximately 7 minute walk to Business School)
Phone: (803) 744-7800

Inn at USC Wyndham Garden Columbia
Address: 1619 Pendelton St (approximately 15 minute walk to Business School, but they offer a complimentary shuttle service)
Phone: (803) 779-7779

Short Courses in Detroit, Michigan, June 2017 - Two Sessions, Twelve Courses

Hosted by Wayne State University

Session 1: June 5-7, Six Courses | Session 2: June 8-10, Six Courses

Short Course Sessions and Groupings

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

Short Course Topics, Instructors and Summaries

Complete Course Listing

Click a course name to see more info.

Session One

Monday June 5 (all day), Tuesday June 6 (all day), and Wednesday June 7 (AM half day)

Session Two

Thursday June 8 (all day), Friday June 9 (all day), and Saturday June 10 (half day)

  1. "Introduction to Structural Equation Methods" - Dr. Larry J Williams
  2. "Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling" - Dr. Robert Vandenberg
  3. "Introduction to Multilevel Analysis" - Dr. James LeBreton
  4. "Introduction to R" - Dr. Scott Tonidandel
  5. "Intro to Big Data and Data Mining with R" - Dr. Jeff Stanton
  6. "Intermediate Regression: Multivariate/Logistic, Mediation/Moderation" - Dr. Ron Landis
  1. "Intermediate SEM: Model Evaluation" - Dr. Larry J Williams
  2. "Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions" - Dr.Robert Vandenberg
  3. "Advanced Multilevel Analysis" -Dr. Paul Bliese
  4. "Multivariate Statistics with R" - Dr. Steve Culpepper
  5. "Analysis of Big Data" - Dr. Fred Oswald
  6. "Advanced Regression: Alternatives to Difference Scores, Polynomial and Response Surface Methods" - Dr. Jeff Edwards

Session 1: June 5-7, Seven Courses

“Introduction to Structural Equation Methods"

Dr. Larry J Williams, University of North Dakota

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 Amos.
  • 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 Amos)

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

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.

"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)

"Introduction to Multilevel Analysis"

Dr. James LeBreton, Pennsylvania State University

Course Description
This course is aimed at faculty and students who are relatively new to multilevel theory, measurement, and analysis. It will review basic issues associated with the development and testing of multilevel theories. Although the focus will be on issues pertaining to multilevel theory and measurement (e.g., multilevel constructs, multilevel construct validation, aggregation and composition models), we will also discuss general issues associated multilevel analysis. Examples will be presented and discussed using both SPSS and HLM. The R package will be introduced, explained, and emphasized during this short course in preparation for the advanced short course in Session II. Specific topics will include:

  • 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 SPSS Software
  • Module 4: Multilevel Measurement and Multilevel Modeling: A Simple Illustration of Analyzing Composite Variables in Hierarchical Linear Models
    • Examples using SPSS, HLM, and R Software
  • Module 5: Wrap up and Final Q & A

Required Software: R ( download here ), SPSS ( free trial version ), HLM ( student version ),

"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)

“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)

"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: TBA

Session 2: June 8-10, Six Courses

“Intermediate SEM: Model Evaluation"

Dr. Larry J Williams, Wayne State University

Course Description
This course is aimed at faculty and students with an introductory understanding of structural equation methods who seek a better understanding of the challenging process of making judgments about the adequacy of their models. Those who attend should have experience in fitting structural equation models with software such as LISREL, MPlus, EQS, or AMOS. This experience requirement can be met by completion of the Introduction to SEM Short Course. Attendees will be expected to bring their own laptop computers installed with their SEM software, and they should also know how to import data from an SPSS save file into their SEM software program. Attendees will learn out to interpret and report results from SEM analyses, and how to conduct model comparisons to obtain information relevant to inferences about their models, as well as advantages and disadvantages of different approaches to model evaluation. Attendees are encouraged to bring their own data for use during parts of the short course.

The course will consist of five sections, with each section having a lecture and lab component using exercises and data provided by the instructor:
• Review of model specification and parameter estimation
• Overview of model evaluation
• Logic and computations for goodness-of-fit measures
• Analysis of residuals and latent variables
• Model comparison strategies

Required Software: Your preferred SEM software package, SPSS (free trial version)

"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)

"Advanced Multilevel Analysis"

Dr. Paul Bliese, University of South Carolina

Course Description
The CARMA Advanced Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct advanced multilevel analyses. Emphasis will be placed on techniques for longitudinal data. The course covers both basic models (e.g., 2-level mixed and growth models), and more advanced topics (e.g., 3-level models, discontinuous growth models, and multilevel moderated-mediation models). Practical exercises, with real-world research data, are conducted in R, with accompanying output from MPlus provided for some examples. Participants who prefer SAS, SPSS, or MPlus and have experience 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 have at least some foundational understanding of conducting multilevel analyses.

