The Applied Psychometric Strategies (APS) lab was directed by Dr. Michael Toland until Spring of 2020, when he took an administrative position at another university. This website is in the process of being shut down. Until the contents of the Events and Resources webpages are relocated to an external website, the Events and Resources webpages will continue to stay temporarily operational, as they contain content that is useful to people interested in psychometrics. However, website visitors who access these webpages must understand that the materials on these APS webpages are no longer being monitored or curated, and EDP Department faculty are not in a position to answer questions or fulfill requests related to this content. Therefore, please utilize the content at your own risk.
If you are interested in the Department of Educational, School, or Counseling Psychology, visit the EDP homepage.
If you are interested in the Quantitative and Psychometric Methods (QPM) program at UK, visit the QPM homepage.
The Applied Psychometric Strategies lab previously provided a series of presentations on a range of quantitative and psychometric topics. Below is a partial record of these presentations. Some include links to video recordings and/or materials associated with the presentations.
Past Events
2019-2020Presentation 1: Introduction to Longitudinal Data Analysis
Rongxiu Wu (Doctoral Candidate, Educational Psychology)
Spring 2019
Presentation 2: Introduction to Item Response Theory
Chen Qiu (Doctoral Student, Quantitative and Psychometric Methods)
Spring 2019
Presentation 3: How to do an Item Response Theory Analysis with IRTPRO and flexMIRT
Chen Qiu (Doctoral Student, Quantitative and Psychometric Methods)
Spring 2019
Presentation 4: Introduction to Item Response Theory Based Differential Item Functioning
Falynn Thompson (Doctoral Candidate, Educational Psychology)
Spring 2019
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)
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 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 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