Introduction/Overview

Understanding key statistics and choosing an appropriate statistical test are an essential part of a quantitative researcher’s skill set. The course will provide a foundation in statistical concepts and essential analyses. After completing the course participants will understand the statistics underlying statistical software outputs and be able to evaluate the appropriateness of each analysis for the data and research objective.

Statistical software packages are not taught in the course. Instead there are 5 workshops over the day where each stage of computing and interpretation of statistics is practised.

Topics covered

• Overview of statistical terms and concepts
• Overview of analysis considerations
• Some do’s and don’ts of graphs and tables
• Measures of central tendency and dispersion
• Confidence Intervals
• Hypothesis testing
• Chi-square test of independence
• t-test and ANOVA
• Regression
• Beyond p-values: revisiting hypothesis testing

Objectives

By the end of the course, participants will:

• Have a good understanding of key statistical terms and concepts
• Understand the practical implications of descriptive versus inferential statistics
• Understand when to choose a parametric or non-parametric testing approach
• Understand how, when and why to create confidence intervals for means and proportions
• Understand which analysis is most appropriate for the data and research aim
• Know how to use appropriate graphs and tables for reporting their data
• Know how to use a range of statistics to evaluate the quality of the research and strength of finding

Who will benefit?

• Participants completely new to statistics with a keen interest in research
• Participants who have had some experience of statistics and would like to refresh and enhance their knowledge
• Participants with recent statistical experience, who have not been formally trained in quantitative research may also benefit as the course is also designed to fill holes in existing knowledge.

The course provides key knowledge in frequentist statistics that will serve as a foundation for building discipline-specific knowledge.

This course is suitable for anyone who undertakes quantitative research in the area of social research, sociology, psychology, politics, biology, mathematics, and financial and economic research.

Learning outcomes

• Participants will have a good understanding of statistics and their use in descriptive situations, in graphs and tables, and in basic hypothesis testing.
• Participants will understand and evaluate results from t-tests, chi-square tests of independence, ANOVA and regression analyses.

Course tutor

Valerija Kolbas is a Senior User Support and Training Officer at the UK Data Archive within the secure data collections.Valerija is passionate about promoting research skills, encouraging data-driven decision-making and improving statistical literacy. She believes in ethical research and is using her experience to teach essential statistics and promote good research practice. Valerija has several years of experience in teaching research methods in higher education. In her current role, Valerija regularly delivers training for researchers on statistical disclosure control of sensitive data and legal responsibilities around data use. She develops training materials on conducting research using secondary data and methods of data preparation and analysis.

Valerija has worked on a number of projects collecting data for cohort, panel, and cross-national studies at every stage of data collection and dataset preparation. Valerija holds a PhD in Survey methodology from the Institute for Social and Economic Research, University of Essex.

  This course contributes 6 hours to the MRS CPD programme