Data processing & Data analysis

This is an introductory guide to the main types of data with a time element. The guide is a brief overview of key elements in need of consideration when using these types of data. It also covers data availability and some commonly used methods for studying change over time quantitatively. 

This guide explains the main reasons for using weights, how weights work and how to use weighting variables in statistical analysis. 

This guide aims to help researchers utilise extensive survey data available. In particular, this guide is designed to support those starting small research projects, especially students doing dissertations. 

The guide includes materials to read, worksheets for getting started and questions to think about and answer.

These quantitative methods e-books and accompanying quizzes are for direct use in teaching students or for self-study. They aim to build skills in quantitative methods and statistical software and use the Living Costs and Food Survey.

Using SPSS, lecturers and students can utilise both the practical and quiz elements of each e-book topic. Topics include examining variables, correlations, regression and multiple regression.

The e-books have been developed through a collaboration of the UK Data Service, National Centre for Research Methods (NCRM), and the Centre for Multi-Level Modelling at the University of Bristol and were created using the StatJR software based on original outputs from the project Using Statistical E-books to teach undergraduate students quantitative methods and statistical software funded by the British Academy.

Tutors can use this resource to create an assignment that enable students to learn to engage with a genuine, real-life piece of research. They are asked to complete specific tasks whilst working within word limits. 

This resource provides generic templates for both the tutor's pack and feedback sheets that can be adapted for courses in different sociological thematic areas. These are in MS Word so that they can be adapted as needed.

Chapter on the data operations needed to prepare data files for analysis and data sharing, starting with data entry and coding of data files. Throughout the different phases of research data files will be edited numerous times. During this process, it is crucial to maintain the authenticity of research information contained in the data and prevent it from loss or deterioration, as well as a comprehensive approach to data quality.

Focus on:

• strategies to minimise errors during the processes of data entry and data coding
• the choice of file formats
• managing the integrity and authenticity of data during the research process
• a systematic approach to data quality
• DMP questions on these topics.

Synergies for Europe's Research Infrastructures in the Social Sciences (SERISS) is a four-year project that aims to strengthen and harmonise social science research across Europe. It brings together leading European Research Infrastructures in the social sciences - ESS, the Survey of Health, Ageing and Retirement in Europe (SHARE ERIC) and Consortium of European Social Science Data Archives (CESSDA ERIC) - alongside organisations representing the Generations and Gender Programme (GGP), European Values Study (EVS) and the WageIndicator Survey.

Focused around three key themes – key challenges facing cross-national data collection, breaking down barriers between research infrastructures, and embracing the future of social sciences – SERISS will address issues relating to survey design and data collection, data management and curation from a collaborative, cross-national perspective. The project will better equip Europe’s social science data infrastructures to play a major role in addressing the key societal challenges facing Europe today and ensure that national and European policymaking is built on a solid base of the highest-quality socio-economic evidence.

SERISS has developed a suite of online tools, webinars and lectures to provide training on data management, data handling and harmonisation, as well as on statistical analysis to a wider social science community.