The School of Psychology at Ulster University will be hosting a Research Methods and Statistics Summer School for the Behavioural and Social Sciences during 31 August - 15 September 2023 on the Coleraine campus.
The booking system for 2023 courses is now open below.
Watch the Video
Mark Shevlin talks about the Research Methods and Statistics Summer School at Ulster University.
Structure and Content
The Summer School allows attendees to select a short course best suited to their current analytic requirements, while at the same time offering the opportunity to expand and build their expertise by taking a series of linked short courses.
For example, a participant can learn about regression and factor analysis models, prior to taking the more advanced short-course on latent variable modelling.
For the extremely keen novice researcher, it is possible to take all of the short-courses to cumulatively build their research and statistics skills over the Summer School period.
To get the most out of the Summer School, participants are encouraged to consider the content of each short course closely and to decide if they have the requisite background knowledge.
To help inform your short course selection, instructors have provided a description of the content that will be covered and a list of desired prerequisites.
Each short course will also provide an opportunity for attendees to discuss their own data and be offered advice on appropriate forms of analysis.
Attendees will receive a certificate of attendance.
Information on Short Courses available for 2023
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Introduction to R and Open Science Practices
Book Introduction to R and Open Science Practices course
Dates
4 -5 September 2023
Course Instructors
Dr Eoin McElroy and Professor Victoria Simms
Synopsis of the course
This 2-day course will introduce participants to important open science practices in the social sciences including the pre-registration of study hypotheses, experimental design and plans for data analysis, as well as processes and procedures for sharing research data and analytic code. The course will also provide applied researchers with an entry-level, practical introduction to R for the purposes of conducting reproducible data manipulation and analysis. It will introduce attendees to the basic functions of R, assuming no prior knowledge of computer programming. Particular attention will be paid to data exploration and visualisation techniques. Attendees will also gain experience of conducting a range of common statistical techniques used in the behavioural and social sciences (e.g. correlational and regression analysis). Participants will also be shown how to install R packages for additional functionality. This course uses lectures to provide a clear understanding of the logic underlying the use of statistical techniques and procedures; however, a greater amount of time will be devoted to giving participants experience of hands-on use of R.
Entry requirements
Basic knowledge of descriptive and inferential statistics (specifically correlation and regression) would be beneficial for the R-portion of the course.
Location (campus)
Coleraine
Start Date, Time and Duration
Monday 4 September and Tuesday 5 September 2023 (2-day course) 9.30 – 4.30
Cost (Full Rate/Discount)
Short course rates:
- Student: £220
- Educational/charitable sector: £330
- Government/commercial sector: £400
A 10% discount will be given where the booking is for more than one short course.
**A limited number of places are reserved for PhD Researchers at Ulster free of charge. To be considered for a free place on the 2023 Summer School send the following details to statisticssummerschool@ulster.ac.uk by Friday 26 May 2023, 5pm: Your Name, Your Email, Your First Supervisor’s name, Your B number, Your First Preference Course, Your Second Preference Course. Note. You will be notified if you have been allocated a slot by Friday 16 June 2023.
Summer School Closing Date
Closing date for all other applications is Friday 7 July 2023, 5pm.Summer School Cancellation Policy
Cancellations made prior to Friday 7 July 2023: full refund less administration fee of £50.
Cancellations made after Friday 7 July 2023: no refund.General course contact
Jennie Reid, Statistics Summer School Administrator
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Descriptive and Inferential Statistics in SPSS
Book Descriptive and Inferential Statistics in SPSS course
Dates
31 August - 1 September 2023
Course Instructor
Dr James Houston
Synopsis of the course
This 2-day short course is designed for individuals who have little or no experience using statistical software such as SPSS or who may need a refresher course on methods of dealing with quantitative data.
The course is ideally suited to participants who have, or whose organisations may have, quantitative data but are unsure of how to analyse it or are unsure of how to get the most information out of their data.
The rationale underlying this short-course is to promote evidence-based decision making through exploiting data.
Participants will be made aware of how they can answer research questions using various types of data and various types of analyses.
The short course begins with an introduction to the SPSS interface, detailing the many features available in this statistical software. We will also show participants how to get data into SPSS from various sources, including databases such as excel.
