• Class Number 3525
  • Term Code 2930
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Geoffrey Kushnick
    • Dr Geoffrey Kushnick
  • Class Dates
  • Class Start Date 25/02/2019
  • Class End Date 31/05/2019
  • Census Date 31/03/2019
  • Last Date to Enrol 04/03/2019
SELT Survey Results

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.

Staff Feedback

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

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). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction
2 Independent Study
3 Consultations Consultations
4 Independent Study
5 Independent Study
6 Workshop Workshop Particpation
7 Independent Study
8 Independent Study
9 Independent Study
10 Presentations Presentations
11 Independent Study
12 Independent Study Data Analysis Project

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Consultation 15 % 12/03/2019 26/03/2019 1, 4
Workshop Participation 10 % 01/04/2019 15/04/2019 5
Presentation 15 % 13/05/2019 27/05/2019 1, 4
Data Analysis Project 60 % 27/05/2019 12/06/2019 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


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 Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

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 ANU Online website. Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

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.


This course does not include any formal examinations. 

Assessment Task 1

Value: 15 %
Due Date: 12/03/2019
Return of Assessment: 26/03/2019
Learning Outcomes: 1, 4


40-minute consultations will be scheduled for Week 3.

Consultation Preparation Summaries are due T 12 March by 5pm (submitted via Turnitin).


During the consultation you will meet for a 40-minute 1-on-1 sessions with the course convenor in Banks 44 to discuss your Data Analysis Project. You are expected to come prepared and to participate in a substantive way to the consultation.

As preparation, you are expected to submit a Consultation Preparation Summary. This document should be 1-page long (at least 500 words). It should Include brief summaries of your topic, data source, and analysis plan. It should also include full bibliographic details (APA format) for two references that you have consulted as part of the preparation.

Marking Criteria:

Your Consultation will be marked out of 100 and converted to 15% for the calculation of your final grade in the course. You will be marked based on the following criteria:

  • Quality of Consultation Preparation Summary
  • Quality of Participation in Consultation

Mark assigned as follows:

  • HD (80-100%): exemplary in both marking criteria.
  • D (70-79%): problem(s) in one of the marking criteria.
  • CR (60-69%): problem(s) in both marking criteria, or major problem in one.
  • P (50-59%): major problems in both marking criteria.
  • N (0-49%): incomplete or failure to submit.

Assessment Task 2

Value: 10 %
Due Date: 01/04/2019
Return of Assessment: 15/04/2019
Learning Outcomes: 5

Workshop Participation


We will have a one-hour workshop related to the Data Analysis Project. You are required, by the time of the workshop, to have put substantive effort into your Data Analysis Project, and your participation should reflect that. Aside from that, no special preparation is required.

Marking Criteria:

  • 100% -- Substantive participation in workshop with evidence of preparation.
  • 70% -- Substantive participation in workshop without evidence of preparation.
  • 50% -- Non-substantive participation in workshop.
  • 0% -- Lack of participation in workshop.

Assessment Task 3

Value: 15 %
Due Date: 13/05/2019
Return of Assessment: 27/05/2019
Learning Outcomes: 1, 4


You will give a polished 10-minute presentation in class about your Data Analysis Project. In your presentation, you should:

  • provide a brief overview of your talk at the beginning.
  • have a first slide with your name (first and last) and title of your talk to supplement the brief overview.
  • use additional slides to supplement the presentation. The ideal number of slides in no more than 5 (which is 2 minutes per slide). Slides that feature well-crafted tables or figures are ideal.
  • not overcrowd your slides.
  • use a font size that is readable from a distance for all text part. At least 18-pt font (preferably larger) for all text parts is recommended. Note that this assumes you are using Powerpoint. Other software may require larger fonts for readability.
  • cite your sources on the slide itself, on a references slide, or in a separate handout. You should cite at least 5 works related to analysis and methods using APA format.
  •  practice your presentation ahead of time.
  • speak clearly and present with confidence.
  • be ready to transition to conclusions, if you have not already, when you are told you have reached the 8-minute mark of your total allotment of 15 minutes. Note that you will not be allowed to go over 15 minutes.
  • be prepared to answer questions for around 5 minutes after your presentation.

