Outline

Overview

The aim is to introduce students to fundamental concepts in statistics and research design as applied to psychological research. These include summary descriptive statistics, an introduction to the principles and practice of research design (both quantitative and qualitative approaches), and the use of inferential statistics. Building upon this framework, the unit of study aims to develop each student's expertise in understanding the rationale for, and application of, a variety of statistical tests to the sorts of data typically obtained in psychological research.

Unit details and rules

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	PSYC1001 OR PSYC1002
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Recommended: HSC Mathematics, any level
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Rebecca Pinkus, rebecca.pinkus@sydney.edu.au
Lecturer(s)	Daniel Costa, daniel.costa@sydney.edu.au
	Rebecca Pinkus, rebecca.pinkus@sydney.edu.au
	Haryana Dhillon, haryana.dhillon@sydney.edu.au
Tutor(s)	Alice Lo, alice.lo@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Final online exam Open book, MCQs on lecture & tutorial content	20%	Formal exam period	1 hour
Outcomes assessed:
Final exam	Final take home exam Open book on lecture & tutorial content. 48 hours - Opens 17 June 1pm	20%	Formal exam period Due date: 19 Jun 2020 at 13:00	2.5+ hours
Outcomes assessed:
Placement	Research participation Be a participant in School of Psychology research and answer questions	5%	Ongoing	5 hours
Outcomes assessed:
Tutorial quiz	Tutorial test 1 Open book, online short answer small test on lecture & tutorial content	6%	Week 05 Due date: 24 Mar 2020 at 13:00	15 minutes + 2min reading time
Outcomes assessed:
Tutorial quiz	Tutorial test 2 Open book, online short answer small test on lecture & tutorial content	12%	Week 07 Due date: 07 Apr 2020 at 13:00	17 minutes + 2min reading time
Outcomes assessed:
In-semester test	In-semester exam Open book, computerized MCQs on lecture & tutorial content	25%	Week 09 Due date: 28 Apr 2020 at 13:00	45 minutes
Outcomes assessed:
Tutorial quiz	Tutorial test 3 Open book, online short answer small test on lecture & tutorial content	12%	Week 12 Due date: 19 May 2020 at 13:00	17 minutes + 2min reading time
Outcomes assessed:
= hurdle task ?

Assessment summary

Research participation: Research participation is not compulsory. If you do not complete any or all of your five hours of research participation, you simply will not receive the marks associated with it. An alternative to research participation (a written assignment) is also available on request (before Week 6) - more details on Canvas.
Tutorial tests: Tutorial tests 1, 2 & 3 are not compulsory. If you do not complete any of these tests, you simply will not receive the marks associated with it.
In-semester exam: This is a compulsory assessment; you must make a serious attempt at the exam or you will receive an Absent Fail (AF) mark, although no minimum performance is required.
Final exam: The initial final exam is a compulsory assessment, but so long as you attend no minimum performance is required. It will be delivered as a 1 hour online exam and a 48 horr take-home assessment.

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	This work shows excellent understanding of the topic and clear evidence of independent critical thought.
Distinction	75 - 84	This work shows a very good understanding of the relevant content. Some calculations or interpretations may be flawed, but a serious and sustained attempt has been made.
Credit	65 - 74	This work shows a clear understanding of the relevant material; it contains only small gaps or minor errors.
Pass	50 - 64	This work shows evidence of a satisfactory level of understanding of the relevant material; it may contain gaps, errors or other kinds of blemishes, but it is obvious that the student has read and digested material from lectures and/or tutorials.
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. The importance and ethics of statistics for psychological research; 2. Variables and descriptive statistics; 3. What is qualitative research? When and why would I use it?	Lecture (3 hr)
Week 02	1. Descriptive statistics and z-scores; 2. Linear transformations and the normal distribution; 3. Theory and how it influences qualitative research	Lecture (3 hr)
Week 02	Descriptive statistics	Tutorial (2 hr)
Week 03	1. The research process and research designs; 2. Research designs and null hypothesis significance testing; 3. Doing qualitative research: Design and data collection	Lecture (3 hr)
Week 03	Linear transformations, normal distribution, z-scores	Tutorial (2 hr)
Week 04	1. z-tests - part 1; 2. z-tests - part 2; 3. Doing qualitative research: Data analysis and reporting	Lecture (3 hr)
Week 04	NHST & z-tests	Tutorial (2 hr)
Week 05	1. t-test for a single mean - part 1; 2. t-test for a single mean - part 2; confidence intervals; 3. Doing qualitative research: Interpretation and reporting	Lecture (3 hr)
Week 05	z-tests & t-tests for a single mean	Tutorial (2 hr)
Week 06	1. Related samples t-test - part 1; 2. Related samples t-test - part 2; power and effect size; 3. Putting the 'quality' in qualitative research	Lecture (3 hr)
Week 06	Confidence intervals	Tutorial (2 hr)
Week 07	1. Independent samples t-test - part 1; 2. Independent samples t-test - part 2	Lecture (2 hr)
Week 07	Related samples t-test	Tutorial (2 hr)
Week 08	1. One-way ANOVA - part 1; 2. One-way ANOVA - part 2	Lecture (2 hr)
Week 08	Independent samples t-test	Tutorial (2 hr)
Week 09	1. One-way ANOVA - part 3; Two-way ANOVA - part 1; 2. Two-way ANOVA - part 2	Lecture (2 hr)
Week 09	One-way ANOVA	Tutorial (2 hr)
Week 10	1. Correlation - part 1; 2. Correlation - part 2	Lecture (2 hr)
Week 10	Two-way ANOVA	Tutorial (2 hr)
Week 11	1. Regression - part 1; 2. Regression - part 2	Lecture (2 hr)
Week 11	Correlation	Tutorial (2 hr)
Week 12	1. Chi-square tests - part 1; 2. Chi-square tests - part 2	Lecture (2 hr)
Week 12	Regression	Tutorial (2 hr)
Week 13	1. Reliability, validity and replicability; 2. Test selection and revision	Lecture (2 hr)
Week 13	Chi-square and revision	Tutorial (2 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.

