MM4DEBR (Cohort 2)

MM4DBER Pre-Training Materials

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Mixture Modeling for Discipline Based Education Researchers (MM4DBER) is an NSF-funded training grant to support STEM Education scholars in integrating mixture modeling into their research.

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Day 1 (May 17, 2024): Introductions, training goals, introduction to mixture modeling

Learning Outcomes:

1. Identify why research goals and aims (not research questions) are more appropriate for mixture models
2. Create a first draft of your equity-focused research goals that can be addressed with mixture modeling
3. Identify how the MM4DBERs training goals and opportunities can help you to address your research goals

Synchronous Activity:

• Slide deck here
• Workspace here

Asynchronous Activity:

• Activity 1: Create a draft of your research goals (starting on slide 9 of the Workspace)

• Activity 2: Install: R, RStudio, MPlus, MplusAutomation, R-packages

• Install R and Rstudio HERE. You will find a guide to installing both R and RStudio.
• Install Mplus HERE
• Install MplusAutomation HERE
• Note: You should have received a copy of Mplus for use during the one-year training (requires a paid license).

Install packages & practice creating Rproject: Watch these tutorial videos which introduce R & Rstudio and walk through how to install and load packages

• HERE you will find a video introducing R and RStudio (Covered in first 7 minutes)
• HERE you will find a video introducing MplusAutomation video tutorial (Covered in first 12 minutes)

NOTE: It is highly recommended that you watch the full videos before day 5 of pre-training. Especially for those who are learning R/Rstudio.

If you need help, drop into office hours. Book an appointment HERE

Anonymous Feedback Survey: Day 1

Pre-Training Day 1 Video

“Parking Lot” document with questions and answers HERE

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Day 2 (May 24, 2024): Introduction to Mixtures

Learning Outcomes:

1. Identify the advantages of using mixture modeling
2. Construct a person-centered research goal in a research topic that you are interested in

Synchronous Activity:

• Slide deck here
• Workspace here

Asynchronous Activity:

• Activity 1: Linking theory to your person-centered research goal (starting on slide 34 here)
• Activity 2: Read these mixture modeling article
• • Paper 1: Identifying Faculty and Peer Interaction Patterns of First-Year Biology Doctoral Students: A Latent Class Analysis
• • Paper 2: From Comprehensive to Singular: A Latent Class Analysis of College Teaching Practices

Anonymous Feedback Survey: Day 2

Pre-Training Day 2 Video

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Day 3 (May 31, 2024): Introduction to Latent Class Analysis

Learning Outcomes:

1. Construct a path diagram for mixture models
2. Connect research goals and theory to a path diagram

Synchronous Activity:

• Slide deck here
• Workspace here

Asynchronous Activity:

1. Create a path diagram for a research topic that you are interested in (starting on slide 16 here)
2. To help with the review of logistic regression, watch these videos:

• Odds Ratio Review Video
• Chi Square Test Review Video
• Conditional Probabilities and Contingency Tables

Anonymous Feedback Survey: Day 3

Pre-Training Day 3 Video

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Day 4 (June 7, 2024): Review of Logistic Regression

Learning Outcomes:

1. Identify why it is relevant to use logistic regression in mixture modeling
2. Understand the relationship between logits, odds ratios, and probabilities in the context of logistic regression
3. Interpret results of logistic regression
4. Identify the importance of multinomial logistic regression in the context of LCA (What is it & why do we care?)

Synchronous Activity:

• Slide deck here

• Workspace here

• Article: Fermant et al. (2021)

Asynchronous Activity:

• Activity 1: Interpret logistic regression output from Mplus output file - Slide 17 Workspace
• Activity 2: Review videos explaining odds ratios, conditional probabilities, and chi square tests (Optional; as needed) - Slide 29 Workspace

Anonymous Feedback Survey: Day 4

Pre-Training Day 4 Video

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Day 5 (June 14, 2024): R, RStudio and MplusAutomation

Learning Outcomes:

1. Using Mplus software, fellows the basic skeleton of a Mplus input (.inp) syntax, run basic descriptive statistics, and evaluate output (.out)
2. Run descriptive statistics in R using the MplusAutomation package
3. This workshop will cover the creation of R-projects and R markdowns and discuss the benefits of organization of the R workflow.

Preparation:

• Instal R, Mplus, and RStudio
• Watch videos (if you haven’t already): Intro to R , Intro to MplusAutomation

Synchronous Activity:

• Slide deck here
• Workspace here

Asynchronous Activity:

• Activity 1: Practice with R/R Studio, Mplus and MplusAutomation - Slide 3 Workspace
• Activity 2: Identify and share an article on mixture modeling - Slide 5 Workspace

Resources:

• Mplus Operartion Handout: Syntax Rules

Anonymous Feedback Survey: Day 5

Pre-Training Day 5 Video

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Helpful Links:

• Please visit our website to learn more.
• Visit our GitHub account to access all the MM4DBER materials.
• Follow us on Twitter!

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