22nd February: Multilevel Modelling
This one-day course begins with a description of some examples where multilevel models are useful in statistical analysis and some examples of multilevel populations. We then cover the basic theory of multilevel models including random intercept and random slope specifications, the use of contextual variables in multilevel analysis and modelling repeated measures. This course is suitable for social scientists who want to learn about a quantitative technique that allows both individual and group level variations to be simultaneously taken into account when modelling social phenomena.
21st March: Practical Skills for Data Analysts
This course will show participants how to use statistical analysis software, in this instance SPSS. In the course, participants will be introduced to the SPSS environment as well as useful key concepts (such as cases, variables, values, and levels of measurement) to perform basic descriptive data analysis. A key component of the course involves getting familiar with basic SPSS data analysis commands in hands-on sessions. By the end of the day, participants will be familiar with the software; know how to open a data file, enter data, and do basic data manipulations; and be able to produce simple descriptive statistics, one- and two-way tables, as well as simple graphs.
This is a foundation course that does not require any previous experience of SPSS and that will provide participants with the appropriate background to progress to other CMIST courses, particularly Introduction to Data Analysis 1 and Introduction to Data Analysis 2.
12th April: Introduction to Longitudinal Data Analysis
The course covers basic concepts in longitudinal design and analysis.
The morning session focuses on the strengths and methodological difficulties of the longitudinal approach such as defining longitudinal populations and target samples; levels and dimensions of change; age, period and cohort effects. There will be one small group discussion.
After lunch, we start with an overview session on the sources and causes of missing data (attrition, etc.), and how to adjust for missingness; this will be followed by another group exercise.
13th April: Longitudinal Data Analysis
The course covers two of the most useful ways of analysing longitudinal data. In the morning we cover growth curve analysis within a multilevel modelling framework. The theoretical ideas are embellished with practical work using data from the National Child Development Study. After lunch, basic concepts in survival analysis and event history analysis are introduced followed by practical work with a simple (pencil and paper) example.