Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
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Date posted: September 2024 No Banner module data is available
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Description The purpose of this module is to provide students with an introduction to data analysis and interpretation. Students will learn about sources of data, methods of data presentation and description, and how to conduct simple hypothesis tests and make inferences. On completion of the module, students should be able to draw on statistics appropriately to support their own arguments and be able to better understand and critique statistical analysis they encounter in academic papers in subsequent courses. Students are expected to attend lectures and to actively participate in workshops and tutorials. Term exercises will be assigned in order to focus students' study and ensure timely review of course material. | |||||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Define and calculate basic statistics used to describe distributions. 2. Present data in a meaningful way, using graphs and tables. 3. Perform calculations and manipulate data using a spreadsheet package, including estimation of a single variable regression. 4. Explain what a hypothesis test is, conduct simple hypothesis tests and interpret statistical significance. 5. Comment on statistical analysis in academic papers and identify shortcomings. | |||||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Data description & presentationTopics will include: Types of data; Displaying data graphically; Descriptive statistics – definitions, uses, examples; Correlation; Applications using data relevant to programme disciplines; Sourcing data.Statistical EstimationStatistical decision theory; Hypothesis tests; Interpretation of test statistics (p values, F tests etc); Regression output interpretation – using firstly statistical estimation, then reviewing data analysis section (eg. to show importance of causation v correlation)Critiquing statistical analysisDrawing on papers from relevant disciplines, show the different ways in which statistics are used to inform and augment debate. Examine the statistics used and conclusions drawn. Highlight potential shortcomings (eg. external factors outside scope of project that may have impact, reverse causation.) | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||