Past Short Courses

Short Courses in Detroit, Michigan, June 3-8, 2019 – Two Sessions, Twelve Course Options

Hosted by Wayne State University

Session 1: June 3-5, Six Course Options | Session 2: June 6-8, 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 3 (all day), Tuesday June 4 (all day), and Wed. June 5 (half day)

Session 2

Thursday June 6 (all day), Friday June 7 (all day), and Sat. June 8 (half day)

  1. “Introduction to Meta-Analysis” – Dr. Ernest O’Boyle
  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. Steven Culpepper
  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 Social Network Analysis” – Dr. Richard DeJordy
  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 3-5, 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

Note the following change in software for this course:
The primary lecture tool will be the R package, lavaan, using the R-studio interface. The instructor will make the Mplus modules (e.g., data, code, output) available for those want to use MPlus for in-class exercises. The topics will be the same as for the past few years this course has been offered – only the primary software for lecture has changed. Students wanting to work with either lavaan or MPlus are welcomed to the course.

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 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. We will use a multi-sample approach in undertaking the invariance tests, and you will be shown how to test latent mean differences using the latent means of the latent variables within each group. 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 by creating vectors of change across time. 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. In doing so, we will extensively cover model identification as it is particularly important to testing reciprocal effects.

Required Software: R installed with LAVAAN package / (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. Steven Culpepper, University of Illinois Urbana-Champaign



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 6-8, Six Courses


Option #1: “Introduction to Social Network Analysis” –
Dr. Richard DeJordy, California State University, Fresno

Dr. Richard DeJordy, California State University, Fresno

Course Description

The CARMA Introduction to Social Network Analysis short course provides both (1) the theoretical foundation, and (2) hands-on experience to prepare attendees to embark on social network research. Emphasis will be placed on fundamental aspects of social network research (e.g., data consideration, commonly used measures). After a brief introduction to fundamental concepts and an in-class tutorial of the software used in class (UCINET for Windows), each module will first cover a specific topic relating to social network theory, then walk through a hands-on exercise using the software. Hands-on exercises are designed to use sample datasets that come with the software, but participants are encouraged to bring their own data and use it to explore the concepts and software functions covered in each session.

• Module 1: Introduction and Fundamentals of Social Network Analysis and a tutorial of UCINET and NetDraw.
• Module 2: Data considerations in Social Network research
• Module 3: Centrality – basic measures of network position
• Module 4: Cohesion – basic measures describing networks
• Module 5: Social Capital – Structural holes and network composition

If time permits, we will also cover: Testing network hypotheses – Nodal and dyadic hypotheses.

Required Software: UCINET download AFTER May 15. (There is a free trial for 30 days from download, so do not download too early.)

NOTE for MAC users: UCINET is Windows software. It does not run natively on Macs. If you use a Mac,
your best option is to run it in a Windows emulator (such as Parallels or VMWare Fusion). These are often available for
free to university faculty and students through campus arrangements (check with your campus technology group). Other options
are listed on the FAQ on the download page. You need to have the software installed in advance to participate in the hands-on
portion of the course.


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



Course Description

Note the following change in software for this course:
The primary lecture tool will be the R package, lavaan, using the R-studio interface. The instructor will make the Mplus modules (e.g., data, code, output) available for those want to use MPlus for in-class exercises. The topics will be the same as for the past few years this course has been offered – only the primary software for lecture has changed. Students wanting to work with either lavaan or MPlus are welcomed to the course.

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 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 and cross-level interactions. 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 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. From this foundation, participants will move into specifying interactions among latent variables and how to test hypotheses with interactions. \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 such as and full information maximum likelihood.

