The Applied Psychometric Strategies lab provides a series of presentations on a range of quantitative and psychometric topics titled: “Applied Quantitative and Psychometric Series (AQPS). Events are open and anyone interested in our research is invited to attend. If you are interested in presenting on a topic, please contact Michael Toland.


Upcoming Events

Presentation 1: Introduction to Longitudinal Data Analysis

Rongxiu Wu (Doctoral Candidate, Educational Psychology)
Spring 2019
Location TBD


Presentation 2: Introduction to Item Response Theory

Chen Qiu (Doctoral Student, Quantitative and Psychometric Methods)
Spring 2019
Location TBD


Presentation 3: How to do an Item Response Theory Analysis with IRTPRO and flexMIRT

Chen Qiu (Doctoral Student, Quantitative and Psychometric Methods)
Spring 2019
Location TBD


Presentation 4: Introduction to Item Response Theory Based Differential Item Functioning

Falynn Thompson (Doctoral Candidate, Educational Psychology)
Spring 2019
Location TBD


Past Events

2018-2019

Presentation 1: Introduction to Confirmatory Factor Analysis

Falynn Thompson (Doctoral Candidate, Educational Psychology)
Wednesday, October 10, 9:30 – 10:30 am
Dickey Hall 203
 

Confirmatory factor analysis is a data analysis procedure used to investigate the appropriateness of a hypothesized measurement model. In this presentation we will discuss more about what it is, why and when to use it, how to specify the model, and interpret results from this model using Mplus (Click here for an introduction of Mplus).
 

Related Materials

Intro to CFA Video
Intro to CFA Handout
Intro to CFA Data Files


Presentation 2: Introduction to Path Analysis

Chunling Niu (EdD, Doctoral Student, Quantitative and Psychometric Methods)
Wednesday, November 14, 9:30 – 10:30 am
Dickey Hall 203

 

This brief introduction to path analysis includes both theoretical and applied aspects. First, the theoretical overview covers (1) nature and principles of path analysis (e.g. key concepts and terminology); (2) the relationships between path analysis, regression, and correlations; (3) model identification; and (4) model evaluation (global and local fit). Finally, a demonstration of path analysis will be performed on an example data set (Click here for an introduction of Mplus).


Presentation 3: Introduction to Structural Equation Modeling

Chunling Niu (EdD, Doctoral Student, Quantitative and Psychometric Methods)
Wednesday, December 12, 9:30 – 10:30 am
Dickey Hall 203

 

Structural equation modeling (SEM) is a combination of path analysis and confirmatory factor analysis (CFA). In this presentation a brief introduction to SEM will be provided with an applied example. First, the theoretical overview covers (1) nature and principles of SEM (e.g. key concepts and terminology); (2) the relationships between path analysis and CFA; (3) model identification; and (4) model evaluation. Finally, a demonstration of SEM will be performed on an existing data set.


Presentation 4: Introduction to Multilevel Latent Class Analysis

Caihong Li (Doctoral Candidate, Quantitative and Psychometric Methods)
Spring 2019
Location TBD



2017-2018

Presentation 1: Regression Discontinuity

Dr. Joseph Waddington
Tuesday, October 3, 1:30 – 3:00 pm
Dickey Hall 109

This presentation focused on the theory, design, and implementation of regression discontinuity (RDD). RDD is one of the most powerful tools in the suite of quasi-experimental methods used to capture causal effects of programs and policies. During the presentation, we will discuss and work through a relevant example (data/syntax/output) of the implementation of RDD in the education literature from the following paper: Angrist, J. & Lavy, V. (1999). Using Maimonides’ Rule to estimate the effect of class size on scholastic achievement. The Quarterly Journal of Economics, 114(2), 533-575.

Regression Discontinuity Handout (PDF)

Stata files for talk – RDD  (zip file)

Regression Discontinuity Video


Presentation 2: Latent Class Analysis

Caihong Li (Doctoral Candidate, Quantitative and Psychometric Methods)
Tuesday, November 7, 12:30 – 1:30 pm
Dickey Hall 109

This presentation introduced Latent Class Analysis (LCA) and its implementation in Mplus. LCA, a latent variable modeling approach, is used to classify people into groups that are similar on unobserved constructs, based on their response patterns. In LCA, group membership is unseen and is indicated by observed variables. LCA is a model-based approach and thus the results from LCA can be replicated in other samples. LCA provides researchers valuable insights into the various types of respondents and how to better construct future intervention strategies targeting different types of respondents. During the presentation, we talked about the overview of LCA and learned how to conduct LCA in Mplus step-by-step through an example (data/syntax/output)

Latent Class Analysis in Mplus (Powerpoint)

LCA Example Data (zip file)

Latent Class Analysis Video

 