The short course will introduce and develop knowledge of statistical analysis, with specific reference to
- hypothesis testing
- statistical concepts and techniques
- selecting an appropriate statistical technique
- the application of statistical software to data analysis
- and the production and interpretation of statistical and graphical output
This course will use lectures to provide a clear understanding of the logic underlying the use of statistical techniques and procedures.
However, a greater amount of time will be devoted to giving participants experience of hands-on use of SPSS.
At the end of each day participants will be given the opportunity to discuss any data they might have, particularly in terms of selecting and applying an appropriate form of analysis or questions they might have about conducting research in general.
Entry requirements
No prior knowledge of SPSS or statistical analysis is required.
Location (campus)
Coleraine
Start Date, Time and Duration
Thursday 31 – Friday 1 September 2023 (2-day course) 9.30 – 4.30
Costs (Full Rate/Discount)
Short course rates:
- Student: £220
- Educational/charitable sector: £330
- Government/commercial sector: £400
A 10% discount will be given where the booking is for more than one short course.
**A limited number of places are reserved for PhD Researchers at Ulster free of charge. To be considered for a free place on the 2023 Summer School send the following details to statisticssummerschool@ulster.ac.uk by Friday 26 May 2023, 5pm: Your Name, Your Email, Your First Supervisor’s name, Your B number, Your First Preference Course, Your Second Preference Course. Note. You will be notified if you have been allocated a slot by Friday 16 June 2023.
Summer School Closing Date
Closing date for all other applications is Friday 7 July 2023, 5pm.Summer School Cancellation Policy
Cancellations made prior to Friday 7 July 2023: full refund less administration fee of £50.
Cancellations made after Friday 7 July 2023: no refund.General course contact
Jennie Reid, Statistics Summer School Administrator
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General linear model with applications to ANOVA, Regression analysis, and factor analysis
Book General Linear Model course
Dates
6 - 8 September 2023
Course Instructors
Dr John Mallett, Professor Gary Adamson and Professor Mark Shevlin
Synopsis of the course
This 3-day course provides participants with a firm working knowledge of a wide range of statistical models used in the behavioural and social sciences.
These models also serve as the fundamental building blocks for advanced statistical models and will be particularly useful for those participants wishing to take more advanced short-courses e.g. the Latent Variable Modelling course.
The course begins by exploring the general linear model and its application in ANOVA, ANCOVA, MANOVA and MANCOVA with repeated measures models.
The course will describe simple bivariate regression and correlation and build gradually to the multivariate case, which incorporates several predictor variables. In addition to examining regression models with a continuous outcome variable, time will be devoted to data situations in which the outcome variable is either dichotomous or polytomous, i.e. binary and multinomial logistic regression models.
Moreover, exploratory factor analysis (EFA) will be covered in some depth, with the focus on its usefulness as a data reduction method: the EFA model primarily involve reducing a large number of observed variables to a lesser number of latent factors, the purpose of which is to explain the structural relationship between the observed variables parsimoniously.
The course will conclude with an introduction to the Confirmatory Factor Analysis models and its applications using advanced statistical software.
The assumptions underpinning the use of all techniques will be considered throughout the short course, together with identifying some strategies for assessing potential violations.
Each element of the course will begin with a lecture to provide participants with a sound conceptual understanding of each statistical model and its application.
However, greater emphasis will be placed on practical activity, with participants gaining experience using a hands-on approach to reinforce the learning concepts and to ensure that participants are able to perform the desired analysis and appropriately interpret the output.
Days 1 and 2 will be taught primarily using SPSS software with Day 3 using both SPSS and Mplus.
Entry requirements
No prior knowledge is assumed, but some experience of descriptive statistics and hypothesis testing would be helpful.
Location (campus)
Coleraine
Start Date, Time and Duration
Wednesday 6 September – Friday 8 September 2023 (3-day course) 9.30 – 4.30
Costs (Full Rate/Discount)
Short course rates:
- Student: £330
- Educational/charitable sector: £495
- Government/commercial sector: £600
A 10% discount will be given where the booking is for more than one short course.
