Multi-level Analysis using Mplus

 
This course is designed as an introductory course in multi-level analysis using the Mplus program. Multi-level analysis should be considered in any research where there is a hierarchical sampling design. That is, whenever observations from subjects are nested within groups or whenever repeated measures are nested within subjects. For example, in education we may have sampled all students within a classroom and then sampled many classrooms. We would say the students are nested within classrooms. In Business we may have sampled employees nested within different companies. In health we may have sampled patients nested within different hospitals. Alternatively, repeated measure designs should be treated as multi-level analysis because the repeated observations are nested within subjects. For example, in marketing we may repeatedly question consumers about their attitudes to a brand over the life of a marketing campaign. This course will investigate a range of two-level research questions and is divided into four parts.
 
Part 1: Introduction to Mplus through revision of basic models. This part of the course introduces the Mplus programming language by revising basic models such as regression, factor analysis and simple structural equation models.
 
Part I1: Introduction to Multi-level modeling. This part of the course introduces the fundamentals of Multi-level modeling noting the difference between the standard (single-level) regression approach and the multi-level regression approach. The dangers of not treating nested data as multi-level data will be explored and the advantages of multi-level analysis will be described.
 
Part III: Investigating types of multi-level models using Mplus. This part of the course will demonstrate a range of multi-level models. The Mplus syntax required to run such models will be introduced and a thorough interpretation of the output from Mplus will be explained. Types of models will include:
• Multi-level Regression Models. These models investigate two-level research questions where subjects are nested within groups and explanatory (independent) variables have been measured at the subject level (Level 1) and/or the group level (Level 2). For example, in education, our outcome variable may be “reading comprehension” and we could regress this outcome on both Level 1 independent variables (e.g. the student’s verbal reasoning skills, their motivation to learn, etc.) and Level 2 independent variables (e.g. the teachers experience, the average ability of all students within the class, etc.)
• Latent Growth-Curve Modeling (Repeated Measure designs). These models investigate change over time and enable the researcher to describe how an outcome (dependent) variable is improving (or declining) across a number of repeated measures. For example, in marketing we may be interested in analysing consumers’ attitudes to a brand over the life of a marketing campaign. The repeated measure (attitudes) may improve as a function of time but different marketing techniques (a time varying independent variable) may also influence the rate of improvement. 
• Part IV: Advanced models. These models include multi-level SEM (an extension of multi-level regression models) in that we model multiple outcomes (dependent variables) and the relation amongst both these multiple outcomes and other independent variables. We will also introduce Mixture Modeling. These models investigate underlying subpopulations within your data where membership is not known but can be inferred from the data. 
 
Part V: Personal Research. Finally, on part of the last day, participants have an opportunity to work on their own research problems with the instructor’s assistance. Therefore participants are encouraged to bring a multi-level data set and/or research problem with them.