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Module JXH-4003:
Advance Research Skills

Module Facts

Run by School of Sport, Health and Exercise Sciences

20 Credits or 10 ECTS Credits

Semester 1

Organiser: Mr Robin Owen

Overall aims and purpose

This is a fabulous hands-on opportunity to learn about research design and data analysis, skills that are critical to reading and writing research material, during your degree and beyond. It 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!

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

To prepare students to complete their Masters independent study or proposal modules, and their dissertation module.

Module leader Jamie Macdonald provides an overview of Research Skills

Course content

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 material relevant to the analysis of group differences and regression analyses using quantitative methods. 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.

Module failure that prevents you passing the year will require resit assessment and attendance at Supplementary Assessment Week (exact date TBC but expected to be second week of July 2018).

Assessment Criteria

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: Students will demonstrate an in-depth understanding of each of the analyses. These analyses will typically include at least one of the more complex analyses e.g., MANOVA, ANCOVA, MANCOVA) with the more straightforward analyses (e.g., one-way repeated measures 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

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.

Part B: Students will demonstrate an acceptable understanding of the analyses although this understanding may be marginal at times. The analyses that are covered will typically include the more basic analyses (e.g., single-factor randomized and repeated measures ANOVA, two-factor fully randomized ANOVA, basic hierarchical regression) with perhaps an attempt at explaining one of the more complex analyses (e.g., fixed-model ANOVA). The explanation of the more straightforward analyses will be good to fair while any coverage of more complex analyses will be largely descriptive. The student will demonstrate a largely acceptable working knowledge of the main analyses albeit with some (possibly quite large) 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.

good

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.

Part B: Students will demonstrate a solid understanding of most of the analyses. These analyses will typically include one of the more complex analyses (e.g., MANOVA, ANCOVA, MANCOVA, mixed-model ANOVA) and the more straightforward analyses (e.g., two-factor fully randomized ANOVA) will be covered in a largely comprehensive manner. The student’s explanations will be fairly concise and precise, demonstrating a reasonably good working knowledge of the analyses and most of their intricacies (e.g., assumptions, follow-up analysis considerations, decisions with regard to unequal group sizes, etc.) with some errors, inconsistencies, or a degree of superficiality. The student will be somewhat reliant on the portfolio and will answer questions in a largely precise but sometimes rather superficial manner. The verbal communication style will be fairly clear with some redundancy.

Learning outcomes

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

  2. 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

  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 B of this module, students will:

    Be able to explain the statistical procedures and the assumptions that underpin the statistical procedures associated with experimental and quasi-experimental designs as well as explain the options available to deal with violations of these assumptions.

  5. On successful completion of Part B of this module, students will:

    Be able to use SPSS for Windows and understand, and be able to explain, the statistical outputs produced by SPSS.

Assessment Methods

Type Name Description Weight
INDIVIDUAL PRESENTATION Part A (Design) presentation

You should select one of the papers below to critique. The papers are available on Blackboard > JXH4003/4020 > Assessment: Design (A): Presentation > Manuscripts to choose from to review. You may work individually and/or as a group to criticize and/or defend the research design of the paper. However, you must work individually to generate your work to submit. Generate one presentation of a maximum of 10 minutes. Your mark will be reduced by one categorical grade (e.g. from A to A-) for every full minute you exceed this time limit. The presentation may be of any format that is accessible by the lecturer on a standard Microsoft Windows-based Personal Computer. Upload the presentation to the University’s OneDrive (www.bangor.ac.uk/itservices/one-drive.php.en), ensuring you change the file sharing settings to “Anyone with this link can edit”, and use the “Copy Link” function to send the link to j.h.macdonald@bangor.ac.uk from your University email address. Marking guidelines are available on Blackboard, including a marking rubric. Note your verbal and visual presentation style will also be marked. Formative feedback on drafts of your presentation and technical advice on how to record it will be provided in the shadow and enhancement classes.

25
VIVA Part B (Statistics): viva

The viva voce The viva voce examination is an opportunity for students to demonstrate their understanding of the various statistical parts of this double module. The viva voce examination will last precisely 20 minutes. At the start of the viva, the student will be asked to choose and explain an analysis that he/she feels very comfortable with (e.g., one-way repeated measures ANOVA). The student will then be asked to choose at least two more analyses, one of which will be as complex as the student feels comfortable dealing with. The tutor will ask questions to clarify understanding where appropriate throughout. Students are strongly encouraged to use their annotated portfolio to help them in their explanations and to demonstrate their understanding of the analyses. However, the portfolio should be a support rather than a crutch. The specific assessment criteria for this examination are detailed below. Immediately after the viva, students will be asked to reflect on these assessment criteria and to provide a critical analysis of their performance in relation to these criteria. The tutor will then discuss the student’s assessment of his/her performance and provide additional feedback. The student will be provided with that feedback and the associated grade before leaving the viva examination.

50
CLASS TEST Part A (Design): sample size estimation (in-class worksheet)

To demonstrate your understanding of sample size estimation, answer nine questions on the worksheet that will be given at the start of the lecture. Each question will be worked on together by the class. The shadow class will offer an opportunity for any student to gain additional support to complete the worksheet. The worksheet will be handed in at the end of the shadow class.

0
Written assignment, including essay Part C (Qualitative): essay

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. o Ensure to critique the mixed method design. • When evaluating the strengths and limitations of the approach, consider the following: o The choice of methodology (e.g., ethnography, phenomenology, grounded theory) in relation to the research question/purpose of study; o The potential ethical issues and how the author(s) addressed / managed them; o How well the author(s) justified each step of their data collection ( data collection method) and analysis approach (analytical strategies); o The steps that were adopted to ensure transparency and improve trustworthiness; o The utility of the approach to glean information that would offer robust applied implications; o The appropriateness of the data representation to address the research question/purpose; o Did the authors noted their ontological & epistemological position (paradigm)? o Sampling

25

Teaching and Learning Strategy

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) = 33 hours; Examination revision and examination = 23 hours.

78
Seminar

Part A: 5 x 1 hr shadow classes.

5
Workshop

Part C: 3 x 4 hr workshops

12
Seminar

Part C: 3 x 1 hr shadow classes

3
Lecture

Part B: Computer classes

22

Transferable skills

  • 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
  • Exploring - Able to investigate, research and consider alternatives
  • 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

Subject specific skills

  • 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
  • plan, design, execute and communicate a sustained piece of independent intellectual work, which provides evidence of critical engagement with, and interpretation of, appropriate data
  • develop a sustained reasoned argument, perhaps challenging previously held assumptions
  • demonstrate effective written and/or oral communication and presentation skills
  • 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

Resources

Resource implications for students

Ability to download GPower software

Talis Reading list

http://readinglists.bangor.ac.uk/modules/jxh-4003.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.

Courses including this module