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Methods Training

Our department offers cutting-edge, advanced methods training for graduate students to prepare them to be avid consumers and producers of research. Our faculty have expertise in advanced research design and statistical methods in applied research settings. Our methods training program reflects this applied focus, by helping students apply advanced statistical techniques to real-world scientific questions. 

Required methods courses for HDFS Ph.D. students:
HDFS 880:  Research Design and Measurement
HDFS 881:  Quantitative Research Methods
HDFS 960: 

Applied Multivariate Data Analysis

Prerequisite:
HDFS 881 or equivalent introductory graduate statistics course

 

Advanced methods electives:
HDFS 961:

Applied Structural Equation Modeling

Prerequisite:
HDFS 881 or equivalent introductory graduate statistics course;
HDFS 960: Applied Multivariate Data Analysis or equivalent is encouraged
HDFS 962:

Longitudinal Structural Equation Modeling

Prerequisite:
HDFS 961: Applied Structural Equation Modeling or equivalent
HDFS 892

Measurement

Prerequisite:
HDFS 881 or equivalent introductory graduate statistics course

For non-HDFS students interested in enrolling, please contact us at: HDFS.methods@hdfs.msu.edu

Method Instructors:
Dr. Ryan P. Bowles is an expert in Rasch measurement, Item Response Theory, and Structural Equation Modeling with categorical outcomes. His research focuses on the assessment of early childhood language and literacy development. Dr. Bowles teaches Quantitative Research Methods, Applied Multivariate Data Analysis, Applied SEM, Longitudinal SEM, and Measurement.
Dr. Megan Maas specializes in latent class methodologies, longitudinal survey research, and dyadic data analysis to understand sexual behaviors, how online and offline sexual behaviors change over time, and how partners in intimate relationships affect one another. Dr. Maas teaches Research Design and Measurement and Quantitative Research Methods.
Dr. Amy K. Nuttall is an expert in structural equation modeling (SEM), longitudinal data analysis (LSEM), and mixture modeling. She employs these methods in longitudinal data (including intensive longitudinal “diary” sampling), family/dyadic data, and biological data to understand the impact of family processes and dynamics on development over time. Dr. Nuttall teaches Applied Multivariate Data Analysis, Applied SEM, and Longitudinal SEM.
Dr. Yijie Wang’s research employs experience sampling and longitudinal designs to understand development in daily lives and over time. Her work also uses secondary data from nationally representative samples such as the National Longitudinal Study of Adolescent to Adult Health (Add Health) and the Early Childhood Longitudinal Study (ECLS). Dr. Wang teaches Applied Multivariate Data Analysis.