Required Software: R installed with LAVAAN package / MPlus (order the full version, try the free demo version)


Option #3: “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 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 2019 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
02/22/19 – 03/15/19
Faculty $800 $1,525 $400 $725
Advanced Registration
02/22/19 – 03/15/19
Student $600 $1,125 $300 $525
Normal Registration
03/16/19 – 05/06/19
Faculty $900 $1,725 $450 $825
Normal Registration
03/16/19 – 05/06/19
Student $700 $1,325 $350 $625
Late Registration
05/07/19 – 06/04/19
Faculty $1,000 $1,925 $550 $1,025
Late Registration
05/07/19 – 06/04/19
Student $800 $1,525 $450 $825

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

** – These prices reflect a 50% discount that you receive if you are student/faculty at an organization that is a member of the CARMA Consortium Webcast Program OR the International Video Library Program.

*** – 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

View Housing Details for Wayne State Short Courses

Qualitative Courses in Boston, Massachusetts, June 2019 – Two Sessions, 8 Course Options

Hosted by Boston College

Session 1: June 10-12, Three Course Options | Session 2: June 13-15, Three Course Options

Complete Course Listing

Session 1

Monday June 10 (all day), Tuesday June 11 (all day), and Wed. June 12 (half day)

Session 2

Thursday June 13 (all day), Friday June 14 (all day), and Sat. June 15 (half day)

  1. “Introduction to Qualitative Methods/Ethnography” – Dr. Michael Pratt
  2. “Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS)” – Dr. Anne Smith
  3. “Interviewing for Qualitative Research” – Dr. Ashley Mears
  4. “Mixed Methods” – Dr. Thomas Greckhamer
  1. “Advanced Qualitative Analysis” – Dr. Rhonda Reger
  2. “The Craft of Inductive Qualitative Research” – Dr. Michel Anteby
  3. “Grounded Theory Method and Analysis” – Dr. Tine Kӧhler
  4. “Qualitative Analysis for Organizational Change” – Dr. Jean Bartunek

Session 1: June 10-12, Four 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: “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: “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

Option #4: “Mixed Methods” – Dr. Thomas Greckhamer, Louisiana State University


Course Description

This course begins with an overview of mixed methods research designs, including sequential explanatory, exploratory, and transformational versions, as well as concurrent triangulation, nested, and transformative alternatives. Next, Qualitative Comparative Analysis (QCA) is introduced as an increasingly popular approach in management research that is relevant for qualitative and quantitative researchers alike. The course includes hands-on application of QCA, Crisp- and Fuzzy-Set analyses, the interpretation of QCA results, and the potential of using QCA as part of mixed methods research designs.

Session 2: June 13-15, Four 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: “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

Dr. Tine Kӧhler


Option #3: “Grounded Theory Method and Analysis” –
Dr Tine Kӧhler, The University of Melbourne

Course Description

The purpose of this workshop is to introduce researchers to the underlying tenets of the grounded theory approach and to aid them in designing and conducting a grounded theory study. In the course, we will cover the following three major topics: (a) Understanding the approach and different methodological traditions, (b) designing a grounded theory study, (i.e., data collection methods, purposive sampling, gaining access, sources, triangulation), and (c) analyzing data following the grounded theory method (i.e., different approaches to coding, constant comparison, memoing, triangulation, theoretical saturation, etc.). Furthermore, the workshop will provide specific examples of practical challenges and strategies to manage them. No software is necessary for this course. Participants are invited to bring samples of their own data to the course.

Jean Bartunek


Option #4: “Qualitative Analysis of Organizational Change” –
Professor Jean Bartunek, Boston College

Course Description

This CARMA Short Course concerns exploration and critique of several qualitative approaches that may be used to study various types of change within organizations from a somewhat mezzo perspective. Course topics will include several types of change that may occur within organizations, including action research/planned change, organizational learning, and dialectical/paradoxical change. It will also address experiences of recipients of organizational change, and affective and temporal processes within change. From a research perspective, it will also address roles of the researcher with regard to change. Researchers may play several roles, including change participant, external researcher, or collaborator with one or more members of the organization in studying the change. In the course we will review recent scholarship that addresses approaches to change and critique qualitative methods this scholarship uses to study them. Finally, using available materials, we will explore how some of the methods would be used in students’ own research.

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).

Lodging: On-Campus Housing

View Housing Details for Boston College Short Courses

Short Courses in Padova, Italy, July 1-6, 2019 – Two Sessions, Four Course Options

Hosted by Department of Economics and Management at the University of Padova

Session 1: July 1-3 | Session 2: July 4-6

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 1 (all day), Tuesday July 2 (all day), and Wed. July 3 (half day)

Session 2

Thursday July 4 (all day), Friday July 5 (all day), and Sat. July 6 (half day)

Complementary CARMA Workshop: Basics of R

This four-hour Workshop provides information on the package R to prepare attendees for follow-up training in CARMA Short Courses
that use R. By attending this workshop, participants will learn basic skills for using the R Studio interface to load and activate
R packages, import and manage data, and create and execute syntax. Having these basic skills will allow Short Course participants to more
easily learn about use of R for data analysis and will enable Short Course instructors to better plan and deliver their content. This
Workshop is only available to those who will be attending one of the CARMA Short Courses to be held at the University of Padova (July
1-6, 2019), it will be offered in person for Session II, July 3 from 1-5 pm, and it will also be available on-line for Session I and II.
There is no separate registration fee for this workshop.

During this Basics of R Workshop, attendees will learn:
1. Using R through the R Studio interface
2. Importing data into R
3. R data sets (a.k.a data frames and tibbles)
4. Data types
5. Subsetting columns of data and selecting cases
6. Recoding data and dealing with missing data
7. Merging data (columns and rows)
8. Output objects
9. User defined functions
10. Getting help

Session I: July 1-3, Two Course Options

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


Option #1: “Intermediate Regression: Multivariate/Logistic, Mediation/Moderation” – Dr. 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 or SPSS

Lisa Lambert


Option #2: “Questionnaire Design” – Dr. Lisa Lambert, Oklahoma State University

Course Description

This workshop will help you develop and execute your data collection. Topics include designing your project (selecting variables and scales, sampling requirements) with a special emphasis on revising/creating new scales (scale development procedures, validation techniques). We will also review procedures for assessing construct validity (EFA/CFA) and focus on how to design your questionnaire to obtain high quality data. Finally, procedures for managing your data collection and for cleaning your data (missing data, outliers, identifying careless responders). There will be opportunities to advance our own project within the workshop.

Session II: July 4-6, Two Course Options

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


Option #1: “Introduction to Structural Equation Methods” – Dr. Robert Vandenberg, University of Georgia

Course Description

This introductory course requires no previous knowledge of structural equation modeling (SEM), but participants should possess a strong understanding of regression. I will use R LAVAAN package in my examples. You need to have R installed with LAVAAN package on your computer, and you also need to have a very strong understanding about the data handling functions using R. There is not enough workshop time to go through R basics, and to assist you in installing LAVAAN. The course will start with an overview of the principals underlying SEM. Subsequently, we move into measurement model evaluation including confirmatory factor analysis (CFA). Time will be spent on interpreting the parameter estimates and comparing competing measurement models for correlated constructs. We will then move onto path model evaluation where paths representing “causal” relations are placed between the latent variables. Again, time will be spent on interpreting the various parameter estimates and determining whether the path models add anything above their underlying measurement models. If time permits, longitudinal models and other advanced topics will be introduced.

Required Software: R installed with LAVAAN package

Paul Bliese


Option #2: “Introduction to Multilevel/Longitudinal Analysis” – Dr. Paul Bliese, University of South Carolina

Course Description

The CARMA Multilevel/Longitudinal Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses, including those associated with longitudinal data. 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.

Required Software: SPSS

Registration, Pricing, Advanced Registration Deadline

To register for 2019 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 21, 2019. 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 Adelaide, Australia, April 8-12, 2019 – Two Sessions, Two Courses

Hosted by University of South Australia

Session 1: April 8-10 | Session 2: April 10-12

University of South Australia logo

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1

Monday April 8 (all day), Tuesday April 9 (all day), and Wednesday April 10 (AM half day)

Session 2

Wednesday April 10 (PM half day), Thursday April 11 (all day), and Friday April 12 (all day)

Session I: April 8-10

 Ernest O’Boyle


Option #1: “Systematic Reviews / Meta Analysis” – Associate Professor Ernest O’Boyle, Indiana University

Course Description

Meta-analyses have now become a staple of research in the organizational sciences. Their purpose is to summarize and clarify the extant literature through systematic and transparent means. Meta-analyses help answer long-standing questions, address existing debates, and highlight opportunities for future research. Despite their prominence, knowledge and expertise in meta-analysis is still restricted to a relatively small group of scholars. This short course is intended to expand that group by familiarizing individuals with the key concepts and procedures of meta-analysis with a practical focus. Specifically, the goal is to provide the necessary tools to conduct and publish a meta-analysis/systematic review using best practices. We will cover how to; (a) develop research questions that can be addressed with meta-analysis, (b) conduct a thorough search of the literature, (c) provide accurate and reliable coding, (d) correct for various statistical artifacts, and (e) analyze bivariate relationships (e.g., correlations, mean differences) as well as multivariate ones using meta-regression and meta-SEM. The course is introductory, so no formal training in meta-analysis is needed. Familiarity with some basic statistical concepts such as sampling error, correlation, and variation is sufficient.

Required Software: Microsoft Excel; R

Session II: April 10-12

Robert Vandenberg


Option #2: “Introduction to Structural Equation Methods” – Professor Robert Vandenberg, University of Georgia

Course Description

This introductory course requires no previous knowledge of structural equation modeling (SEM), but participants should possess a strong understanding of regression. I will use R LAVAAN package in my examples. You need to have R installed with LAVAAN package on your computer, and you also need to have a very strong understanding about the data handling functions using R. There is not enough workshop time to go through R basics, and to assist you in installing LAVAAN. The course will start with an overview of the principals underlying SEM. Subsequently, we move into measurement model evaluation including confirmatory factor analysis (CFA). Time will be spent on interpreting the parameter estimates and comparing competing measurement models for correlated constructs. We will then move onto path model evaluation where paths representing “causal” relations are placed between the latent variables. Again, time will be spent on interpreting the various parameter estimates and determining whether the path models add anything above their underlying measurement models. If time permits, longitudinal models and other advanced topics will be introduced.

Required Software: R installed with LAVAAN package

Registration, Pricing, Advanced Registration Deadline

To register for 2019 CARMA Short Courses in Adelaide, Australia, 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: $900.00
• Students: $700.00
CARMA Member prices per course
• Faculty/Professional: $450.00
• Students: $350.00

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 Friday, March 22, 2019. After this date, a $75.00 fee will be added to all registrations.

Short Courses in Columbia, South Carolina, January 10-12, 2019 – One Session, Seven Course Options

Hosted by University of South Carolina

University of South Carolina logo

Short Course Sessions and Groupings

All courses in a session are taught concurrently, so a participant can take only one course per session.

CARMA Workshop: Basics of R

This four-hour Workshop provides information on the package R to prepare attendees for follow-up training in CARMA Short Courses that use R. By attending this workshop, participants will learn basic skills for using the R Studio interface to: load and activate R packages, import and manage data, and create and execute syntax. Having these basic skills will allow Short Course participants to more easily learn about use of R for data analysis and will enable Short Course instructors to better plan and deliver their content. This Workshop is only available to those who will be attending one of the CARMA Short Courses to be held at the University of South Carolina (January 10-12, 2019), it will be offered in person January 9 from 2-6 pm, and it will also be available on-line after December 1. There is no separate registration fee for this workshop.

