This module will introduce students to research and survey design, and hypothesis testing, drawing upon different scientific approaches. The principles of experimental design and how these can be applied to field projects will be explained, together with the nature of both quantitative and qualitative data. An introduction to sampling strategies and the role of probability in inferential statistics will lead into the role of descriptive statistics and measures of variability in data exploration. This will be complemented by consideration of the application of both parametric and nonparametric statistics in data analysis (t-tests, ANOVA, regression, correlation, their nonparametric equivalents), including multivariate tests. The rules underlying the appropriate presentation of statistical data in research reports will be discussed. Upon completion students will understand the principle quantitative analytical approaches to research, and the best ways of presenting results.
Private Study: 175
Contact Hours: 25
Total Hours: 200
Compulsory for:
MSc Forensic Osteology and Field Recovery Methods
MSc Conservation Science and cognate pathways
Optional for:
MSc Ethnobotany
*Critical Reading (50%): the students are tasked to write a critical response to one of two scientific articles issued by the module convenor in the form of a 'Letter to the Editor'. This critical piece should not exceed 800 words (excluding references)
*Statistical Analysis (50%). This assignment consists of statistical exercises with a series of tasks with clear instructions based on given datasets.
Reassessment methods:
Like for like.
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
Demonstrate a sound knowledge of the principles of research and survey design.
Construct a research project with testable hypotheses.
Demonstrate a comprehension of the difference between quantitative and qualitative data.
Critically evaluate methodologies and results.
Be able to align inferential statistical tests with different types of variables and different research designs.
Analyse quantitative data with univariate and multivariate inferential statistical tests using appropriate software (SPSS or R).
Present results suitable for a scientific report.
Critically evaluate and problem solve.
Independently learn and time manage.
Demonstrate enhanced computer literacy using statistical software.
Demonstrate critical skills and communication by appropriate methods.
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