**A limited number of places are reserved for PhD Researchers at Ulster free of charge. To be considered for a free place on the 2023 Summer School send the following details to statisticssummerschool@ulster.ac.uk by Friday 26 May 2023, 5pm: Your Name, Your Email, Your First Supervisor’s name, Your B number, Your First Preference Course, Your Second Preference Course. Note. You will be notified if you have been allocated a slot by Friday 16 June 2023.
Summer School Closing Date
Closing date for all other applications is Friday 7 July 2023, 5pm.Summer School Cancellation Policy
Cancellations made prior to Friday 7 July 2023: full refund less administration fee of £50.
Cancellations made after Friday 7 July 2023: no refund.General course contact
Jennie Reid, Statistics Summer School Administrator
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An Introduction to latent variable modelling
Book Latent Variable Modelling course
Dates
11 - 12 September 2023
Course Instructor
Professor Mark Shevlin
Synopsis of the course
This course provides students with an introduction to latent variable modelling – an ever increasingly used approach in the behavioural and social sciences.
The course covers many of the major features of latent variable models including confirmatory factor analysis, path analysis (with and without error) and modelling the relationships between latent variables.
The historical and statistical foundations of latent variable models will be detailed, with particular attention paid to the issues of measurement, specification, estimation and interpretation of models.
The course will demonstrate how latent variable models offer an extremely flexible framework for statistical analysis and one that allows complex hypotheses to be tested.
Some extensions to the basic latent variable model will be introduced, such as multiple group analysis, MIMIC models, and the application of model constraints.
The course will be delivered by means of lectures and hands-on practical work. The final session of each day will include a question and answer session with the opportunity for everyone to discuss their research interests and their own data structures.
Entry requirements
Mplus will be used, but no experience of this software is required.
It is expected that participants will have some knowledge of different variable types (nominal, ordinal, etc.), descriptive statistics and a working knowledge of hypothesis testing prior to taking the course.
An understanding of regression and correlation would be a benefit.
The following websites provide accessible overviews of latent variable models.
This is the homepage for the Mplus software. It has links to examples, data, and tutorials.
Location (campus)
Coleraine
Start Date, Time and Duration
Monday 11 September – Tuesday 12 September 2023 (2-day course) 9.30 – 4.30
Costs (Full Rate/Discount)
Short course rates:
- Student: £220
- Educational/charitable sector: £330
- Government/commercial sector: £400
A 10% discount will be given where the booking is for more than one short course
**A limited number of places are reserved for PhD Researchers at Ulster free of charge. To be considered for a free place on the 2023 Summer School send the following details to statisticssummerschool@ulster.ac.uk by Friday 26 May 2023, 5pm: Your Name, Your Email, Your First Supervisor’s name, Your B number, Your First Preference Course, Your Second Preference Course. Note. You will be notified if you have been allocated a slot by Friday 16 June 2023.
Summer School Closing Date
Closing date for all other applications is Friday 7 July 2023, 5pm.Summer School Cancellation Policy
Cancellations made prior to Friday 7 July 2023: full refund less administration fee of £50.
Cancellations made after Friday 7 July 2023: no refund.General course contact
Jennie Reid, Statistics Summer School Administrator
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An introduction to latent class analysis
Book Latent Class Analysis course
Date
13 September 2023
Course Instructor
Professor Gary Adamson
Synopsis of the course
Many important concepts in the disciplines of psychology and other social sciences, for example personality, quality of life, or prejudice, cannot be directly observed (i.e. they are hidden or latent constructs).
Researchers often attempt to measure these concepts using standardised questionnaires, which are assumed to be imperfect indicators of the latent construct of interest.
These observed indicators are assumed to be caused by the latent variable; therefore, covariation among these observed measures is expected.
The patterns of interrelationships among observed measures can be explored and analysed using latent variable modelling.
A number of latent variable models are used widely in the behavioural and social sciences – the most common of which is the factor analytic (FA) model.
The main difference between the traditional FA model and the latent class (LC) model lies in the nature and distribution of the latent variable.
For the FA model, the latent variable is continuous and normally distributed, whereas the LC model assumes as categorical latent variable with a multinomial distribution.