During this Basics of R Workshop, attendees will learn:
1. Using R through the R Studio interface
2. Importing data into R
3. R data sets (a.k.a data frames and tibbles)
4. Data types
5. Subsetting columns of data and selecting cases
6. Recoding data and dealing with missing data
7. Merging data (columns and rows)
8. Output objects
9. User defined functions
10. Getting help


Option 1: “Introduction to R” – Dr. Scott Tonidandel, University of North Carolina-Charlotte

Scott Tonidandel

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 other CARMA short courses.


Option 2: “Regression with R” – Dr. Ron Landis, Illinois Institute of Technology

Ron Landis

Course Description

This short course will begin with an introduction to linear regression analysis with R. Models for single and multiple predictors will be covered, as will model comparison techniques. Particular attention will be paid to using regression to test models involving mediation and moderation, followed by consideration of advanced topics including multivariate regression, use of polynomial regression, logistic regression, and the general linear model. For all topics, examples will be discussed and assignments completed using either data provided by the instructor or by the short course participants.


Option 3: “Introduction to Multi-level Analysis with R” – Dr. Paul Bliese, University of South Carolina

Paul Bliese

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.


Option 4: “Introduction to SEM with R and LAVAAN” – Dr. Robert Vandenberg, University of Georgia

Robert Vandenberg

Course Description

This introductory course requires no previous knowledge of structural equation modeling (SEM), but participants should possess a strong understanding of regression AND have understanding about the basic data handling functions using R. All illustrations and in-class exercises will make use of the R LAVAAN package, and participants will be expected to have LAVAAN installed on their laptop computers prior to beginning of the course. No course time will be spent going over basic R data handling and installing the LAVAAN package. The course will start with an overview of the principals underlying SEM. Subsequently, we move into measurement model evaluation including confirmatory factor analysis (CFA). Time will be spent on interpreting the parameter estimates and comparing competing measurement models for correlated constructs. We will then move onto path model evaluation where paths representing “causal” relations are placed between the latent variables. Again, time will be spent on interpreting the various parameter estimates and determining whether the path models add anything above their underlying measurement models. If time permits, longitudinal models will be introduced.


Option 5: “Web Scraping: Data Collection and Analysis” – Dr. Richard Landers, University of Minnesota

Richard Landers

Course Description

In this course, you will learn how to create novel datasets from information found for free on the internet using only R and your own computer. After a brief introduction to web architecture and web design, we will explore the collection of unstructured data by scraping webpages directly through several small hands-on projects. Next, we will explore the collection of structured data by learning how to send queries directly to service providers like Google, Facebook and Twitter via their APIs. Finally, we will conduct a complete scraping project from start to finish including some novel analytic approaches (e.g., automatic identification of gender for social media contributors, language processing to extract themes, and interactive visualization with a simple web app).


Option 6: “Introduction to Bayesian Analysis with R” – Dr. Steve Culpepper, University of Illinois

Steve Culpepper

Course Description

Many inferential statistical procedures include an examination of p-values, a strategy that is sometimes labeled as the frequentist approach. An alternative has emerged over recent decades, known as Bayesian inference, that uses different strategies for making statistical decisions. In this short course, we will compare and contrast traditional frequentist inference with Bayesian inference. We will use the R open source statistical platform to conduct Bayesian inference, starting simply with the t-test and working towards more complex multivariate techniques. We will also examine some research publications to see Bayesian inference in action. By the end of this short course, you will be able to substitute Bayesian inferential procedures in place of some of the frequentist analysis techniques you may currently use.


Option 7: “Analysis of Big Data” – Dr. Jeff Stanton, Syracuse University

Jeff Stanton

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. We will build hands-on skills for developing and running predictive models relevant to big data. We will discuss feature selection and dimension reduction. A range of predictive models will be covered: e.g., penalized regression models, random forest, stochastic gradient boosted trees, and support vector machines. We will touch briefly on text mining. We will use R and R-Studio for this course. Students are welcome to bring their own data sets for experimentation, but many data sets will be provided, so this is not required.

Registration Details

To register for 2019 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.

