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Team Research and Development

Team Research and Development – Research Question, Hypothesis and Statistical Data Analysis

This group assessment for 7COM1079 – Team Research and Development requires students to collaboratively select a dataset and conduct a structured research investigation using data analysis. The assessment is worth 40% of the module mark and requires group members to work together to develop and submit a final research report. All group members are expected to contribute equally to the work, with one designated group member submitting the final assessment on behalf of the group. The main objective of the assignment is to develop a meaningful research question and corresponding hypothesis based on a selected dataset. The research question defines a specific claim or issue that the group intends to investigate and provides the starting point for constructing and testing an evidence-based argument. The hypothesis provides a proposed explanation or prediction that can be examined through analysis of the available data. Together, the research question and hypothesis establish what evidence is required, how that evidence will be evaluated and how effectively the findings can support or challenge a particular position. The assignment permits three types of research questions. The first involves establishing a difference in means between two groups. The second involves establishing a difference in proportion between two groups. The third involves establishing a correlation between two measures. The selected research question should therefore be appropriate for the characteristics of the chosen dataset and should allow the group to perform meaningful statistical analysis. Following the selection of the dataset and formulation of the research question and hypothesis, students are expected to produce appropriate data visualisations and statistical analysis. The analysis should provide evidence relevant to the research question and allow the proposed hypothesis to be examined systematically. The resulting findings should be interpreted in relation to the original research question and hypothesis rather than simply presenting numerical results. The assignment provides a Microsoft Word final report template containing the required table of contents, chapter and subchapter names and explanations of the expected content. Students are instructed to download and use this template when preparing their final report. Work produced by individual students during their first assignment may be incorporated where the student examined the same dataset allocated to the group, provided the material is appropriately incorporated into the group submission. All italicised instructional text in the template must be removed before submission. The completed amended template and the group's dataset file must be submitted through Canvas. Acceptable submission formats include PDF, DOC, DOCX, CSV and XLS. The assignment has a late-submission penalty, and the submission deadline is stated as being available in the assignment specification on Canvas. Assessment is based on the criteria provided in the module rubric. The rubric is used to assess the group's work, with group members initially receiving the same mark, although peer review may be taken into account. The assignment therefore combines collaborative research, statistical reasoning, data visualisation, analytical interpretation and academic report writing. The assignment instructions explicitly state that students should not use AI for this assessment. The module team also reserves the right to arrange a viva if academic misconduct is suspected. The final report should therefore represent the group's own research, analysis, interpretation and contribution.

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Digital Marketing and Analytics 2,974 words

Digital Marketing Analytics Consultancy Report — Accessibility, Social Listening and Web Analytics for an E-Commerce Storefron

This assessment places the student in a consultancy role advising a global brand's e-commerce storefront on its digital marketing strategy. It is written as a business report rather than an academic essay, in the third person, with appendices used only for supporting material that the main text explicitly refers the reader to. The work is built from four analytical layers, and the mark weighting tells students where the effort belongs. The first is an accessibility and user experience evaluation of the client site benchmarked against two self-selected industry competitors — the selection itself must be justified, and the analysis must cover both technical and content dimensions using recognised evaluation tools rather than impressionistic browsing. The second layer, carrying the smallest weight, is social listening: which networks the brand and its competitors are active on, what engagement looks like, which content types perform, and who the influential voices are. This is paired with qualitative sentiment or content analysis of actual social comments, on the premise that quantitative engagement metrics describe reach but not attitude. The third and heaviest layer is quantitative analysis using a web analytics platform, typically via a demonstration account that provides real traffic data. Students query the data themselves and extract performance insights. Comparison across time periods is what separates competent work from strong work here: a single-period snapshot describes, while period-over-period comparison explains. The fourth layer, weighted equally with the analytics, asks the student to synthesise everything into strategic recommendations for the coming year, covering areas such as customer segments, user behaviour, landing and exit page performance, search ranking positions, advertising budget allocation, marketing channels and e-commerce performance. This section is where most marks are lost. Recommendations that do not trace back to a specific finding from the preceding analysis read as generic digital marketing advice, and rubrics at this level penalise exactly that. Presentation requirements are prescriptive — specified font, size, line spacing and justified margins — and the report is expected to be concise despite the breadth of analysis, which makes ruthless selection of evidence part of the task. A draft submission point for similarity checking is usually provided separately from the marked final submission. Assessments of this type commonly require a signed declaration itemising any AI tool use, with an explicit confirmation that AI was not used to generate sentences, paragraphs or sections. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how accessibility evaluation tools are used and what their output actually shows, clarifying the difference between reporting analytics figures and interpreting them, showing how a recommendation should be traced to a specific finding, advising on report structure and appendix discipline, checking APA consistency, and reviewing a student's own draft against the published rubric.

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