Marking Criteria:

  • Your presentation will be marked out of 100 and converted to 15% for the calculation of your final grade in the course. You will be marked based on the following criteria:
  • Quality of Content: Presentation reflects a well-thought out Data Analysis Project with ample evidence of critical thinking and understanding of the analytical methods.
  • Quality of References.
  • Quality of Overheads.
  • Conformity to Instructions.

Mark assigned as follows:

  • HD (80-100%): exemplary in all marking criteria.
  • D (70-79%): problem(s) in 1-2 of the marking criteria.
  • CR (60-69%): problem(s) in more than 2 marking criteria, or major problem in one.
  • P (50-59%): major problems in 2-3 more criteria.
  • N (0-49%): major problems in all marking criteria, or failure to present.

Assessment Task 4

Value: 60 %
Due Date: 27/05/2019
Return of Assessment: 12/06/2019
Learning Outcomes: 1, 2, 3, 4, 5

Data Analysis Project


The data analysis project is a 3500 to 4000-word (not including tables, figures and references) essay that reports a set of analyses (methods and results, with brief intro and discussion) that provide you with a practice run for the sorts of analyses that will eventually be in your thesis once you have the actual data. The analyses should be quantitative except under very limited circumstances with written permission from Honours Supervisor. Here are instructions:

  • Do produce a polished essay that reflects (a) an entire semester’s work, and (b) a data analysis project that is characterized by appropriate use of analytical methods and excellent critical thinking. Start working on the project at the beginning of the semester. There are many steps along the way—identifying a problem, identifying data, identifying the appropriate analytical approach, doing the analysis, and refining the analysis—and you will not be hounded. You must work independently. Think of it as a testing ground for a thesis project.
  • Do provide complete details of your methods in the Methods section, and complete details of the results in the Results section. The Methods should also include a summary of the statistical methods you are using with enough detail so that it is clear that you fully understand their use and assumptions. The very brief Introduction should provide a statement of the problem and some background. The very brief Discussion should tie things together, discussing the results in a non-technical way and bringing the results to bear on the literature.
  • Do produce a well formatted essay. Use a reasonable font. Single or double space. Make sure it is clear where new paragraphs begin by indenting or putting a space between paragraphs. Use subheadings to divide the paper into its component parts.
  • Do cite 5 or more pieces of appropriate literature, going beyond the list of recommended readings in this course outline. Use APA citation guidelines for both in-text citations and the references cited.
  • You must have at least one table and one figure in the paper, but no more than two of each. Each should be labelled with Figure or Table followed by a number, followed by a concise description of the table or figure. Don’t cut and paste output from stats packages (like SPSS or Stata) to your essay. You should reformat the relevant material into thesis-quality tables or figures, when appropriate. Note that figures, tables, and their descriptions do not count toward the word count of the project.
  • The essay should have a clear, concise title that reflects your specific topic.
  • Your work on the Data Analysis Project may be ‘recycled’ in your thesis as long as you follow the current guidelines for doing so.

Marking Criteria:

Your report will be marked out of 100 and converted to 60% for the calculation of your final grade in the course. You will be marked based on the following criteria:

  • Quality of Content: Project should take on a problem of relevance to biological anthropology; data and analyses should be appropriate to the problem; analyses well done; essay is characterized by excellent critical thinking and understanding of the approach used.
  • Quality of Writing: Project should be free from grammatical and stylistic errors.
  • Quality of References.
  • Conformity to Instructions.

Marks assigned as follows:

  • HD (80-100%): exemplary in all marking criteria.
  • D (70-79%): problem(s) in 1-2 of the marking criteria.
  • CR (60-69%): problem(s) in more than 2 marking criteria, or major problem in one.
  • P (50-59%): major problems in 2-3 more criteria.
  • N (0-49%): major problems in all marking criteria, or failure to submit.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

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) as 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. Late submission is not accepted for take-home examinations.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

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. The Course Convener may grant extensions 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

Resubmission is 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).

Dr Geoffrey Kushnick

Research Interests

Dr Geoffrey Kushnick

Monday 11:00 12:00
Tuesday 10:30 11:30
Dr Geoffrey Kushnick

Research Interests

Dr Geoffrey Kushnick

Monday 11:00 12:00
Tuesday 10:30 11:30

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