The early registration date is December 7, 2018.

Price Per Course
Early Registration Non-Member CARMA Member* SMA and SIOP Members**
Faculty/Professional $800.00 $400.00 $400.00
Student $600.00 $300.00 $300.00
Late Registration Non-Member CARMA Member* SMA and SIOP Members**
Faculty/Professional $900.00 $450.00 $450.00
Student $700.00 $350.00 $350.00

*Not sure if your Institution is a CARMA Member? Universities in the US and Canada may check here.

**All Southern Management Association Members and SIOP members receive discounted prices on Short Course registration fees for all South Carolina Short Courses.

If you are a member of SIOP or SMA, you can use one of the following discount codes when registering for these short courses:

Faculty Code: c1a6-03b5
Student Code: 7a87-1267

Note that we will be verifying SIOP/SMA membership for all those who use these discount codes. Anyone who uses one of these
discount codes and is not a member of SIOP or SMA will be required to pay the non-SIOP/SMA member rate.

Accommodations/Overnight Lodging Suggestions

Hotel Address Phone
Courtyard Columbia Downtown at USC 630 Assembly St (approximately 5 minute walk to Business School) (803) 779-7800
Hilton Columbia Center Hotel 924 Senate St (approximately 7 minute walk to Business School) (803) 744-7800
Inn at USC Wyndham Garden Columbia 1619 Pendelton St (approximately 15 minute walk to Business School
but they offer a complimentary shuttle service)
(803) 779-7779

Short Courses in Adelaide, Australia, Nov 12-16, 2018 – Two Sessions, Two Courses

Hosted by University of South Australia

Session 1: November 12-14 | Session 2: November 14-16

University of South Australia logo

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1

Monday November 12 (all day), Tuesday November 13 (all day), and Wed. November 14 (AM half day)

Session 2

Wed. November 14 (PM half day), Thursday November 15 (all day), and Friday November 15 (all day)

Session I: November 12-14

Jean Bartunek


Option #1: “Qualitative Analysis of Organizational Change” –
Professor Jean Bartunek, Boston College

Course Description

This CARMA Short Course concerns exploration and critique of several qualitative approaches that may be used to study various types of change within organizations from a somewhat mezzo perspective. Course topics will include several types of change that may occur within organizations, including action research/planned change, organizational learning, and dialectical/paradoxical change. It will also address experiences of recipients of organizational change, and affective and temporal processes within change. From a research perspective, it will also address roles of the researcher with regard to change. Researchers may play several roles, including change participant, external researcher, or collaborator with one or more members of the organization in studying the change. In the course we will review recent scholarship that addresses approaches to change and critique qualitative methods this scholarship uses to study them. Finally, using available materials, we will explore how some of the methods would be used in students’ own research.

Required Software: None

Session II: November 14-16

Lisa Lambert


Option #2: “Advanced Regression Analysis: Alternatives to Difference Scores, Polynomial and Response Surface Methods” –
Associate Professor Lisa Lambert, Oklahoma State University

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

Registration, Pricing, Advanced Registration Deadline

To register for 2018 CARMA Short Courses in Adelaide, Australia, 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: $900.00
• Students: $700.00
CARMA Member prices per course
• Faculty/Professional: $450.00
• Students: $350.00

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 November 1, 2018. After this date, a $75.00 fee will be added to all registrations.

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.


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


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.

Short Courses in Adelaide, Australia, March 2017 – Two Sessions, Two Courses

Hosted by University of South Australia

March 20-24, 2017 – Two Courses, Two Sessions*

*We offer two sessions of courses in order to allow participants to take two back-to-back methods courses. You may select one course from Session I and one course from Session II.

