Outline

Overview

This unit of study expands upon students' knowledge of the general linear model and its applications in the analysis of data from psychological research. One half of the unit introduces students to contrast analysis and interaction analyses as an extension of ANOVA, which allows for more focused analysis of data where group comparisons are the primary interest. Another half focuses on multiple regression and its extensions, which are used when the primary interest is to predict or explain a particular variable based on a set of other variables.

Unit details and rules

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	PSYC2012 and (PSYC2X10 or PSYC2X11 or PSYC2013 or PSYC2014 or PSYC2X15 or PSYC2016 or PSYC2017
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Sabina Kleitman, sabina.kleitman@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final Exam Extended-answer questions.	40%	Formal exam period	3 hours
Outcomes assessed:
Online task	ANOVA Quizzes and MR Quizzes MCQ and/or SAQ.	20%	Multiple weeks	See Canvas for specifics.
Outcomes assessed:
Assignment	ANOVA Assignment Written assignment.	20%	Week 08 Due date: 04 Oct 2021 at 23:59 Closing date: 01 Nov 2021	1000 words
Outcomes assessed:
Assignment	MR Assignment Written assignment.	20%	Week 12 Due date: 05 Nov 2021 at 23:59 Closing date: 03 Dec 2021	1000 words
Outcomes assessed:
= hurdle task ? = Type D final exam ?

Assessment summary

ANOVA Assignment: This assignment is based on lecture and tutorial material from Weeks 1 – 6 (inclusive). You will be required to analyse a student-specific dataset of a factorial ANOVA using SPSS to answer contrast questions, interpret the output, and write a brief report. Reports will be evaluated on the basis of the appropriateness of analyses and contrasts to answer the research questions as they are related to a) the given experimental design, b) the plausibility of the data, and c) the clarity and conciseness of the written interpretations. This assignment is not compulsory – if you do not complete this assignment in the specified time frame, you will simply not receive the marks associated with it. A successful application for Special Consideration for the ANOVA Assignment will result in an extension (up until the closing date) or submitting a replacement version of the assignment.
MR Assignment: This assignment is based on lecture and tutorial material from weeks 7–9 (inclusive). You will be required to analyse a dataset using SPSS, to perform and interpret regression analyses correctly and competently to address the postulated assignment requirements. You will be evaluated based on the appropriate use of SPSS to conduct regression analyses, valid interpretation of SPSS output and conclusions drawn, and competency of your overall output addressing assignment requirements. This assignment is not compulsory – if you do not complete this assignment in the specified time frame, you simply will not receive the marks associated with them. A successful application for Special Consideration for the MR Assignment will result in an extension (up until the closing date) or submitting a replacement version of the assignment.
ANOVA Quizzes & MR Quizzes: There will be quizzes for the ANOVA and MR parts of the course with five summative quizzes for EACH part of the course. The quizzes are based on lecture and tutorial material from Weeks 1–6 (inclusive) for the ANOVA part and Weeks 7–12 (inclusive) for the MR part. You will be evaluated based on appropriate understanding and interpretation of information provided, including SPSS output in light of theory, research design and postulated questions. Quizzes will be available progressively throughout the semester. The quizzes will have to be completed to their relevant deadlines.
Final Exam: The final exam is a compulsory assessment, but so long as you attend/attempt it no minimum performance is required. A successful application for Special Consideration or Special Arrangements for the Final Exam will result in you being offered a Replacement Exam during the Replacement Exam period.

More 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.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

You will receive a penalty of 5% of the maximum value of the assignment for each calendar day it is submitted after your due date. More than 10 calendar days after your due date, a mark of zero is applied. Submissions will not be accepted after the closing date.

