To enrol for any of the courses below, please contact the CSTAR team on cstar@ul.ie  to request a registration form.  Numbers will be limited so please contact us asap if you wish to attend.  Event details will be communicated after enrolment a few days before each course to all registered attendees. (NB Another set of courses will be running in August) For information on courses on offer at CSTAR@UL, click here.

MAY 2019 COURSES:

Tue May 14th 2019 Questionnaire Design - one day course 9:30-16:30

This introductory course covers the basic elements of questionnaire design and question wording. Learn how to construct unbiased questions - the common mistakes and how to put them together in a survey and/or interview schedule that will be easily answered. The different requirements for postal and interview questionnaires as well as online surveys will be emphasised and practical exercises will be given in question wording. Various modes of presentation will be described. Some suggestions for ways of improving response rates will be given. The methodology behind reliability and validity of scores/scales will also be explained. The course is taught by an experienced researcher and tips and advice will be given for specific surveys. After the course participants will be able to send their questionnaires for review by SCU/CSTAR consultants when constructed.

Thu May 16th 2019: Introductory SPSS - one day course 9:30-16:30

This course provides an intensive introduction to SPSS. It assumes that participants have a basic familiarity with the Windows environment. We examine the features of SPSS for Windows, use a simple data set to cover the topics of transforming variables, selecting data for analysis, then perform basic analyses to produce frequency distributions, summary statistics and cross tabulations before examining some of the extensive graphic capabilities of SPSS. This course would provide an introduction to anyone wishing to analyse their own data using SPSS. This course is taught by an experienced statistician - advice can be sought during the day for specific SPSS problems. Those with sufficient statistical knowledge already would only need to go on this course in order to carry out any analyses. It is not recommended that SPSS and statistics be learnt at the same time.

Fri May 17th and Mon May 20th 2019 Basic Statistics for Researchers - two day course - both 9:30-16:30

This course covers the basic methods of analysis needed for quantitative research. Most researchers would find the material covered would be sufficient to analyse any data gathered as part of their research (both categorical and continuous/scalar variables). No prior knowledge of statistics is assumed although you will require a basic knowledge of using SPSS and/or other statistical software packages e.g. knowledge gained from the 'Introductory SPSS' course . The course uses sample data from the sciences and social sciences fields but the application is relevant to all subject areas. Topics covered include: Research Methodologies; Sampling; Data analysis – an overview; Types of data; Scales of data measurement; Coding questionnaire data; Describing data using graphical and numerical methods; Normal Probability distributions; Confidence Intervals and Hypothesis Testing (Parametric and non-parametric); Multivariable analysis – Categorical variables – Chi-squared Test; Multivariable analysis – Quantitative (continuous) variables – Scatter plots, correlation and regression.
This course is taught by an experienced statistician - advice can be sought during the day for specific research/statistics queries.

Tue May 21st 2019: Exploring Relationships & Regression Analyses - one day course 9:30-16:30

One day course looking at exploring relationships between continuous/scalar variables including correlation, simple linear regression, multivariate regression with categorical variables included, more complex models & logistic regression. Other predictive models will be demonstrated. We will be using spss. This course is taught by an experienced statistician - advice can be sought during the day for specific research/statistics queries.

 

The current full set of NVIVO courses are given below – please note participants will enjoy full post workshop support by telephone, e-mail and on-line one-to-one direct desktop support and consultancy for the life of their current project after taking these courses. These courses are all taught by a qualified trainer from QDATRAINING and for full benefit they should be taken in their entirety.

Wed May 22nd 2019: Introductory NVIVO - one day course 9:30-16:30

The objective of this course is to equip you with enough knowledge to enable you to begin your project using a computer-aided methodology. By the end of the course you will understand the advantages and limitations of using a computer for qualitative data analyses and will know how to set-up a database, how to import data and how to code data. You will also understand the potential of NVIVO as a tool for organising, questioning and reporting on the data so that you can truly support and defend your findings.
The depth and breadth to which these topics will be covered will depend on the general level of computer literacy of the group coupled with their experience, if any, of using databases designed for working with qualitative or unstructured data. Participants are encouraged to bring their own data if they have some. Otherwise, tutorial data will be provided on the day.

Thu May 23rd 2019 : Analysing data with NVIVO - one day course 9:30-16:30

*YOU SHOULD ONLY ENROL ON THIS ONE DAY COURSE IF YOU HAVE ALREADY COMPLETED AN INTRODUCTORY NVIVO DAY COURSE - Preferably at a previous course session. It is advisable for you to have completed day one OR to be an experienced NVIVO user in order to be able to participate in day two. The objective of this workshop is to conduct a piece of analysis using raw data and a research question. By the end of the session, the participants will have:
1. Set up a database with a robust architecture
2. Analysed the data using queries and manual analysis where appropriate
3. Reported on the findings