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Statistics and Research Methods: an Introduction

  • 25 hours
  • 5 days

Overview

This five-day short course will give you a comprehensive introduction to the fundamental aspects of research methods and statistics. It's suitable for those new to quantitative research.

You'll look at topics ranging from study design, data type and graphs through to choice and interpretation of statistical tests - with a particular focus on standard errors, confidence intervals and p-values.

You can also take this course over 10 evenings.

This course is delivered by UCL's Centre for Applied Statistics Courses (CASC) - part of the UCL Great Ormond Street Institute of Child Health (ICH).

Course content

During this basic introductory course in research methodology and statistical analyses you'll cover a variety of topics.

This is a theory-led course, but you'll be given plenty of opportunities to apply the concepts via practical and interactive activities integrated throughout.

The topics covered include:

  • Introduction to quantitative research
  • Research question development
  • Study design, sampling and confounding
  • Types of data
  • Graphical displays of data and results
  • Summarising numeric and categorical data
  • Numeric and categorical differences between groups
  • Hypothesis testing
  • Confidence intervals and p-values
  • Parametric statistical tests
  • Non-parametric tests
  • Bootstrapping
  • Regression analysis

Many examples used in the course are related to health research, but the concepts you'll learn about can be applied to most other fields.

Eligibility

The course is suitable for those new to quantitative research.

Learning outcomes

By the end of this course you should have a good, practical understanding of:

  • research design considerations (question formulation, sample selection and randomisation, study design, and research protocols)
  • data types, and appropriate summaries and graphs of samples and differences
  • standard errors, confidence intervals and p-values
  • parametric and nonparametric assumptions and tests
  • how to select an appropriate statistical test

Cost and concessions

The fees are:

  • External delegates (non UCL) - £750
  • UCL staff, students, alumni - £375*
  • ICH/GOSH staff and students - free

* valid UCL email address and/or UCL alumni number required upon registration.

Workbooks, online material and refreshments each day (including lunch for non-ICH participants) are included in the price.

Certificates

You can request a certificate of attendance for all of our courses once you've completed it. Please send your request to ich.statscou@ucl.ac.uk

Include the following in your email:

  • the name of the completed course for which you'd like a certificate
  • how you'd like your name presented on the certificate (if the name/format differs from the details you gave during registration)

Cancellations

You can cancel your booking up to five working days before the start of the course for a full refund, but please give as much notice as possible. Places cancelled or changed after this point won't be eligible for a refund. Please send all cancellation requests to ich.statscou@ucl.ac.uk

Find out about CASC's other statistics courses

CASC's stats courses are for anyone requiring an understanding of research methodology and statistical analyses. The courses will allow non-statisticians to interpret published research and/or undertake their own research studies.

Find out more about CASC's full range of statistics courses, and the continuing statistics training scheme (book six one-day courses and get a seventh free.)

Sign up for short course announcements: Subscribe to the UCL Life Learning newsletter to receive news and updates on courses in your chosen area. (For updates on a specific course, contact the administrator - see 'Contact information'.)

Course team

Dr Dean Langan - Course Lead

Dean joined GOS ICH in 2015 as a Teaching Fellow in CASC. He has a Bachelor’s degree in Mathematics from University of Liverpool, a Master's degree in Medical Statistics from University of Leicester, and a PhD from University of York for his research in statistical methods for random-effects meta-analysis. He's worked as a statistician on a number of clinical trials related to stroke and myeloma at the Clinical Trials Research Unit in Leeds. His specialist areas include statistical methods for meta-analysis, R programming, clinical trial methodology and research design.


Dr Dan Green - Course Lead

Dan joined GOS ICH in May 2017 as a Teaching Fellow in CASC. He has a Bachelor’s degree in Mathematics and a Master's degree in Medical Statistics from University of Leicester. He was awarded a NIHR Research Methods Fellowship, hosted at the Arthritis Research UK Primary Care Centre within Keele University. He started a PhD at the same department in 2012, with a NIHR School for Primary Care Research Studentship, exploring hand symptoms over a six year follow-up using latent-related methodology. During his six years at Keele, Dan was involved in numerous applied observational studies and a clinical trial that included a variety of statistical methods, with specialties including latent class analysis (and extensions), STATA programming and survival analysis.


Eirini Koutoumanou - Course Lead

Eirini joined GOS ICH in 2008 as the first CASC Teaching Fellow and was promoted to Senior Teaching Fellow in 2014. She has a Bachelor’s degree in Statistics from the Athens University of Economics and Business and a Master's degree in Statistics from Lancaster University, and she's currently studying for a PhD at ICH. Eirini started teaching at ICH straight after her student days, putting into practice and further developing her passion for statistics teaching. She's played an instrumental role in the formation of CASC and hopes to see it develop further.


Student review

"All sessions were exceptionally organised and presented in a clear and engaging style. The lecturers were incredibly knowledgeable and flexible and patient to the different levels of understanding in the room. The key concepts of making inferences set out at the beginning and carried throughout were especially helpful."


“Explaining the visual representation of data was very useful, as was having examples in the workbooks to learn from and 'correct'.”


“The most memorable session for me was the one about significance testing. I am sure it will be very useful in my practice.”


Course information last modified: 12 Mar 2019, 16:52

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