APA Acredited Statistics Training
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IN PERSON – 5-day Statistics Short Course

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Seminar Overview:

Morning sessions will consist primarily of lectures and class discussions. Afternoon sessions will be dedicated to lab demonstrations and activities designed to help participants become users of social network methods and software. Participants are not required but encouraged to bring their own complete network data. The Friday afternoon session will be reserved for consulting on individual projects.

Survey a variety of approaches to collecting and analyzing network data at single and multiple points in time using R software. Topics include basic statistics and visualization, network regression/QAP, exponential random graph models (ERGMs), and stochastic actor-oriented models (RSiena). The workshop will consist of a mixture of classroom teaching and hands-on computer work. As such, this is a great introduction to R for anyone! Network data will be provided for the lab activities and participants will conduct some type of analysis every day. This is an applied seminar that will take you from novice to proficient in 5 days!

Seminar Topics:

  • Coming Soon…

Seminar Description:

This summer institute is designed primarily for researchers who are interested in conducting social network research, particularly those who are embarking upon it for the first time.

Instructor: Leslie Echols, Ph.D.

Leslie is an Assistant Professor in the Department of Psychology at Missouri State University. She holds a Ph.D. in education with a specialization in human development and psychology from University of California, Los Angeles. She also hold a M.S. in education from City University of New York. She is a former elementary education teacher and currently studies peer relations in the school context. Specifically, much of her research investigates the role of school ethnic composition and scheduling practices on friendship and victimization among classmates and other peers.

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Instructor: Michael D. Siciliano, Ph.D.

Michael is an Associate Professor in the Department of Public Administration at the University of Illinois at Chicago (UIC). He holds a Ph.D. in public policy and public administration from the University of Pittsburgh and a master’s in public policy analysis from Carnegie Mellon University. Michael’s work investigates the factors influencing network formation as well as the effect of social structure on individual and collective behavior, decision-making, and performance. Michael has taught network analysis training seminars and workshops for the Public Management Research Association, the Midwest Association for Public Opinion Research (MAPOR), the Science of Team Science Conference, the Center for Disaster Management at the University of Pittsburgh, and the Center for Clinical and Translational Science at UIC.

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APA Continuing Education Credits:

This course offers 23 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Identify the differences between traditional data and social network data.
  • Conduct social network analysis in the R environment.
  • Describe the relationship between the individual and social network characteristics.
  • Generate visualizations of social networks using R.
  • Practice working with matrices, network objects, and basic linear models.
  • Discuss Exponential Random Graph (ERG) models in the context of cross-sectional data.
  • Build ERG models in R.
  • Interpret results and parameters from ERG models.
  • Demonstrate how to interpret an ERG model diagnostics.
  • Discuss SIENA models in the context of longitudinal data.
  • Select proper SIENA model.
  • Interpret SIENA model parameters.
  • Evaluate SIENA model fit.
  • Troubleshoot SIENA models.
  • Identify advanced applications of in social network analysis.
  • Apply social analysis techniques to personal research questions.

Seminar Prerequisites:

Required:

  • Basic proficiency in multiple linear regression, the generalized linear model.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multilevel models
  • Intermediate proficiency in R, or syntax base software

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Bayesian Statistics
  • Data analysis using R

Software and Computer Support:

Coming Soon…

Seminar Audience:

This summer institute is designed primarily for researchers who are interested in conducting social network research, particularly those who are embarking upon it for the first time. The seminar will provide information on data collection and visualization, and will focus on the use of exponential random graph models (ERGMs; cross-sectional network analysis) and stochastic actor-oriented models (Siena, longitudinal network analysis) with in the R programming environment. R novices are welcome!

Seminar Files

Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.

Instructor will provide password on first day of seminar:
Click Here to Access Introduction to Social Network Analysis using R and RSiena

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Summer Stats Camp 2022: Social Network Analysis
Monday June 5, 2023
9:00 – 9:30 Welcome and Introductions
9:30 – 10:45 Traditional vs. Social Network Data
10:45 – 11:00 Rest Break
11:00 – 12:30 Introduction to R
12:30 – 1:30 Rest Break
1:30 – 3:15 Lab demonstration: using R
3:15 – 3:30 Rest Break
3:30 – 5:00 Lab exercises
Tuesday June 6, 2023
9:00 – 10:45 Social network statistics – from individual to network characteristics
10:45 – 11:00 Rest Break
11:00 – 12:30 Network visualization
12:30 – 1:30 Rest Break
1:30 – 3:15 Lab demonstration: matrices, network objects, basic linear models
3:15 – 3:30 Rest Break
3:30 – 5:00 Lab exercises
Wednesday June 7, 2023
9:00 – 10:45 ERG models for cross-sectional data
10:45 – 11:00 Rest Break
11:00 – 12:30 ERG model-building and interpretation
12:30 – 1:30 Rest Break
1:30 – 3:15 Lab demonstration: ERG model diagnostics
3:15 – 3:30 Rest Break
3:30 – 5:00 Lab exercises
Thursday June 8, 2023
9:00 – 10:45 SIENA models for longitudinal data
10:45 – 11:00 Rest Break
11:00 – 12:30 SIENA model selection and parameter interpretation
12:30 – 1:30 Rest Break
1:30 – 3:15 Lab demonstration: evaluating model fit and troubleshooting in SIENA
3:15 – 3:30 Rest Break
3:30 – 5:00 Lab exercises
Friday June 9, 2023
9:00 – 10:45 SIENA models of selection and influence
10:45 – 11:00 Rest Break
11:00 – 12:30 Advanced topics in social network analysis
12:30 – 1:30 Rest Break
1:30 – ~3:30 Individual Consultations

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