STATS 153 · STAT 153Hierarchical Linear Modeling
Statistics · 4 units · Undergraduate upper division (100-199)
Introduction to hierarchical linear modeling (HLM) with emphasis on theoretical foundations and applied analysis of multilevel data. Topics include formulation and interpretation of random-intercept and random-slope models, intraclass correlation, variance partitioning, centering strategies, cross-level interactions, and design considerations such as sample size at each level and construction of appropriate multilevel data structures. Applications to science, technology, engineering, and mathematics fields—including laboratory experiments nested within technicians, repeated measurements nested within experimental units, or engineering teams nested within projects—are highlighted, with brief extensions to education, psychology, sociology, and medicine. Focus is placed on conceptual understanding, theoretical grounding, hands-on programming skills in R, and accurate interpretation and communication of multilevel findings within context to both statistical and non-statistical audiences.
P/NP or letter grading.
When it runs
- Fall 2026
Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.
Requisites
Official UCLA wording
Requisites: courses 100B (or Mathematics 170S), 101A, 101B.
BruinTree reads · Prerequisite
confidence 1.00 · from UCLA’s structured dataRequires
Everything that has to come before this course, not just the courses named in the requisite above.
STATS 153
- STATS 100BIntroduction to Mathematical Statistics
- STATS 100AIntroduction to Probability
- MATH 170AProbability Theory I
- MATH 170EIntroduction to Probability and Statistics 1: Probability
- STATS 101AIntroduction to Data Analysis and Regression
- STATS 10Introduction to Statistical Reasoning
- STATS 12Introduction to Statistical Methods for Geography and Environmental Studies
- ECON 41Probability and Statistics for Economists
- STATS 13Introduction to Statistical Methods for Life and Health Sciences
- STATS 15Introduction to Data Science
- PSYCH 100APsychological Statistics
- COMPTNG 10AIntroduction to Programming
- PSYCH 10Introductory Psychology
- STATS 20Introduction to Statistical Programming with R
- STATS 12Introduction to Statistical Methods for Geography and Environmental Studiesanother path to it
- MATH 170SIntroduction to Probability and Statistics 2: Statistics
- STATS 101BIntroduction to Design and Analysis of Experiment
4 direct requisites. Showing 36 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
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What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.