Resources for:
  • Current Students
  • Faculty & Staff
  • Alumni
  • Directory
  • News
  • Events
UCI School of Education
  • About Us
    • Dean's Welcome
    • Our Mission & Vision
    • Facts & Information
    • Climate Council
    • Maps & Directions
  • Academics
    • Ph.D. in Education
    • MAT + Credential
    • Undergraduate
  • Community Engagement
    • Overview
    • Teacher Academy >
      • California Reading & Literature Project
      • UCI CalTeach
      • UCI History Project
      • UCI Math Project
      • UCI Science Project
      • UCI Writing Project
    • Orange County Educational Advancement Network
    • Center for Educational Partnerships >
      • SAGE Scholars Program
      • COSMOS
      • California Alliance for Minority Participation
    • Center for Research on Teacher Development and Professional Practice
  • Faculty
    • Our Faculty
    • Faculty Interviews
    • Centers
    • publications
  • Giving

"Latent classes from complex assessments: What do they tell us?"

11/25/2020

 
Alumnus Ryan W. Lewis (Ph.D. ’18, pictured left), and Associate Professor Drew Bailey (right) published an article in Learning and Individual Differences on the practical effectiveness of Latent Class Analysis for identifying patterns of student knowledge.
 
The title of the article is “Latent classes from complex assessments: What do they tell us?”
 
Adjunct Professor/Senior Research Fellow Jake McMullen of University of Turku, Finland is first author.
Picture
Picture
​Lewis is a Research Associate at WestEd in San Francisco. He is an inter-disciplinary education researcher with a background in nonprofit education programming and advanced training in quantitative, qualitative, and applied research methods. He is interested in the intersection of education policy, program implementation, and targeted supports, specifically in settings that confront educational and social inequalities. For his doctoral work, Lewis specialized in Educational Policy and Social Context. Distinguished Professor George Farkas served as his dissertation advisor
 
Bailey's research interests include mathematical development, individual differences, and longitudinal methods. As a Jacobs Foundation Research Fellow (2019-2021), Bailey is studying the processes underlying the stability of individual differences in youngsters’ mathematical achievements and the medium- and long-term effects of early interventions. He serves as the UCI School of Education’s Faculty Director of Undergraduate Programs.
 
Abstract
 
Latent variable mixture models are commonly used to examine patterns of students' knowledge. These models, including Latent Class Analysis (LCA), have proven valuable for uncovering qualitative variation in students' knowledge that is hidden by traditional variable-centered approaches, particularly when testing a particular cognitive or developmental theory. However, it is far less clear that these models, when applied to broader measures of student knowledge, have practical applications, such as identifying meaningful and actionable knowledge patterns on standardized achievement tests. In the present study, we probe the practical effectiveness of LCA for identifying valid patterns of students' knowledge on broadly defined achievement tests that provide added predictive value beyond overall scores and other known indicators of success. We examined the performance of 3481 fifth-grade students from a mid-sized school district in the western United States on two benchmark assessments of their mathematics achievement during the school year. Latent classes extracted from pass-fail scores on specific standards measured by these assessments were then used to predict students' end-of-year performance on a statewide-standardized mathematics assessment. Latent classes generally showed face validity and identified qualitatively different knowledge patterns. The predictive value of class membership for end-of-year test scores was greatly reduced when adjusting for overall benchmark scores and very small after also adjusting for additional pre-existing differences among students. These results suggest that, although LCA might improve the interpretability of achievement test scores, their predictive value is largely redundant with overall scores. These results are tentative; we encourage replication with different kinds of data, especially with finer-grained measures.

Comments are closed.
Quick Links:

Fall 2021 Magazine
​Faculty & Research
Faculty Interviews
Directory
Admissions
​Giving
​News Center
Employment
Programs:
​
PhD in Education
MAT
Major in Edu Science
Minor in Edu Studies
CalTeach
CASE
Resources for:
​

​Current Students​
Faculty & Staff
University of California, Irvine
School of Education
401 E. Peltason Drive
Suite 3200
Irvine, CA  92617
(949) 824-8073

Picture
© ​2022 UC Regents 
  • About Us
    • Dean's Welcome
    • Our Mission & Vision
    • Facts & Information
    • Climate Council
    • Maps & Directions
  • Academics
    • Ph.D. in Education
    • MAT + Credential
    • Undergraduate
  • Community Engagement
    • Overview
    • Teacher Academy >
      • California Reading & Literature Project
      • UCI CalTeach
      • UCI History Project
      • UCI Math Project
      • UCI Science Project
      • UCI Writing Project
    • Orange County Educational Advancement Network
    • Center for Educational Partnerships >
      • SAGE Scholars Program
      • COSMOS
      • California Alliance for Minority Participation
    • Center for Research on Teacher Development and Professional Practice
  • Faculty
    • Our Faculty
    • Faculty Interviews
    • Centers
    • publications
  • Giving
  • Resources For:
  • Future Students
  • Current Students
  • Faculty & Staff

  • Search This Site
  • Directory
  • News
  • Events