Students will need to bring a calculator to all tutorials and assessments; the calculator should have statistical functions. The calculators used in HSC mathematics courses will be suitable.

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

Hayward, S. et al. (2018). Statistics for social sciences: A Top Hat interactive text.

Freberg, L. et al. (2018). Research methods in psychological science. A Top Hat interactive text.

Recommended reading resources for this unit can be accessed on the Library eReserve link available on Canvas.

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. Calculate and interpret descriptive statistics such as measures of central tendency and variability
LO2. Demonstrate understanding of graphical and tabular representations of data, and be able to use statistical tables
LO3. Conduct significance tests for statistical hypotheses relevant to Psychology
LO4. Compute and interpret confidence intervals and other effect size indices
LO5. Describe, apply, and evaluate the different quantitative research methods used by psychologists
LO6. Describe and define qualitative research methods used by psychologists
LO7. Demonstrate practical skills in laboratory-based and other psychological research
LO8. Recognise potential bias in human thinking, including cultural biases in interpretation of research
LO9. Use information in an ethical manner (e.g., acknowledge and respect work and intellectual property rights of others through appropriate citations in oral and written communication); understand the ethical obligations of research scientists and understand best practice in research design

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.

In response to previous student feedback, a single-sided A4 page of notes is now allowed for each of the in-class assessments. Students also recommended switching from the Canvas Discussion Boards to Piazza to facilitate engagement and search functionality. The qualitative lecture series has been consolidated to the first half of the semester. Students are encouraged to provide feedback on these changes.

Additional information

SIGNING UP FOR STUDIES ON SONA

The website to sign up for experiments is linked to from Canvas, but is an external website.

Your login and password is the unikey and unipass you use for Canvas and if you navigate to SONA directly from Canvas you won't have to log in again. However if you are a late enrolment this will not work until you are added to SONA. All students we know of are enrolled in the system from Week 1, and we update this list with new enrolments every week until all enrolments are accounted for. If you are a late enrolment, then simply wait and within a week you will be added.

Within the first few weeks, you will be offered the opportunity to complete a ‘pre-screen’ questionnaire. You do not have to complete this. If you choose to, then allow 30min to complete it – and you will receive 30min credit time (0.5%). The aim of the pre-screen is to allow researchers to more efficiently select subjects for later studies, but if you choose not to complete it you will still be able to participate in most studies.

You may browse for available studies, and sign-up for those you are interested in. Realise that each sign-up is an appointment you have with a researcher. The penalty for breaking an appointment if you do not cancel more than 24 hours before the study runs, is half a credit point. If you accumulate more than 5 penalties your access to SONA will be suspended.

Importantly, the online sign-up itself constitutes your informed consent to participate, so read the description well. Understand this:

I don’t understand how to use SONA	Read this section. Check the online documentation on SONA. Ask your colleagues. Ask your tutor.
I am unsure of what my password is	Your login and password are the same ones you use for all USYD materials (your unikey and unipass).
SONA does not recognize my UNIKEY	Check you’ve entered your UNIKEY correctly. If you have enrolled late, then simply wait (we will update SONA weekly with new enrolments). If the problem persists for more than a week, email your tutor with the details.

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.

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

PSYC2012: Statistics and Research Methods for Psych

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

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