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Unit of study_

PSYC3010: Advanced Statistics for Psychology

Semester 2, 2022 [Normal day] - Remote

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

Unit code PSYC3010
Academic unit Psychology Academic Operations
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
PSYC2012 and (PSYC2X10 or PSYC2X11 or PSYC2013 or PSYC2014 or PSYC2X15 or PSYC2016 or PSYC2017)
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Daniel Costa, daniel.costa@sydney.edu.au
Lecturer(s) Sabina Kleitman, sabina.kleitman@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home extended release) Type E final exam hurdle task Final Exam
Extended-answer questions.
40% Formal exam period 48 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Online task ANOVA Quizzes and MR Quizzes
MCQ and/or SAQ.
20% Multiple weeks See Canvas for specifics.
Outcomes assessed: LO1 LO6 LO4 LO3 LO2
Assignment MR Assignment
Written assignment.
20% Week 05
Due date: 02 Sep 2022 at 23:59

Closing date: 16 Sep 2022
500 words excluding SPSS output & syntax
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment ANOVA Assignment
Written assignment.
20% Week 11
Due date: 21 Oct 2022 at 23:59

Closing date: 04 Nov 2022
1000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
hurdle task = hurdle task ?
Type E final exam = Type E final exam ?

Assessment summary

  • MR Assignment: This assignment is based on lecture and tutorial material from weeks 1–4 (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 Assignment: This assignment is based on lecture and tutorial material from Weeks 7 – 10 (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.
  • 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 MR part and Weeks 7–12 (inclusive) for the ANOVA 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 sydney.edu.au/students/guide-to-grades.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Simple linear regression: revision and extension Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multiple regression 1: introduction Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Multiple regression 2: more detail Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multiple regression 3: more detail Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multiple regression 1 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Multiple regression 4: 3+ variables Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Different types 1 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Multiple regression 2 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 04 Different types 2 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Continuous variables and interactions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Three types of multiple regression Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 05 Categorical and continuous variables, more on interactions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Interactions and curves Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 06 Assumptions Lecture (1 hr) LO1 LO2 LO3 LO4
Summary and revisions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Summary; reliability and assumptions Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 07 One-way analysis of variance Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Contrast analysis Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
One-way analysis of variance Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 Type I error correction Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Two-way analysis of variance Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
One-way contrast analysis Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 09 Two-way contrast analysis I Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Two-way contrast analysis II Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Two-way analysis of variance Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 10 Psychological measurement Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Repeated measures I Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Two-way contrast analysis Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Repeated measures II Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Mixed designs Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Repeated measures and mixed designs Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 13 Advanced topics Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Overview and revision Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Overview and revision Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6

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: MR (Weeks 1 – 6)

  • 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.

Section 2: ANOVA (Weeks 7 – 13)

  • 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.

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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

.

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.