  • Module 1: 2-Level Mixed Models: Cross-Level Main Effects & Interactions
    • Introduction to multilevel modeling in R and MPlus
    • Exercise 1a: Mixed modeling in R
    • Exercise 1a: Mixed modeling in MPlus
  • Module 2: Analyzing change and growth
    • Exercise 2a: Growth modeling in R
    • Exercise 2a: Growth modeling in MPlus
  • Module 3: Bayes Estimates and lme4
    • Bayes Estimates using lme in R
    • Specifying models in lme4
  • Module 4: Discontinuous growth models
    • Examples using R
  • Module 5: 3-level models; moderated-mediation models
    • Examples using R and MPlus

Required Software: R (download here)

“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
  • Binary, multinomial, and ordinal logistic regression
  • 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: http://miktex.org/ , for OS X http://www.tug.org/mactex/, for Ubuntu/Debian (Linux): apt-get install texlive or http://www.tug.org/texlive/ )

"Analysis of Big Data"

Dr. Fred Oswald, Rice University

Oswald

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

“Advanced Regression: Alternatives to Difference Scores, Polynomial and Response Surface Methods”

Dr. Jeff Edwards, University of North Carolina - Chapel Hill

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: TBA

Accommodations/ Overnight Lodging

Lodging: On-Campus Housing

For ease of lodging, CARMA has a contract with Wayne State University in which our short course members are permitted to pay a fee and stay on-campus in a university residence hall during the duration of the courses. The on-campus housing location is within 2 blocks of the building the short courses are held within (Prentis Building), and is a safe and affordable option as all rooms are available for under $25.00 a night and Wayne State University has a very safe campus. Photos of the housing facilities as well as a full list of features is available here.

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.

  • Session I Attendees: Check in Sunday June 4. Stay the night Sunday, Monday, Tuesday, and check out Wednesday June 7.
  • Session II Attendees: Check in Wednesday June 7. Stay the night Wednesday, Thursday, Friday, and check out Saturday June 10.
  • BOTH Session I AND Session II Attendees: Check in Sunday June 4. Stay the night Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, and check out Saturday June 10.

Total Price Information:

Your options for housing depend on 1) if you are staying for one session or two sessions and 2) if you want a private room or if you want to share a room with another individual.

  • For 3-nights (one Session), one person room: $94.00
  • For 3-nights (one Session), two person room: $77.50
  • For 6-nights (both Sessions), one person room: $166.00
  • For 6-nights (both Sessions), two person room: $133.00

Price Breakdown (for your information):

  • One person room: Room Fee ($24.00 per person/per night)*Number of nights + Mandatory Access Card ($2.00) + Mandatory Linen Packet ($10.00) + CARMA Processing Fee ($10.00)
  • Two person room: Room Fee ($18.50 per person/per night)*Number of nights + Mandatory Access Card ($2.00) + Mandatory Linen Packet ($10.00) + CARMA Processing Fee ($10.00)
    • Linen Packet includes: 1 pillow, 1 blanket, 1 sheet set, 1 bath towel, 1 washcloth.

How to Reserve a Residence Hall Room:

  1. Log in to your CARMA User Account.
  2. If you do not yet have a CARMA User Account, create one here.
  3. Once you are logged in, make sure you are in the "User Area" (red navigation bar at the top of the webpage has User Area located on it).
  4. Select "Purchase Short Courses" on the right side of the webpage.
  5. Select either the Individual Housing Package OR the Shared Housing Package from the drop-down menu (Note: If you are sharing a room, make sure both people purchase housing within their own CARMA website user accounts.)
  6. Select either Session I, Session II, or Both Sessions.

Deadline for On-Campus Housing Registration:

All reservations MUST be made by May 19, 2017. No changes can be made after this date.

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 19, 2017. If we are notified after this date, unfortunately we will not be able to complete a refund. Please understand that the money we receive from the housing registration is directly given to Wayne State University therefore we do not have the ability to issue a refund after this date.

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.