Short Course Topics, Instructors, Summaries

Session I: Monday March 20 (full day), Tuesday March 21 (full day), Wednesday March 22 (a.m. half day)

“Introduction to Research Methods”

Dr. Larry Williams
Dr. Larry Williams

Dr. Larry J Williams, University of Nebraska-Lincoln

Course Description

This short course begins with an overview of the research process typically used in organizational research. Next, constructs and their measurement are considered, including introductory coverage of reliability and validity. Consideration of experimental and non-experimental designs follows. And, data analysis tools will also be introduced, including descriptive statistics, analysis of variance, correlation, and linear regression. 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: SPSS (free trial version)


Session II: Wednesday March 22 (p.m. half day), Thursday March 23 (full day), Friday March 24 (full day)


“Introduction to Multilevel Analysis”

Dr. Bob Vandenberg
Dr. Bob Vandenberg


Dr. Robert Vandenberg, University of Georgia

Course Description
The purpose of this short course is to provide introductory coverage of multilevel modeling using the MPlus statistical software package. 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


Registration, Pricing, Advanced Registration Deadline for Adelaide Short Courses

To register for 2016 CARMA Short Courses in Adelaide, Australia 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.

Non-member prices per course: *All prices are in US Dollars (USD)

  • Faculty/Professional: $900.00
  • Students: $700.00

CARMA Member prices per course

  • CARMA Members Faculty/Professional: $450.00
  • CARMA Members Students: $350.00
    • 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 February 20, 2017. After this date, a $75.00 fee will be added to all registrations.

2017 CARMA Short Courses in Columbia, South Carolina

Courses hosted by University of South Carolina

January 5-7, 2017 – Three 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

Session I: January 5 and 6 (full days), 7 (a.m. half day)

Course: “Introduction to SEM”

Dr. Larry Williams, University of Nebraska-Lincoln


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

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.


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.

Registration, Pricing, Advanced Registration Deadline

To register for 2017 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)

Find out if your organization is a CARMA International Video Library Program Member.
(institutions outside US and Canada only)

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

Southern Management Association Members receive an additional discount on Short Course registration fees for all South Carolina Short Courses. For more information
on the discount, sign into SMA’s website and under the “Resources” tab select “CARMA”
for more information on the discount.

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
Discounted Rate of $125 per night: When making reservation, ask for “CARMA rate”

2016 CARMA Short Courses in Adelaide, Australia

Courses hosted by School of Management, University of South Australia

November 14-18, 2016 – Two Sessions, Three Courses

We offer two sessions of courses in order to allow participants to take two back-to-back methods courses. You may select one course
from Session I and one course from Session II.

Short Course Topics, Instructors and Summaries

Session I: November 14 and 15 (full days), 16 (a.m. half day)

Course – “Introduction to Research Methods II, Linear Regression”

Dr. Larry Williams, University of Nebraska-Lincoln


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

Course Description – This short course will begin with a brief review of correlations and hypothesis testing. Next the
basics of linear regression with a single predictor will be introduced, including consideration of OLS estimation and associated statistical
testing (e.g. t-test, F-test, confidence interval, statistical power). These basics will be introduced using a continuous predictor, and then
dummy variables for categorical variables will be covered. Finally, extension of the above topics to the case of two predictors will be covered.
For most topics, examples and assignments will be provided.

Required Software: TBA


Course – “Intermediate Regression Analysis”

Dr. Ron Landis, Illinois Institute of Technology


Dr. 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 II: November 16 (p.m. half day), 17 and 18 (full days)

Course – “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

Registration, Pricing, Advanced Registration Deadline

To register for 2016 CARMA Short Courses in Adelaide, Australia 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: $900.00
• Students: $700.00

CARMA Member prices per course

• CARMA Members Faculty/Professional: $450.00
• CARMA Members Students: $350.00

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

Find out if your organization is a CARMA International Video Library Program Member.
(institutions outside US and Canada only)

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.

Short Courses in Adelaide, Australia, April 18-22

Courses hosted by School of Management, University of South Australia

April 18-22, 2016 – Two Sessions, Three Courses

We offer two sessions of courses in order to allow participants to take two back-to-back methods courses. You may select one course from Session I and one course from Session II.