Use of the LC model has mushroomed in recent years largely due to the increased tendency to collect data at either the nominal or ordinal level of measurement.
This workshop will involve a mixture of interactive lecture-type sessions and practical examples using real-life epidemiological datasets in Mplus.
Entry requirements
No prior knowledge of latent class analysis or Mplus is expected; however, attendees should have a basic understanding of the concept of using latent variables within psychological and social science research.
Location (campus)
Coleraine
Start Date, Time and Duration
Wednesday 13 September 2023 (1-day course) 9.30 – 4.30
Costs (Full Rate/Discount)
Short course rates:
- Student: £110
- Educational/charitable sector: £165
- Government/commercial sector: £200
A 10% discount will be given where the booking is for more than one short course.
**A limited number of places are reserved for PhD Researchers at Ulster free of charge. To be considered for a free place on the 2023 Summer School send the following details to statisticssummerschool@ulster.ac.uk by Friday 26 May 2023, 5pm: Your Name, Your Email, Your First Supervisor’s name, Your B number, Your First Preference Course, Your Second Preference Course. Note. You will be notified if you have been allocated a slot by Friday 16 June 2023.
Summer School Closing Date
Closing date for all other applications is Friday 7 July 2023, 5pm.Summer School Cancellation Policy
Cancellations made prior to Friday 7 July 2023: full refund less administration fee of £50.
Cancellations made after Friday 7 July 2023: no refund.General course contact
Jennie Reid, Statistics Summer School Administrator
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Analysing longitudinal data using latent variable models
Book Analysing Longitudinal Data course
Date
14-15 September 2023
Course Instructors
Professor Gary Adamson and Professor Mark Shevlin
Synopsis of Course
The analysis of change is central to much psychological and social research. Latent Growth Models (LGM) are an important class of models for the assessment of change. In essence these describe individuals’ behaviour in terms of an initial starting point (intercept) and their subsequent developmental trajectories (slope). The technique also allows for the introduction of predictors (covariates) of change. These predictors can be both time-invariant and time-varying and the model can be extended to incorporate other advantages of latent variable framework, e.g., the ability to handle missing data, to introduce both direct and indirect effects and correction for measurement error.
In the context of longitudinal data, latent variable modelling facilitates robust estimation of direct and indirect effects, together with controlling for, and assessing the impact of, moderating and mediating variables. This session will introduce some of the recent developments in the area. Furthermore, applications of the Cross-lagged panel model will be explored and extended to include mixture distributions.
Growth mixture models (GMMs) will be introduced. These models enable the researcher to explore longitudinal data for the presence of unobserved or latent subgroups. In GMMs the assumption of a single homogenous population with a single growth trajectory is relaxed. Instead, a latent categorical variable is introduced with the intention of capturing latent subpopulations in the longitudinal data. These subpopulations are not directly observed, but are inferred from the patterns of responses in the data. In sum, the GMM facilitates the exploration of longitudinal data for unobserved subgroups and estimates latent growth parameters for each of the subgroups.
Entry requirements
This workshop will use the Mplus programme. It is expected that participants will have some knowledge and understanding of Structural Equation Modelling.
Location (campus)
Coleraine
Costs (Full Rate/Discount)
Short course rates:
- Student: £220
- Educational/charitable sector: £330
- Government/commercial sector: £400
A 10% discount will be given where the booking is for more than one short course.
**A limited number of places are reserved for PhD Researchers at Ulster free of charge. To be considered for a free place on the 2023 Summer School send the following details to statisticssummerschool@ulster.ac.uk by Friday 26 May 2023, 5pm: Your Name, Your Email, Your First Supervisor’s name, Your B number, Your First Preference Course, Your Second Preference Course. Note. You will be notified if you have been allocated a slot by Friday 16 June 2023.
Summer School Closing Date
Closing date for all other applications is Friday 7 July 2023, 5pm.Summer School Cancellation Policy
Cancellations made prior to Friday 7 July 2023: full refund less administration fee of £50.
Cancellations made after Friday 7 July 2023: no refund.General course contact
Jennie Reid, Statistics Summer School Administrator
Course Instructors
If you require any additional information, please feel free to contact statisticssummerschool@ulster.ac.uk.