This course will familiarise students with common methods used in biological anthropology. Specifically, it will deal with issues including the methods of data analysis, data presentation and hypothesis testing within the discipline of biological anthropology. This will be accomplished through students conducting both qualitative and quantitative analysis on an existing data set, and then interpreting the results of that analysis. The main aim of this course is to prepare students for the data analysis portion of their own thesis projects and future research in the discipline.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

Upon successful completion of this course, students will have the knowledge and skills to:

1. Choose an appropriate research design to answer their research questions;
2. Assess a data set and use appropriate techniques to clean it;
3. Use descriptive statistics to describe the data set;
4. Choose the correct statistical tests to perform significance testing on data sets; and
5. Interpret statistical tests and present results in a scientific format.

Research-Led Teaching

Students will conduct original analyses with actual biological anthropology data.

Staff Feedback

Students will be given feedback in the following forms in this course:

• Written feedback on the Project Statement.
• Written feedback on the Presentation.
• Written feedback on the Project Report.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

ChatGPT and Other Generative AI Tools in the Course:

Although the course convenor believes that there are some legitimate and productive uses for ChatGPT and other generative AI tools in academia, for the purpose of this class and its assessments, you may not use these tools. The rationale is that the course is meant to test your analytical and writing skills and abilities, and the use of ChatGPT and other generative AI tools would make this impossible. Therefore, you may not use ChatGPT or other generative AI tools to assist you with writing your assessments, conducting your assessment related analyses, or creating your assessment-related illustrations, including figures and tables. Failure to conform to this requirement will result in a 0 grade for the affected assessment.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Orientation; Identify dataset, topic, and methods-related references in consultation with your supervisor	Readings: Kass et al 2016 + Independent Readings
2	Identify dataset, topic, and methods-related references in consultation with your supervisor	Readings: Martin et al 2019 + Independent Readings
3	Identify dataset, topic, and methods-related references in consultation with your supervisor	Assessment: Project Statement dueReadings: Smith 2019 + Independent Readings
4	Work on analyses for project; continue meeting with supervisor; seek additional feedback	Readings: Parker et al 2016 + Independent Readings
5	Work on analyses for project; continue meeting with supervisor; seek additional feedback	Readings: Lakens 2022 + Independent Readings
6	Work on analyses for project; continue meeting with supervisor; seek additional feedback	Readings: Zuur et al 2010 + Independent Readings
7	Work on analyses for project; continue meeting with supervisor; seek additional feedback	Readings: Nakagawa & Freckleton 2010 + Independent Readings
8	Work on analyses for project; continue meeting with supervisor; seek additional feedback	Readings: Zuur & Ieno 2016 + Independent Readings
9	Write up data analysis project and prepare presentation	Readings: Rougier et al 2014 + Independent Readings
10	Write up data analysis project and prepare presentation	Readings: Doubleday & Connell 2017 + Independent Readings
11	Presentations; Write up data analysis project	Presentation dueReadings: Hartley 2008 + Independent Readings
12	Write up data analysis project and prepare presentation	Project report due

Tutorial Registration

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

Assessment Summary

Assessment task	Value	Due Date	Learning Outcomes
Project Statement	25 %	07/03/2024	5
Presentation	25 %	12/05/2024	1,4
Project Report	50 %	23/05/2024	1,2,3,4,5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Examination(s)

This course does not include any formal examinations. 

Assessment Task 1

Project Statement

During the first few weeks of class, you should be working to Identify a topic, database, and methods-related references for your data analysis project, which is the overarching project for this course. The Project Statement is the first assessment item related to the project. It is highly recommended that you work closely with your honours supervisor to identify a project and the data to be used for this project. You should aim for using good research practices (Parker et al 2016).

The data for your data analysis project can be: (a) the data, or a subset of the data, that you will use in your thesis; or (b) a dataset that you will procure from the many freely available ones on the internet and elsewhere. Please read and understand the current rules related to ‘recycling’ of material from the thesis, as this is important in deciding whether you do something that will be slotted into your eventual thesis or something entirely different.

The Project Statement is a statement of the topic, dataset, and initial references you have consulted. The statement of topic should include:

• a brief but detailed outline of the analyses you intend to do for the project and how they relate to your thesis;
• a statement about the dataset which should include the source and brief description of the data, which might include a table of descriptive statistics;
• the initial references should include full bibliographic information for 3 well-chosen references that you have identified as being critical for your project, plus a brief sentence for each explaining why you have chosen it.

