Run by School of Human and Behavioural Sciences

20.000 Credits or 10.000 ECTS Credits

Semester 1

Organiser: Dr Jamie Macdonald

Do you want to understand why, for example, some athletes perform better than others under pressure, why obesity is on the rise, how training and recovery influences performance or something else related to sport and physical activity? If so this module is for you! Research is fundamental to what we, as scientists, do and informs practice in the sport, health and fitness industry. Therefore, developing good research skills and being able to analyse scientific data are key skills to develop in order to support your successful career. The module is hands on and involves practical activities relating to data management, running data analyses, understanding different approaches to science, and developing your own research projects. These are real world graduate skills that will help make you more employable. The module is delivered in a lively, professional, and very friendly manner by world-renowned researchers. They will help you develop the necessary skills to run your own programme of research, and to evaluate that of others. Despite possible preconceived ideas about research design and analysis, students end up loving this module!

Module leader Jamie Macdonald provides an overview of Research Skills

The aims: To provide critical understanding of research design and data analysis.

To prepare students to complete their Dissertation Proposal module and their Dissertation module.

Delivered by active researchers and practitioners within the sport and exercise domain, this module is split into three parts:

Part A of this module covers quantitative research design. Using an experiential and flipped teaching format, students will read a series of scientific papers to develop their critical understanding of research design, including formulating a question, generating a hypothesis, study designs, sampling methods, ensuring validity and reliability, and good dissemination practices. Both original investigations and review studies will be included.

Part B of this module covers how to analyse and understand data: computational statistics. Specific content includes: Single factor analysis of variance with and without repeated measures; Two factor analysis of variance with and without repeated measures; Single factor and two factor multivariate analysis of variance (with and without repeated measures); Doubly repeated measures analysis of variance; Analysis of covariance; Follow-up procedures for all of the above; Assumptions underpinning all of the above and available options for dealing with violations to these assumptions. Regression based analyses: simple and multiple linear regression; curvilinear regression; mediation and indirect effects; moderated hierarchical regression; moderated mediation.

Part C of this module covers qualitative research methods and analysis. The qualitative part of the module will address the different philosophical positions underpinning quantitative and qualitative research; qualitative research data collection methods, including interviews, focus groups and observational methods; and qualitative data analysis, including thematic content analysis, grounded theory and discourse analysis.

good

For Parts A&C: Demonstrates a good understanding of qualitative and quantitative research design. Is able to recognise research designs, validity and reliability threats. Few inaccuracies or misconceptions.

For Part B: In order to obtain a good level in this examination, students will demonstrate an acceptable understanding of the analyses. These analyses will typically include the more basic analyses (e.g., single-factor ANOVA) with perhaps an attempt at explaining one of the more complex analyses (e.g., two-factor ANOVA, multiple regression). The explanation of the more straightforward analyses will be fairly comprehensive while any coverage of more complex analyses will be largely descriptive. The student will demonstrate an acceptable working knowledge of the main analyses albeit with some gaps in the underlying intricacies (e.g., assumptions, follow-up analysis considerations, decisions with regard to unequal group sizes, etc.). The explanations will be somewhat confused at times with some errors and superficiality, most of which will be remedied with some prompting. The student will be rather reliant on the portfolio. The student will answer questions in a somewhat superficial manner at times and the verbal communication style will sometimes be somewhat unclear and unnecessarily verbose.

excellent

Parts A & C: A thorough understanding of qualitative and quantitative research design. Demonstrates a consistent ability to recognise research designs, validity and reliability threats. Be able to propose solutions to threats to research design.

Parts B: In order to obtain an excellent level in this examination, students will demonstrate an in-depth understanding of each of the analyses. These analyses will typically include at least one more complex analysis (e.g., two factor ANOVA) with the more straightforward analyses (e.g., one-way ANOVA) covered in a comprehensive manner. The student’s explanations will be concise and precise, demonstrating an integrated knowledge of the different analyses and their various intricacies (e.g., assumptions, follow-up analysis considerations, decisions with regard to unequal group sizes, etc.). The student will use the portfolio only to the extent that it supports his/her well-integrated understanding. The student will answer questions in a concise and accurate fashion. The verbal communication style will be clear and concise.

threshold

For Parts A & C: Basic understanding of qualitative and quantitative research design. Some ability to recognise research designs, validity and reliability threats. Inaccuracies and misconceptions evident.

For Part B: In order to obtain a threshold level in this examination, students will demonstrate a barely acceptable understanding of the analyses. The explanations will include only the most basic analyses (e.g., single-factor randomised ANOVA, simple regression). The explanations will either not include the more complex analyses or any attempt to do so will be very descriptive and confused. The student’s explanations of most analyses will be confused in places, demonstrating only a barely acceptable working knowledge of each of the analyses with some fairly serious gaps with regard to their intricacies (e.g., assumptions, follow-up analysis considerations, decisions with regard to unequal group sizes, etc.) combined with a number of errors and inconsistencies, reflecting an overall superficial understanding. The student will be heavily reliant on the portfolio and will answer questions in a largely superficial or confused manner. The verbal communication style will be somewhat unclear and unnecessarily verbose with the need for much prompting.

1. On successful completion of Part A of this module, students will: Be able to criticise and defend the experimental and quasi-experimental research designs that are often utilised in sport science studies

2. On successful completion of Part B of this module, students will: Demonstrate an understanding of the principles underlying variance testing (e.g., ANOVA) and regression (simple and multiple regression).

