Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS®
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Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS®
Duration: 14 hours
BHLNM : BHLM42
Learn How To
  • Use basic multilevel models.
  • Use three-level and cross-classified models.
  • Use generalized multilevel models for discrete dependent variables.
  • Who Should Attend
    Researchers in psychology, education, social science, medicine, and business, or others analyzing data with multilevel nesting structure
    Prerequisites
    Before attending this course, you should:;
  • Preferably, be familiar with the basic structure and concepts of SAS (for example, the DATA step and procedures).
  • Be familiar with concepts of linear models such as regression and ANOVA and with generalized linear models such as logistic regression.
  • Be familiar with linear mixed models to enhance understanding, although this is not necessary to benefit from the course.;It is recommended that you complete SAS Programming 1: Essentials and Statistics 2: ANOVA and Regression or have equivalent knowledge before taking this course.
  • SAS Products Covered
    SAS/STAT
    Course Outline
    Introduction to Multilevel Models
  • Nested data structures.
  • Ignoring dependence.
  • Methods for modeling dependent data structures.
  • The random-effects ANOVA model.
  • Basic Multilevel Models
  • Random-effects regression.
  • Centering predictors in multilevel models.
  • Model building.
  • A comment on notation (self-study).
  • Intercepts as outcomes.
  • Slopes as Outcomes and Model Evaluation
  • Slopes as outcomes.
  • Model assumptions.
  • Model assessment and diagnostics.
  • Maximum likelihood estimation.
  • The Analysis of Repeated Measures
  • The conceptualization of a growth curve.
  • The multilevel growth model.
  • Time-invariant predictors of growth (self-study).
  • Multiple groups models.
  • Three-Level and Cross-Classified Models
  • Three-level models.
  • Three-level models with random slopes.
  • Cross-classified models.
  • Multilevel Models for Discrete Dependent Variables
  • Discrete dependent variables.
  • Generalized linear models.
  • Multilevel generalized linear models.
  • Additional considerations.
  • Generalized Multilevel Linear Models for Longitudinal Data (Self-Study)
  • Complexities of longitudinal data structures.
  • The unconditional growth model for discrete dependent variables.
  • Conditional growth models for discrete dependent variables.
  • THIS COURSE IS PART OF

    SAS Social and Behavioral Research​ Learning Subscription



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