The School of Psychology at Ulster University will be hosting a Research Methods and Statistics Summer School for the Behavioural and Social Sciences during 30 August - 12 September 2024 in person on the Coleraine campus.
The booking system for 2024 courses is now closed. Successful applicants will be notified by 31 July 2024.
Summer School overview
Professor 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.
Course information
-
Short Course 1 Open Research: a practical guide
Instructor
Professor Victoria Simms
Date
Friday 30 August 2024 (09.30 – 16.30)
Synopsis of the course
This one 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 via open access repositories.
Location (campus)
Coleraine
Any entry requirements
No prior knowledge is required.
General course contact
Donna Taggart, Statistics Summer School Administrator
-
Short Course 2 Descriptive and Inferential Statistics in SPSS
Instructor
Dr James Houston
Dates
Monday and Tuesday 2-3 September 2024 (09.30 – 16.30 daily)
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.
Location (campus)
Coleraine
Any entry requirements
No prior knowledge of SPSS or statistical analysis is required.
General course contact
Donna Taggart, Statistics Summer School Administrator
-
Short Course 3 General Linear Model with applications to ANOVA, Regression Analysis and Factor Analysis
Instructors
Dr John Mallett, Professor Gary Adamson, and Professor Mark Shevlin
Dates: Wednesday to Friday 4-6 September 2024 (09.30 – 16.30 daily)
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 in this Summer School.
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.
Location (campus)
Coleraine
Any entry requirements
No prior knowledge is assumed, but some experience of descriptive statistics and hypothesis testing would be helpful.
General course contact
Donna Taggart, Statistics Summer School Administrator
-
Short Course 4 An Introduction to R for social and life science research
Instructor
Dr Eoin McElroy
Date: Monday
9 September 2024 (09.30 – 16.30)
Synopsis of the course
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.
Location (campus)
Coleraine
Any entry requirements
Basic knowledge of descriptive and inferential statistics (specifically correlation and regression) would be beneficial for the R-portion of the course.
General course contact
Donna Taggart, Statistics Summer School Administrator
-
Short Course 5 An Introduction to Latent Variable Modelling
Instructors
Professor Mark Shevlin and Professor Gary Adamson
Dates
Tuesday-Thursday 10-12 September 2024 (09.30 – 16.30 daily)
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. The patterns of interrelationships among observed measures can be explored and analysed using latent variable modelling.
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 model to assess differential item functioning and the application of model constraints. The use of the latent variable approach to assess change over time will also be introduced, together with assessing how time invariant and time varying covariates may contribute to explaining change over time. In addition, latent variable models designed to “uncover” homogeneous subgroups within multivariate categorical and/or continuous data will be introduced, such as Latent class/profile models.
In sum, this 3-day course provides an introduction to the specification, estimation and interpretation of a series of latent variable models using the Mplus software, including: Confirmatory factor analysis, Path analysis, Structural Equation models, Multiple group models, MIMIC models, Latent Growth models, Latent Class and Latent Profile models. Furthermore, some consideration will be given to how aspects of these models can be combined to address interesting and complex research questions.
Location (campus): Coleraine
Any 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.
It has links to examples, data, and tutorials.
General course contact
Donna Taggart, Statistics Summer School Administrator
Course Instructors
If you require any additional information, please feel free to contact statisticssummerschool@ulster.ac.uk.