Overview

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. You will develop the necessary skills to run your own programme of research, and to evaluate that of others. The module will be assessed with a viva, a narrated presentation, an in-class worksheet, and an essay. Despite possible preconceived ideas about research design and analysis, students end up loving this module!

Watch module leader Jamie Macdonald provide an overview of Advanced Research Skills: https://youtu.be/nZWLPbXq-LY

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 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.

Assessment Strategy

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.

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.

Module failure that prevents you passing the year will require resit assessment and attendance at Supplementary Assessment Week (exact date TBC).

Learning Outcomes

• 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

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

• 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.

• 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.

• 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.