Research Methods and Statistics Summer School

The School of Psychology at Ulster University will be hosting a Research Methods and Statistics Summer School for the Behavioural and Social Sciences during 25 August - 8 September 2021.

Due to the COVID-19 pandemic, the 2021 Statistics Summer School will be offered online only via Ulster University's Blackboard E-Learning platform.

Course information and booking links for 2021 are available below.

Please email with any queries for further information.

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.

Booking facilitates are now open!

Information on courses to be offered online during 2021 can be found below.

Information on Short Courses available for 2021

  • Introduction to Systematic Review and Meta-Analysis

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    25th August 2021

    Course Instructor

    Dr Peter Taylor

    Synopsis of the course

    This 1-day course is designed to provide a general introduction to the principles and practice of conducting a systematic review and meta-analysis.

    The morning will focus on systematic reviewing. Systematic reviews will first be defined and contrasted with other review methodologies (e.g. narrative reviews, meta-synthesis).

    Next, the process of conducting a systematic review, with an emphasis on best practice, will be outlined.

    Steps including generating a research question, literature searching and screening, and risk of bias assessment, will be covered.

    The afternoon will then focus on the use of meta-analysis to statistical aggregate effect sizes as part of a review.

    The principles of meta-analysis, including different models (random vs. fixed-effects; heterogeneity) will be covered. Attendees will gain experience of implementing meta-analysis in R, primarily using the “meta” package.

    Participants will be shown how to evaluate heterogeneity and test putative moderators of effect size.

    The concept of publication bias and how it can be evaluated will also be introduced.

    Whilst the information covered will be applicable to a wide range of fields within the social and medical sciences, examples will focus on the area of clinical psychology and mental health.

    More advanced topics in this area, including network meta-analysis and individual patient data meta-analysis, will be briefly mentioned but not covered in any detail.

    Entry requirements

    No prior knowledge or experience of conducting systematic reviews will be required. For the section on meta-analysis, some basic familiarity with using R and R studio will be beneficial (e.g. navigating R studio, setting up and editing R script files).

    Various online tutorials to R exist, for example see Introduction to R Studio and R Markdown.

  • Introduction to R with application to network analysis

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    26th - 27th August 2021

    Course Instructor

    Dr Eoin McElroy

    Synopsis of the course

    This 2-day short course is designed to provide participants with an entry-level, practical introduction to R for the purposes of conducting data analysis and statistics.

    The first day of the course will introduce attendees to the basic functions of R, assuming no prior programming knowledge or experience.

    Particular attention will be paid to introducing participants to a wide range of 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. descriptive and inferential statistics, correlational analysis).

    Comparisons between the functionality of R and other standard data analysis software packages (e.g. SPSS) will be made.

    Participants will be shown how to install R packages for additional functionality.

    Day 2 advances on the first day by focusing on using R to conduct network analysis, a statistical procedure that is used to visualise and study complex relationships between variables.

    Participants will be introduced to the fundamental concepts in network analysis and will be taught how to use specific R packages (e.g. qgraph) to explore and analyse network data, as well as learning how to visualize networks.

    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

    No prior knowledge of programming or statistical analysis is required.

  • Descriptive and Inferential Statistics in SPSS

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    30th - 31st August 2021

    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.

  • General linear model with applications to ANOVA, Regression analysis, and factor analysis

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    1st - 3rd September 2021

    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.

  • An Introduction to latent variable modelling

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    6th-7th September 2021

    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.

  • An introduction to latent class analysis

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    8th September 2021

    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.

Course Instructors

If you require any additional information, please feel free to contact

Dr James Houston

Associate Head of School of Psychology

School of Psychology