Theme: Software Packages
7 matching courses
This course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between mini-lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions, in which you will learn how to analyse qualitative data using the software.
The sessions will introduce participants to the following:
- consideration of the advantages and limitations of using qualitative analysis software
- setting-up a research project in Atlas.ti
- use of Atlas.ti's menus and tool bars
- importing and organising data
- starting data analysis using Atlas.ti’s coding tools
- exploring data using query and visualization tools
Please note: Atlas.ti for Mac will not be covered.
This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:
- Ways of reading data into Python
- How to manipulate data in major data types
- How to draw basic graphs and figures with Python
- How to summarise data using descriptive statistics
- How to perform basic inferential statistics
This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques.
This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface. Students will learn:
- Ways of reading data into R
- How to manipulate data in major data types
- How to draw basic graphs and figures with R
- How to summarise data using descriptive statistics
- How to perform basic inferential statistics
This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques.
For an online example of how R can be used: https://www.ssc.wisc.edu/sscc/pubs/RFR/RFR_Introduction.html'''
The course will provide students with an introduction to the popular and powerful statistics package Stata. Stata is commonly used by analysts in both the social and natural sciences, and is the statistics package used most widely by the SSRMP. You will learn:
- How to open and manage a dataset in Stata
- How to recode variables
- How to select a sample for analysis
- The commands needed to perform simple statistical analyses in Stata
- Where to find additional resources to help you as you progress with Stata
The course is intended for students who already have a working knowledge of statistics - it's designed primarily as a ""second language"" course for students who are already familiar with another package, perhaps R or SPSS. Students who don't already have a working knowledge of applied statistics should look at courses in our Basic Statistics Stream.
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled.
The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.)
MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here
More information on the course can be found here
The module explores Good Data Visualisation (GDV) and graph creation using Python.
In this module we demystify the principles of data visualisation, using Python software, to help researchers to better understand and reflect how the “5 Principles” of GDV can be achieved. We also examine how we can develop Python’s application in data visualisation beyond analysis. Students will have the opportunity to apply GDV knowledge and skills to data using Python in an online Zoom, self-paced, practical workshop. In addition there will be post-class exercises and a 1-hour asynchronous Q&A forum on Moodle Forum.
The data we obtain from survey and experimental platforms (for behavioural science) can be very messy and not ready for analysis. For social science researchers, survey data are the most common type of data to deal with. But typically the data are not obtained in a format that permits statistical analyses without first conducting considerable time re-formatting, re-arranging, manipulating columns and rows, de-bugging, re-coding, and linking datasets. In this module students will be introduced to common techniques and tools for preparing and cleaning data ready for analysis to proceed. The module consists of four lab exercises where students make use of real life, large-scale, datasets to obtain practical experience of generating codes and debugging.