See Appendix 4 of the ‘Course and Assessment Guide’ for referencing style. As explained in Appendix 5 below, you may not use ChatGPT or other generative AI tools on this assessment. 

The Project Statement is worth 25% of you grade in the course, and will be marked using the following criteria:

• Quality of the statement of topic
• Quality of the statement of data
• Quality of references
• Adherence to assessment instructions

Assessment Task 2

Presentation

You will give a 10-minute presentation about your data analysis project. You should:

• have a first slide with a title and your first and last name (first and last) to show while you provide a brief overview.
• use up to 4 additional slides to supplement the presentation of the details of the project. Slides that feature a well-chosen illustration, or well-crafted tables or figures are ideal.
• not overcrowd your slides.
• use a font size that is readable from a distance for all text parts. In Microsoft PowerPoint, 18-point font is the bare minimum but larger is preferable.
• cite your sources on the slide itself, a references slide, or a separate handout. You should cite at least 5 works related to analysis and methods using the style in Appendix 4 of the course handout. Give the audience a chance to inspect your references.
• practice your presentation ahead of time and speak clearly and confidently. 
• speak clearly and with present confidence.
• be ready to transition to conclusions, if you have not already, when you are told you have reached the 1-minute-left mark.
• be prepared to answer questions for around 2 minutes after your presentation.

Make sure to submit the final version of your slides before the presentation (by 11am of the due date).

The Presentation is worth 25% of you grade in the course, and will be marked using the following criteria:

• Quality of project.
• Quality of references.
• Quality of overheads.
• Adherence to assessment instructions.

Using ChatGPT or other generative AI tools will lead to a grade of 0.

Assessment Task 3

Project Report

The Project Report is a formal report of the analyses that you have done for the data analysis project for this class. You will have gotten feedback on the topic, data source, and references in an earlier assessment. The analyses you do for your project should be quantitative except under very limited circumstances with written permission from your supervisor provided to the course convenor early in the semester. The project provides a platform for you to practice the sorts of analyses you will do in the thesis, or even to start working on thesis analyses depending on current rules regarding the ‘recycling’ of material from the thesis. That is, if the current rules allow it, you are welcome to use this opportunity to do analyses that will be slotted eventually into the thesis.

The Project Report should be 2500-3000 words (excluding title, figures, figure captions, tables, and references).

The Project Report:

• should reflect an entire semester’s worth of independent work and should be characterized by the appropriate use of quantitative analytical methods of the standard that would be expected in the thesis itself. You should aim for using good research practices (Parker et al 2016). You should consult with your supervisor about this project and you will be provided feedback on your Project Statement in the first half of the semester. You will not be hounded or nudged. You are expected to put in the effort independently and seek input from your supervisor independently.
• does not require a lot of background or theoretical justification. You should provide more detail in the Methods and Results than you provide in the Introduction and Discussion. Ideally, the balance should be something like 10% of the word count to Introduction, 10% to Discussion, and 80% to Methods and Results. The background will be further developed in the thesis.
• should be well formatted, with clear demarcation of paragraphs and section headings. Use a reasonable font and single-spacing, as the paper will be marked electronically.
• must cite 8-15 pieces of appropriate literature, at least 8 going beyond the list of required and indicative readings in the course outline. Preference should be given to references that you have consulted for the analytical and statistical methods and are likely to go beyond those from biological anthropology. Use the citation style from Appendix 4 of the ‘Course and Assessment Guide’.
• must have at least 1 table and 1 figure in the paper, but no more than two of each. Each should be labelled and include a numbered and descriptive caption. Each should be referred to in text at least once. Do not cut and paste output from statistical software.
• should have a clear and concise title that reflects your specific topic.

As explained in Appendix 5 below, you may not use ChatGPT or other generative AI tools on this assessment.

The Project Report is worth 50% of you grade in the course, and will be marked using the following criteria:

• Quality of the project
• Quality of the writing
• Quality of the references
• Conformity to assessment instructions

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

All assessments are “returned” via Turnitin. That is, your mark and feedback for each assignment will be entered into Turnitin. When the marked assessments are released, you will be able to see your mark and feedback in Turnitin. The approximate dates for the return of assessments is included in the Course Overview section of this outline. 

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Not allowed.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students