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	GLM and ANOVA	Lecture (1 hr)
Week 01	Contrasts: formulation and testing	Lecture (1 hr)
Week 02	Contrasts: trend analysis, orthogonality	Lecture (1 hr)
	Contrasts: adjusting for type 1 errors	Lecture (1 hr)
	ANOVA and Contrasts	Tutorial (2 hr)
Week 03	Two-way ANOVA model part 1	Lecture (1 hr)
	Two-way ANOVA model part 2	Lecture (1 hr)
	Contrasts	Tutorial (2 hr)
Week 04	Two-way ANOVA: interaction contrasts	Lecture (1 hr)
	Repeated measures 1	Lecture (1 hr)
	Trend Contrasts	Tutorial (2 hr)
Week 05	Repeated measures 2	Lecture (1 hr)
	Contrasts for 2-way ANOVA designs	Lecture (1 hr)
	Multifactor ANOVA	Tutorial (2 hr)
Week 06	Mixed designs 1	Lecture (1 hr)
	Mixed designs 2 and extensions	Lecture (1 hr)
	Repeated measures	Tutorial (2 hr)
Week 08	Simple linear regression: revision and extension	Lecture (1 hr)
Week 08	Multiple regression 1: introduction	Lecture (1 hr)
Week 09	Multiple regression 2: more detail	Lecture (1 hr)
	Multiple regression 3: more detail	Lecture (1 hr)
	Multiple regression 1	Tutorial (2 hr)
Week 10	Multiple regression 4: 3+ variables	Lecture (1 hr)
	Different types 1	Lecture (1 hr)
	Multiple regression 2	Tutorial (2 hr)
Week 11	Different types 2	Lecture (1 hr)
	Continuous variables and interactions	Lecture (1 hr)
	Three types of multiple regression	Tutorial (2 hr)
Week 12	Categorical and continuous variables, more on interactions	Lecture (1 hr)
Week 12	Interactions and curves	Lecture (1 hr)
Week 13	Assumptions	Lecture (1 hr)
	Summary and revisions	Lecture (1 hr)
	Summary; reliability and assumptions	Tutorial (2 hr)

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

Section 1: ANOVA (Weeks 1 – 6)

Set Textbook:
- No set textbook for this section
Recommended:
- Howell, D. C. (2013). Statistical Methods for Psychology (8th ed.). Belmont, CA: Wadsworth, Cengage Learning. (other editions will also be helpful)
- Maxwell, S. E. & Delaney, H. D. (2004). Designing experiments & analyzing data: a model comparison perspective. (2nd Ed) Belmont, CA: Wadsworth. NOTE: e-book
- Field, A. (2013-2018). Discovering Statistics Using IBM SPSS Statistics, 4th Edition. Sage.
- Keppel, G. & Wickens, T.D. (2004) Design and Analysis: A Researcher’s Handbook. (4th Ed) NJ: Prentice Hall.

Section 2: MR (Weeks 8 – 13)

Set Textbook:
- Keith, Z. T. (2006-2014). Multiple Regression and Beyond. Pearson New International Edition. USA: Pearson Education, Inc.
Recommended:
- Berk, R. A. (2006). Regression Analysis: A constructive Critique. Advanced Quantitative Techniques in the Social Sciences Series. Sage Publications, Inc.
- Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research: Explanation and Prediction. Harcourt Brace College Publishers: New York.

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. develop a thorough understanding of techniques of statistical inference used in psychological research, including the ability to conduct and interpret analyses
LO2. understand, apply, and evaluate research methods in Psychology, including research design, advanced data analysis and interpretations, and the appropriate use of terminology
LO3. use critical thinking to solve problems related to psychological inquiry
LO4. value empirical evidence; act ethically and professionally
LO5. communicate effectively in a variety of formats and in a variety of contexts
LO6. develop an awareness of the applications of the statistical theory and research design in psychology to examine problems in everyday life and in society

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.

Additional information

Purchasing SPSS software is not essential for this course. The Learning Hub computers across campus have SPSS installed. Students may wish to purchase a copy of IBM SPSS Statistics from the Co-Op bookshop. Version 24 for Mac and PC is the latest version, but earlier versions are more than adequate. Please note that the SPSS Base Grad pack is a limited version that DOES NOT allow you to run all the analyses you need (i.e., it is not suitable for this course). It is your responsibility to check version/operating system compatibility. Note that SPSS is available via the ICT Virtual Desktops located in the Access labs and University Libraries, and can also be accessed online through Bring Your Own Device (BYOD). Although SPSS is now up to version 26, earlier versions are more than adequate.

Additional costs

There are no additional costs for this unit. Purchasing the SPSS licence is recommended but optional.

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

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

PSYC3010: Advanced Statistics for Psychology

Semester 2, 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

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect