Fall 2020 Course Available: CEP 982 Seminar in Counseling, Educational Psychology, & Special Education

August 10, 2020

Fall 2020 Course Offering
CEP 982: Seminar in Counseling, Educational Psychology,
& Special Education
Section 001, Online

Statistical Toolbox: Seminar on Advanced Quantitative Techniques

Course Content

This class will cover a number of statistical methods that are used in the social sciences but are not typically taught in general applied statistics classes. The object of the class will be to provide a brief introduction to the logical and mathematical basis of each method, supplemented with a case study of the application of the method. In this seminar, students will choose a topic, present an introduction to the topic, a review of its application to data, a summary of software packages available (if applicable), and suggestions for further reading on the topic. The topics we will examine during the semester will be chosen by students and faculty from a large list of topics. At the first class meeting, we will brainstorm and finalize the list of topics to be covered during the semester.

Course Format

The course will have weekly synchronous meetings on Thursdays 1-2:30pm, with an asynchronous delivery of the remainder of content. As such, access to Zoom and suitable internet to support video conferencing is required.

Prerequisite Knowledge

Knowledge of basic algebra is needed. It is recommended that students have completed CEP 932 & CEP 933, or other applied introductory statistics sequence. Completion of STT 441/861 would be helpful, but is not required.

Some Example Topics:

  • Statistical Estimation – Maximum Likelihood Estimation (MLE)
  • MLE – EM Algorithm
  • MLE – Grid Search
  • MLE – Method of Scoring, Newton-Raphson and Fletcher-Powell algorithms
  • MLE – Univariate and Multivariate Delta Method Statistical Estimation – Other Related
  • Balanced Repeated Replication (BRR)
  • Bootstrapping & Jackknife
  • Empirical Likelihood
  • Plausible Values
  • Grouping or Classifying Data
  • Cluster Analysis
  • Data mining & classification
  • Principal Components Analysis and Weighted Combinations

Statistical Modeling

  • Event history analysis
  • Generalized linear model
  • Growth curve analysis
  • Latent class model
  • Logistic regression
  • Longitudinal data modeling
  • Ordinal regression
  • Quantile regression

Data Analysis Strategies/Methods

  • Gain/difference scores
  • Graphical exploratory data analysis
  • Multiple Comparisons and Simultaneous Inference
  • Power analysis
  • Sensitivity analysis
  • Dimension Reduction

Data Analysis Issues

  • Outliers – identification and treatment
  • Recoding of Variables
  • Treatment of Missing data
  • Causal Inference Related
  • Causal modeling
  • Instrumental variables
  • Propensity score analysis
  • Randomized experiments
  • Regression discontinuity

Tools

  • Design and analysis of statistical simulations (including random number generators)
  • Linear, quadratic programming
  • Matrix derivatives
  • Monte Carlo integration
  • Quadratic forms and their distributions
  • Other topics of interest
    • Value added modeling

Other topics?

For more information contact Kim Kelly (kmaier@msu.edu).