led by Louise Rowllings and Marifatul Amalia, PhD students at the University of Edinburgh
This workshop will run over two consecutive mornings: the first day (25th September) will focus on the importance of the data cleaning process when working with secondary quantitative data; a step-by-step guide to data cleaning and how to plan effectively. The second day (26th September) will focus on undertaking data preparation in STATA, including use of do. files, and learners will have the opportunity to conduct data cleaning with an example data set.
The workshop will be held on Microsoft Teams, and will be a mix of presentations by the facilitators, smaller breakout sessions and Q&A. All attendees should have access to STATA software for the practical aspects of the workshop (we will be using STATA version 17, although learners may use the version most familiar to them).
The workshop will not cover the following aspects in detail, but we will situate the data preparation process within the overall research process and make reference to the following:
- Defining the research question and formulating hypothesis
- Sourcing suitable secondary quantitative data for analysis
- Statistical / econometric methods suitable for modelling the relationship of interest
For the practical aspects of the workshop, we will use a secondary dataset sourced from the UK Data Service (e.g. English Longitudinal Study of Ageing). The dataset provided will be suitable for using in a workshop environment.
Learning Outcomes:
By the end of the training, learners will be able to:
- Explain the importance of data preparation when undertaking quantitative analysis using secondary data
- Identify common issues and challenges when working with secondary data
- Document a step-by-step process to prepare their secondary data for analysis
- Identify missing, erroneous and outliers in the data and implement strategies to handle them
- Recode, transform and create new variables to meet research requirements
- Execute a variety of commands in STATA for efficient data preparation, including use of STATA do. files
- Label and document variables
- Situate the data preparation process within the requirements of the overall research project