Network Psychometrics with Exploratory Graph Analysis - Online Course
A 4-Day Livestream Seminar Taught by
Hudson GolinoTuesday, May 14 –
Friday, May 17, 2024
10:30am-12:30pm ET (convert to your local time)
1:30pm-3:00pm ET
An Innovative Approach to Structural Validity, Item Analysis, and Dimension Analysis
Researchers and applied professionals from many different fields, including psychology, sociology, education, political science, and the health sciences, have undoubtedly faced at least one of the following questions:
- How many factors (latent dimensions) are being assessed by my instrument (test, questionnaire, survey, etc.)?
- How stable is the dimensionality solution?
- How good are my items? Do they replicate into the same latent dimensions or not?
- How can I compare multiple dimensionality configurations (or solutions)?
- Is my instrument structurally valid?
- Are my items reliable, or do they merely seem reliable because we are using the common “cheating by repeating” approach?
- How can I answer all these questions if I have (intensive) longitudinal data instead of cross-sectional data?
Too many important questions.
Or maybe you’re just interested in text mining and want to analyze data from social media in order to estimate latent variables.
Maybe you have heard a lot about network psychometrics and want to learn more about it.
Or, being more creative, you just want to know what Russian trolls have to do with dimensionality assessment and reduction and item analysis.
Again, exciting and important questions.
If these are questions you’ve asked yourself, and you’ve been scratching your head staring at the dataset on the screen in front of you, this course is what you’ve been looking for.
Bonus feature: All seminar participants will get exclusive access to the new EGA Wizard AI Assistant (requires a ChatGPT Plus subscription).
Starting May 14, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
Computing
This is a hands-on course with instructor-led software demonstrations and guided exercises. These guided exercises will be primarily designed for the R language, so you should use a computer with a recent version of R (version 4.1.3 or later) and RStudio (version 2022.02.1+461 or later).
To follow along with the course exercises, you should have good familiarity with the use of R, including opening and executing data files and programs, as well as performing very basic data manipulation and analyses.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
This is a hands-on course with instructor-led software demonstrations and guided exercises. These guided exercises will be primarily designed for the R language, so you should use a computer with a recent version of R (version 4.1.3 or later) and RStudio (version 2022.02.1+461 or later).
To follow along with the course exercises, you should have good familiarity with the use of R, including opening and executing data files and programs, as well as performing very basic data manipulation and analyses.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
This course does not have any specific requirements, except a basic familiarity with R. This course is appropriate for researchers (in public-sector or private-sector domains, or students or faculty in academia, and researchers in general) who have a working knowledge of basic statistics.
This course does not have any specific requirements, except a basic familiarity with R. This course is appropriate for researchers (in public-sector or private-sector domains, or students or faculty in academia, and researchers in general) who have a working knowledge of basic statistics.
Seminar outline
Exploratory Graph Analysis (EGA): what it is and how it works.
- A brief introduction to network models
- The Regularized Partial Correlations approach
- The Triangulated Maximally Filtered Graph approach
- Findings from recent simulation studies
- Guided hands-on activities in R with real and simulated datasets
How to verify the fit of the EGA-estimated dimensionality structure to the data: The Entropy Fit Indices
- A brief introduction to information and quantum information metrics
- Shannon Entropy and Von Neumann’s Entropy
- Watanabe’s Total Correlation
- The Total Entropy Fit index
- The Generalized Total Entropy Fit Index for bifactor structures with multiple correlated general factors
- Comparing dimensionality solutions using the Total Entropy Fit Index
- Findings from recent simulation studies.
- Guided hands-on activities in R with real and simulated datasets
How to estimate the stability of the dimensionality structure: the Bootstrap Exploratory Graph Analysis technique
- Estimating a “typical” structure using the bootstrap EGA technique
- Investigating the structural stability of the dimensionality structure estimated via EGA: introducing the structural consistency metric
- Investigating the item stability metric
- Sources of structural/item instability: wording effects, redundant items, items that don’t work well
- Configural Invariance using EGA
- Guided hands-on activities in R with real and simulated datasets
How good are my items? The Network Loadings metric (akin to factor loadings)
- How can network loadings be computed?
- What do they mean?
- How do they compare to factor loadings?
- Guided hands-on activities in R with real and simulated datasets
Invariance using the EGA approach
- Configural Invariance using structural consistency and item stability (recap)
- Metric invariance in the EGA approach
- Guided hands-on activities in R with real and simulated datasets
Identifying redundant items using unique variable analysis
Dynamic Exploratory Graph Analysis for (intensive) longitudinal data or for text data from social media
- Generalized local linear approximation
- Dynamic Exploratory Graph Analysis
- Network loadings and network scores for Dynamic EGA
- Recent simulation studies
- Guided hands-on activities in R with real and simulated datasets
All topics will have simulated and real datasets that will be used during supervised hands-on activities.
Exploratory Graph Analysis (EGA): what it is and how it works.
- A brief introduction to network models
- The Regularized Partial Correlations approach
- The Triangulated Maximally Filtered Graph approach
- Findings from recent simulation studies
- Guided hands-on activities in R with real and simulated datasets
How to verify the fit of the EGA-estimated dimensionality structure to the data: The Entropy Fit Indices
- A brief introduction to information and quantum information metrics
- Shannon Entropy and Von Neumann’s Entropy
- Watanabe’s Total Correlation
- The Total Entropy Fit index
- The Generalized Total Entropy Fit Index for bifactor structures with multiple correlated general factors
- Comparing dimensionality solutions using the Total Entropy Fit Index
- Findings from recent simulation studies.
- Guided hands-on activities in R with real and simulated datasets
How to estimate the stability of the dimensionality structure: the Bootstrap Exploratory Graph Analysis technique
- Estimating a “typical” structure using the bootstrap EGA technique
- Investigating the structural stability of the dimensionality structure estimated via EGA: introducing the structural consistency metric
- Investigating the item stability metric
- Sources of structural/item instability: wording effects, redundant items, items that don’t work well
- Configural Invariance using EGA
- Guided hands-on activities in R with real and simulated datasets
How good are my items? The Network Loadings metric (akin to factor loadings)
- How can network loadings be computed?
- What do they mean?
- How do they compare to factor loadings?
- Guided hands-on activities in R with real and simulated datasets
Invariance using the EGA approach
- Configural Invariance using structural consistency and item stability (recap)
- Metric invariance in the EGA approach
- Guided hands-on activities in R with real and simulated datasets
Identifying redundant items using unique variable analysis
Dynamic Exploratory Graph Analysis for (intensive) longitudinal data or for text data from social media
- Generalized local linear approximation
- Dynamic Exploratory Graph Analysis
- Network loadings and network scores for Dynamic EGA
- Recent simulation studies
- Guided hands-on activities in R with real and simulated datasets
All topics will have simulated and real datasets that will be used during supervised hands-on activities.
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.