Presentation 3: Power Analysis

Hao Zhou

David Dueber
Tuesday, February 6, 12:30 – 1:30 pm
Dickey Hall 109

Power Analysis Talk (Powerpoint)


 

Presentation 4: How to Choose an Instrument for My Research Study

Abbey Dani

Abbey Love

Danielle Rosenkrantz
Tuesday, March 20, 12:30 – 1:30 pm
Dickey Hall 109

This presentation will be an application-focused talk about 1.) How to determine if an existing scale should be used, and 2.) How to develop an scale to measure a psychological construct when a psychometrically sound scale is not available. The first half of the talk will provide advice on where to find scales and how to determine if reliability and validity evidence is acceptable. The second part of the presentation will focus on the steps needed to develop an scale, should you find this is necessary. We will discuss measurement issues that arise from scales with weak psychometric evidence and suggest best practices in scale development.

How to Choose An Instrument Video

How to Choose an Instrument (Powerpoint)


Presentation 5: Managing Measurement Error in Regression Analysis

Danielle Rosenkrantz

David Dueber
Tuesday, April 3, 12:30 – 1:30 pm
Dickey Hall 109

This presentation focused on the pernicious effects of measurement error and techniques for correcting and accounting for measurement error, with special emphasis on censoring.

Managing Measurement Error Video

Advanced Issues in Measurement Error Video

Managing Measurement Error (Powerpoint)

MeasErrorTalkAnalyses (zip file)

SILV Estimation Advanced (Powerpoint)

SILV_Estimation_Advanced (zip file)


2016-2017

Presentation 1: Introduction to Mplus

Intro to Mplus Video

Intro to Mplus Handout – (Powerpoint)

Intro to Mplus Data Files – (zip file)

David M. Dueber, MA (Doctoral student, STEM education)

This presentation focuses on how to get your data ready for use in Mplus, conduct basic descriptive analyses, and the commands available in Mplus for estimating basic correlations and regression models.

October 4, 2016
2:00-3:00 pm
Taylor Education Building 122


Presentation 2: Bifactor Analysis in Mplus

Bifactor Analysis in Mplus Video

Bifactor Analysis in Mplus Handout– (Powerpoint)

Bifactor Analysis in Mplus Data Files – (zip file)

A tool to calculate ancillary indices for bifactor models (e.g., ECV, IECV, PUC, Omega, OmegaH, Relative Omega, H, Absolute Relative Bias, FD) go to ‘Resources’.

Joseph H. Hammer, PhD

Michael D. Toland, PhD

This presentation introduces the bifactor confirmatory factor analysis (CFA) model: what it is, when to use it, how to run it in Mplus, and how to use follow-up Explained Common Variance and Omega coefficients to answer questions about the internal structure and reliability of multidimensional scales/instruments used in research.

November 1, 2016
3:00-4:00 pm
Taylor Education Building 122


Presentation 3: Measurement Invariance for Categorical Indicators in Mplus

Measurement Invariance with Categorical Indicators in Mplus Video

Measurement Invariance with Categorical Indicators in Mplus Handout – (Powerpoint)

Measurement Invariance with Categorical Indicators in Mplus Data Files – (zip file)

Caroline Gooden, PhD

Zijia Li, PhD

This presentation focused on what is and how to conduct a measurement invariance analysis for categorical items within a confirmatory factor analysis framework in Mplus.

December 6, 2016
2:00-3:00 pm
Taylor Education Building 122


Presentation 4: Missing Data Handling with Mplus

Missing Data Handling with Mplus Video

Missing Data Handling with Mplus Handout – (Powerpoint)

Missing Data Handling with Mplus Data Files – (Zip file)

James L. Peugh, PhD

This presentation focused on how to handle missing data for Four SEM-Based Analyses (Categorical CFA with Covariate (MIMIC Model), Moderated Mediation (MacArthur Method), SEM with a Latent Variable Interaction Term, and Multilevel ANCOVA SEM.

March 1st
12:30 – 2 pm
109 Dickey Hall


Presentation 5: Mediation Analysis and More

Mediation Analysis and More Video.

Mediation, Moderation, and Measurement Error, oh My! (Powerpoint)

Mediation Data and Analysis (zip file)

Moderation Data and Analysis (zip file)

David M. Dueber, MA

This presentation will focus on modern mediation concepts and analysis in Mplus including Structural Equation Modeling (SEM) methods to account for measurement error.

May 2, 2017
12:00 – 1:30 pm
Taylor Education Building 236B


2015-2016
“Instructional Guide to Applying an Item Response Analysis”

Abbey Love


“Introduction to Thurstonian Item Response Analysis”

Abbey Love


“Introduction to General Growth Mixture Modeling”

Trisha Turner


“Instructional Guide to Logistic Regression and Discriminant Analysis”

Caihong Li