3. On successful completion of Part C of this module, students will: Be able to critically evaluate the qualitative designs and associated analytical procedures that are used in sport science.

4. On successful completion of Part A of this module, students will: Be able to demonstrate and apply the basic concepts of sample size estimation.

5. On successful completion of Part B of this module, students will: Organise data in an SPSS spreadsheet and interpret the main features of SPSS computer printout from ANOVA and regression analyses.

Type	Name	Description	Weight
CLASS TEST	Part A (Design): sample size estimation (in-class worksheet)	During this session we will work through an in-class worksheet together, using content from the session to answer questions on the worksheet. Passing this assessment is essential to ensure you have met the learning outcome: "Be able to demonstrate and apply the basic concepts of sample size estimation". Thus the assessment is marked as pass or fail. A follow up shadow class will support students who need additional time to complete the assessment.	0.00
INDIVIDUAL PRESENTATION	Part A (Design): presentation	You will select one of three offered papers and critique this paper in terms of its research design. This assessment will allow you to practice delivering a powerpoint presentation but in the (relative) safety of a non-public setting. You will record a narration over your powerpoint slides and submit using the University's OneDrive. Thus you will also learn a transferable skill useful for recording instructional podcasts and youtube videos. Your mark will be reduced by one categorical grade (e.g. from A to A-) for every full minute you exceed the 10 minute time limit	25.00
COURSEWORK	Part C (Qualitative) essay (Tommie's content)	With consideration of relevant literature, critique the rigor of the method the provided study has adopted, and offer potential recommendations for further research that are born from the strengths and limitations provided. Ensure to critique the mixed method design.	25.00
VIVA	Part B (Statistics) Viva	Throughout the module you will collate a portfolio of SPSS computer statistical outputs. Your understanding of this portfolio will form the basis of this viva. In this viva voce examination, you can bring with you whatever you want, apart from your best friend! You will be expected to answer questions set by the examiner on all sections of your portfolio; the examiner will increase the difficulty of the questions if you answer the initial questions correctly. Your marks will be based on the verbal answers you give; the portfolio itself will not be assessed. Students tell us that this is a challenging and very rewarding experience.	50.00

		Hours
Private study	Part A & C: Reading for each Part A flipped lecture (5 x 2 hours) = 10 hours; Reading for each Part C workshop (3 x 3 hours) = 9 hours; Preparing for each Part A flipped lecture (e.g., reading slides, papers, and completing Blackboard tasks, 5 x 2 hours/week) = 10 hours; Preparing and completing Part A assignment = 20.5 hours; Preparing and completing Part C assignment = 20.5 hours.	70
Lecture	Part A: 5 x 2 hr flipped lectures	10
Private study	Part B: Reading (2 hours/lecture) = 22 hours; Developing portfolio (e.g., annotating papers and outputs; 3 hours/week) = 28 hours; Examination revision and examination = 23 hours.	73
Practical classes and workshops	Part B: 4 x 1 hour practical sessions + 1 hr online preparation session.	5
Lecture	Part B: 11 x 2 hour lectures	22
Workshop	Part C: 3 x 4 hour workshops.	12
Seminar	Part A: 5 x 1 hr shadow classes	5
Seminar	Part C: 3 x 1 hr shadow classes	3

• Literacy - Proficiency in reading and writing through a variety of media
• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
• Information retrieval - Able to access different and multiple sources of information
• Inter-personal - Able to question, actively listen, examine given answers and interact sensitevely with others
• Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
• Teamwork - Able to constructively cooperate with others on a common task, and/or be part of a day-to-day working team
• Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting

• critically assess and evaluate data and evidence in the context of research methodologies and data sources
• describe, synthesise, interpret, analyse and evaluate information and data relevant to a professional or vocational context
• develop a sustained reasoned argument, perhaps challenging previously held assumptions
• demonstrate effective written and/or oral communication and presentation skills
• work effectively independently and with others
• take and demonstrate responsibility for their own learning and continuing personal and professional development
• recognise and respond to moral, ethical, sustainability and safety issues that directly pertain to the context of study including relevant legislation and professional codes of conduct
• demonstrate an understanding of the philosophical basis of scientific paradigms
• demonstrate evidence of competence in the scientific methods of enquiry, and interpretation and analysis of relevant data and statistical outputs.
• develop transferable skills of relevance to careers outside of sport, health and exercise sciences.
• communicate succinctly at a level appropriate to different audiences.
• accurately interpret case study data

Talis Reading list

http://readinglists.bangor.ac.uk/modules/jxh-4020.html

Reading list

The Talis reading list includes all the reading associated with this module. Example texts include: Part A: How to design and report experiments. Book by Andy P. Field; Graham Hole, 2003; Part B: Applied multivariate statistics for the social sciences. Book by Keenan A. Pituch; James Stevens, 2016; Part C: Qualitative research & evaluation methods: integrating theory and practice. Book by Michael Quinn Patton, 2015.

Compulsory in courses:

• B6AF: MSc Applied Sports Science year 1 (MSC/ASPS)
• B6AP: MSc Exercise Rehabilitation year 1 (MSC/EXREHAB)
• B6AL: MSc Applied Sport and Exercise Physiology year 1 (MSC/SPEXPHYS)
• B6AN: MSc Sport and Exercise Psychology year 1 (MSC/SPEXPSY)