Short Course Topics, Instructors and Summaries

Session I: April 18 & 19 (full days), 20 (AM half day)

  • “Qualitative Methods”
Dr. Rhonda Reger
Dr. Rhonda Reger

Dr. Rhonda Reger, The University of Tennessee, Knoxville

Course Description

In this course, students will be exposed to findings about the prevalence of research methods currently used in macro-level management fields, specifically in strategic management, organization theory and entrepreneurship in the Academy of Management Journal and the Strategic Management Journal. The course will provide a deeper grounding in several qualitative methods including comparative case study research, content analysis, discourse analysis, rhetorical analysis, and repertory grid interview methods. 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: TBA


Session II: April 20 (p.m. half day), 21 & 22 (full days)

  • “Introduction to Research Methods”
Dr. Larry Williams
Dr. Larry Williams

Dr. Larry J Williams, University of Nebraska-Lincoln

Course Description

This short course begins with an overview of the research process typically used in organizational research. Next, constructs and their measurement are considered, including introductory coverage of reliability and validity. Consideration of experimental and non-experimental designs follows. And, data analysis tools will also be introduced, including descriptive statistics, analysis of variance, correlation, and linear regression. 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: SPSS (free trial version)


  • “Introduction to Multilevel/Longitudinal Analysis”
Dr. Paul Bliese
Dr. Paul Bliese


Dr. Paul Bliese, University of South Carolina

Course Description

The CARMA Multilevel/Longitudinal Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses, including those associated with longitudinal data. 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.

Required Software: SPSS (free trial version)

Registration, Pricing, Advanced Registration Deadline for Adelaide Short Courses

To register for 2016 CARMA Short Courses in Adelaide, Australia 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.

Non-member prices per course: *All prices are in US Dollars (USD)

  • Faculty/Professional: $900.00
  • Students: $700.00

CARMA Member prices per course

  • CARMA Members Faculty/Professional: $450.00
  • CARMA Members Students: $350.00
    • 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 April 1, 2016. After this date, a $75.00 fee will be added to all registrations.

Academy of International Business CARMA Workshop Registration Page

Intermediate SEM: Model Evaluation – Dr. Larry Williams, University of Nebraska-Lincoln

Dr. Larry Williams
Dr. Larry Williams

Dr. Larry J Williams, University of Nebraska-Lincoln

Time: 9:00am-12:00pm

Date: Sunday, June 26th

Description: This workshop 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 some exposure to SEM and
experience in fitting structural equation models with software such
as LISREL, MPlus, EQS, or AMOS. 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.

The workshop will include (a) review of model
specification and parameter estimation, (b) overview of model
evaluation,
(c) logic and computations for goodness-of-fit measures, (d) analysis
of residuals and latent variables, and (e) model comparison
strategies.

An Introduction to Multi-level Analysis-
Dr. Bob Vandenberg, University of Georgia

Dr. Bob Vandenberg
Dr. Bob Vandenberg

Dr. Bob Vandenberg, University of Georgia

Time: 2:00pm-5:00pm

Date: Sunday, June 26th

Description:
An Introduction to Multi-level Analysis The objective
of this workshop is to provide a review of commonly undertaken
practices in multilevel analyses. Given the limited time, focus will
be entirely on how to conduct the analyses using the Mplus software
package.

Therefore, the primary focus is on how to conduct the analyses rather
than the conceptual basis as to why and when multilevel analyses
should be undertaken. We will start with the simplest multilevel
model, and build more and more complex ones using both mediation and
moderation hypotheses. With respect to mediation, participants will
be shown how to test the significance of the indirect effects and how
to conduct bootstrapping. We will repeat this process twice. The
first time will use observed variables only. The second time will
use the same models, but variables will be treated as latent
variables within a structural equation modeling framework.
Participants will be given syntax, and a data file so that they can
either do the analyses in real time during the workshop, or later in
the quiet of their offices.

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