Outline

Overview

Analytical Thinking is a course covering aspects of research design, interpretation of data, analysis, logic, and thinking processes. It is comprised of three sections: Data Concepts and Analysis; Logic and Basic Arguments; and Research and Everyday Reasoning. The section on Data Concepts and Analysis covers aspects of research design, data collection, and basic forms of hypothesis testing and statistical tests are introduced. The Logic and Basic Arguments section covers material ranging from valid and invalid forms of argument and errors in reasoning to critiques of arguments presented in case studies. The Research and Everyday Reasoning section examines how arguments and scientific evidence are presented and interpreted in the media, society, and interpersonal interactions. Together, the three course components teach foundational skills necessary for carrying out meaningful academic discussions, arguments, and research studies, which may be applied to any content area of scholarly enquiry.

Unit details and rules

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Bruce Burns, bruce.burns@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Exam Multiple choice and short answer questions	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Analysis and evaluation of a unique data set	17.5%	Week 07 Due date: 23 Apr 2021 at 23:59 Closing date: 21 May 2021	750 words
Outcomes assessed:
Assignment	Assignment 2 Questions on logic and research evaluation	22.5%	Week 11 Due date: 17 May 2021 at 23:59 Closing date: 07 Jun 2021	1000 words
Outcomes assessed:
Online task	Online mastery quizzes Short, mastery quizzes on that week's content (best of 10)	10%	Weekly	May be done multiple times
Outcomes assessed:
= hurdle task ? = Type B final exam ?

Assessment summary

Assignment 1: Mainly assesses material from the “data concepts and analysis” component of the unit.
Assignment 2: Mainly assesses material from the “Logic and Basic Arguments” component of the unit.
Mastery quizzes: There are 13 quizzes, however we only count your 10 best quiz marks. Each quiz will consist of a small number of questions about the material presented that week in lectures or tutorials.
Final exam: assess data concepts and analysis (45% weighting), Logic and Basic Arguments (30% weighting), and Research and Everyday Reasoning (25% weighting).

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts
Distinction	75 - 84	At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.
Credit	65 - 74	At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.
Pass	50 - 64	At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. What is analytical thinking?; 2. Why study statistics?; 3. Descriptive statistics	Lecture (3 hr)
Week 01	Orientation	Tutorial (1 hr)
Week 02	1. Deceptive statistics; 2. Correlation; 3. Basic probability	Lecture (3 hr)
Week 02	Using descriptive statistics	Tutorial (1 hr)
Week 03	1. Problems with probability; 2. Collecting data; 3. More on data	Lecture (3 hr)
Week 03	Using correlation	Tutorial (1 hr)
Week 04	1. The central limit theorem; 2. Hypothesis testing; 3. Inference	Lecture (3 hr)
Week 04	Instructions for assignment 1	Tutorial (1 hr)
Week 05	1. Statistical tests 2. Categorical data analysis; 3. Regression analysis;	Lecture (3 hr)
Week 05	Underpinnings of hypothesis testing	Tutorial (1 hr)
Week 06	1. Regression analysis issues; 2. Program evaluation; 3. Summing up	Lecture (3 hr)
Week 06	Testing hypotheses	Tutorial (1 hr)
Week 07	1. Correlation versus causation; 2. Mastering correlation versus causation; 3. Correlational study and experiment problems	Lecture (3 hr)
Week 07	Using statistical tests	Tutorial (1 hr)
Week 08	Arguments and logic	Lecture (3 hr)
Week 08	Evidence: correlations and experiments	Tutorial (1 hr)
Week 09	Arguments and logic	Lecture (3 hr)
Week 09	Logic and syllogisms 1	Tutorial (1 hr)
Week 10	Real-world reasoning and heuristics	Lecture (3 hr)
Week 10	Logic and syllogisms 2	Tutorial (1 hr)
Week 11	Real-world reasoning and heuristics	Lecture (3 hr)
Week 11	Reasoning	Tutorial (1 hr)
Week 12	Real-world reasoning and heuristics	Lecture (3 hr)
Week 12	Heuretics and biases	Tutorial (1 hr)
Week 13	Real-world reasoning and heuristics	Lecture (3 hr)
Week 13	Applications	Tutorial (1 hr)

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class. In that case, students should discuss the problem with the coordinator, and attend another session, if available.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Short readings for this unit can be accessed on the Library eReserve link available on Canvas. Their availablity will be announced when appropriate.

There is one recommended (but not required) text which will help you with the Data Concepts and Analysis section:

Wheelan, C. (2013). Naked Statistics: Stripping the dread from the data. New York: Norton.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Demonstrate a basic conceptual understanding of descriptive statistics used across disciplines.
LO2. Demonstrate a basic conceptual understanding of inferential statistics used across disciplines, though not of their mathematical underpinnings.
LO3. Demonstrate basic skills in computing and data handling
LO4. Understand and evaluate the quality of data based on its sources and the different methods used to collect it.
LO5. Identify ways of approaching the exploration of a research question and understand potential sources of bias in information sources.
LO6. Demonstrate a basic understanding of how logic can help analytical thinking and use it to analyze problems, within your discipline and across disciplines.
LO7. Demonstrate an understanding of basic processes of thinking and why these could lead to errors in reasoning, the evaluation of data and of learning.
LO8. Communicate the results of data analysis and research appropriately through written work

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ATHK1001: Analytical Thinking

Semester 1, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect