Essentials of Survey Research Design

Organised by

Royal Statistical Society

Presenter

Dr. Pamela Campanelli

Date

22/06/2021 - 23/06/2021

Venue

Online

Map

View in Google Maps  (EC1Y 8LX)

Contact

training@rss.org.uk

Description

Surveys are a key component of quantitative research whether this is for government, academia, research institutes, or other organisations. The recent explosion of easy-to-use web survey packages means that everyone thinks they can do a survey. However, generating statistically unbiased and reliable data is more complex than writing a few questions. Without proper instruction, the conclusions drawn from a survey can be flawed and misleading. This course looks at each of the quality areas for producing a successful and efficient quantitative survey. It covers all the major methods of data collection (quantitative interviewing, web and paper self-completion) as well as all the key aspects of the survey process from initial design through implementation through post-field processing. It focuses on the practical details of conducting a survey as well as including crucial tips from what is known in the survey methodology and statistical literature.


Course Outline

  • An overview of survey design (including types of survey designs, project management, time-tabling and budgeting) (with DISCUSSION)
  • Choosing a method of data collection (face-to-face, telephone, paper self-completion, web surveys) (with WORKSHOP)
  • What to look for in a good sample design (types of probability and non-probability samples and general population internet panels; 7 considerations for sample size)
  • Key principles of questionnaire design (for individual questions and the questionnaire as a whole) (with WORKSHOP)
  • How to test your questionnaire in an effective manner.
  • A thorough look at ways to minimising nonresponse/nonresponse bias during field work for
    all methods of data collection (with WORKSHOP)
  • An overview of coding and data processing, but including detailed introductions to
    weighting and imputation (with WORKSHOP)
  • Analysis (Not covered as there are other RSS courses that cover analysis)
  • What makes a good survey report
    Appendices with:
  • Approaches to ensure quality (best practice, total survey error, total quality management)
  • Ethical considerations

Learning Outcomes

  • Have a better awareness of quality areas for producing a successful and efficient quantitative survey
  • Be able to critique aspects of existing surveys from both the practical and statistical sides
  • Have knowledge to conduct or improve the quality of their own surveys


Learning Outcomes

Attendees with no experience in GAM modelling will get an understanding of what GAM models are, when are they useful and how can they be used to perform statistical analysis, for inferential or predictive purposes. Attendees who have some experience with GAMs will learn about the new Big Data and visual GAM methods, as well as about GAMLSS models and quantile GAMs.

Topics Covered

  • model building, inference and fitting methods 
  • key Bayes empirical smoothing theory
  • types of smooth and random effects
  • visual methods and diagnostics 
  • Generalized Additive Models for Location Scale and Shape
  • Quantile GAMs
  • GAM modelling in R


Target Audience

The target audience are practictioners, either in industry or academia, interested in learning new powerful statistical methods, which can be used in a wide variety of applications such as rainfall modelling, electricity demand forecasting, survival analysis and air pollution modelling to name a few.


Knowledge Assumed

Attendees should have some background on (linear) regression modelling, and a good understanding of fundamental statistical concepts such as probability densities, quantiles, etc. Some basic proficiency with R (eg. loading data, accessing data frames, basic use of the lm() function), at a level equivalent to a couple of days of self-study, is also assumed. 

Attendees will need to bring a laptop with a recent version of R installed. Prior to the course attendees will be asked to install some additional R packages.

Level

Entry (no or almost no prior knowledge)

Cost

£588 - £816 (inc. VAT)

Website and registration

Region

Greater London

Keywords

Quantitative Data Handling and Data Analysis

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