Applications of Hierarchical Linear Models

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This workshop teaches the concepts and analysis of multilevel data through multilevel models (also known as hierarchical linear models or mixed models). With understanding of basic linear regression concepts as a prerequisite, the instructors will cover a wide range of topics including clustered data, longitudinal studies, and clustered longitudinal data. Participants will be introduced to the use of HLM 7.0 software. The workshop will consist of lively lectures and hands-on examples using HLM software.Many studies in social sciences (e.g., education, human development, public health, sociology) are multilevel, longitudinal, or both. Multilevel data arise when participants are clustered within social settings. The variation and covariation within and between such settings are often of interest substantively and should not be ignored when assessing relationships between explanatory variables and outcomes. In longitudinal research, we repeatedly observe subjects. These repeated measures for each participant will be correlated and explanatory variables may be time-varying or time-invariant. This workshop will consider the issues of analysis that arise in multilevel and longitudinal research settings.

We will first consider two-level cross-sectional studies in which persons (level-1) are nested within groups (level-2). The level-1 model specifies a process within each group, and the level-2 model explains how these processes are different between groups. Next, we will discuss two-level studies of individual growth and compare the structures of these studies to multilevel studies. We will also consider three-level models. We will focus on the case in which repeated measures (level-1) are nested within persons (level-2) who are themselves nested in organizations (level-3).

All of these studies will involve nearly continuous outcomes for which the normality distribution is at least plausible. They will also feature purely nested designs (e.g., persons nested within organizations). The workshop will provide participants with an overview of other types of applications where hierarchical linear models or generalized hierarchical linear models are appropriate (e.g., binary outcomes), and briefly discuss how the HLM software could be used to model such data.

Advanced Stata

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Note: Topic order is subject to change.  Participants must sign up for the entire series.

This workshop provides additional Stata training on topics more advanced than those covered in the Introduction to Stata workshop. Models for clustered/longitudinal data will be discussed along with other regression modelling techniques such as quantile regression and multinomial logistic regression. Structural Equation Modelling and Survival Analysis in Stata will also be discussed. The workshop will end with an introduction to programming in Stata using .do files. Basic looping techniques and macros will be covered. Note that an entire workshop will be offered in spring term on Programming in Stata. This workshop is designed to teach participants how to implement the methods outlined above in Stata and only a brief overview of the theory behind these methods will be covered. Participants should have a working knowledge of Stata as a prerequisite.

Statistical Analysis with R

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This workshop will introduce participants to R. R is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, the most unique feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphics are another reason R is gaining wide popularity.

  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive Statistics
  • Multivariate Statistical Analyses (Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

Issues in Analysis of Complex Sample Survey Data

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This workshop will provide participants with an introductory, hands-on overview of issues frequently encountered when conducting secondary computer analyses of survey data collected from samples with complex, multi-stage designs (e.g., PSID, NHANES, NCS), including design-based weight determination, software choice, and proper analysis methods.  The workshop is not intended for participants looking to design a survey, but rather for participants who have a desire to analyze complex sample survey data.  Topics to be covered include:

  • Recognizing a sample with a complex design
  • Calculation of sample weights based on sample designs / non-response / post-stratification
  • Calculation of new weights for subgroups / longitudinal analyses
  • Weighted vs. unweighted analyses
  • Calculation of correct confidence intervals for population quantities
  • Hypothesis Testing based on sample estimates
  • Design Effects
  • Software packages capable of complex sample survey data analysis
  • Common analysis methods (linear modeling, descriptive statistics), interpretation of results
  • Approaches to handling missing data using specialized software procedures
  • Hands-on examples using software programs to analyze real survey data

Issues in Analysis of Complex Sample Survey Data

Register
SummaryView full course description

This workshop will provide participants with an introductory, hands-on overview of issues frequently encountered when conducting secondary computer analyses of survey data collected from samples with complex, multi-stage designs (e.g., PSID, NHANES, NCS), including design-based weight determination, software choice, and proper analysis methods.  The workshop is not intended for participants looking to design a survey, but rather for participants who have a desire to analyze complex sample survey data.  Topics to be covered include:

 

  • Recognizing a sample with a complex design
  • Calculation of sample weights based on sample designs / non-response / post-stratification
  • Calculation of new weights for subgroups / longitudinal analyses
  • Weighted vs. unweighted analyses
  • Calculation of correct confidence intervals for population quantities
  • Hypothesis Testing based on sample estimates
  • Design Effects
  • Software packages capable of complex sample survey data analysis
  • Common analysis methods (linear modeling, descriptive statistics), interpretation of results
  • Approaches to handling missing data using specialized software procedures
  • Hands-on examples using software programs to analyze real survey data

 

Applied Structural Equation Modeling

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This workshop is designed to help participants develop skills in defining, estimating and testing structural equation models. Applied Structural Equation Modeling will focus on covariance structure models with latent variables. Two submodels, confirmatory factor analysis and path analysis, will also be covered.  Lectures covering structural equation modeling in general will be interspersed with hands-on computer work. The workshop is intended as an introduction to structural equation modeling. The software used is Stata–no prior use of Stata is required