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	Daniel Costa, daniel.costa@sydney.edu.au
Lecturer(s)	Rebecca Pinkus, rebecca.pinkus@sydney.edu.au
	Daniel Costa, daniel.costa@sydney.edu.au
	Haryana Dhillon, haryana.dhillon@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam See Canvas for details.	40%	Formal exam period	2 hours
Outcomes assessed:
Placement	Research Participation See Canvas for details.	5%	Ongoing	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 1 See Canvas for details.	3%	Week 02	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 2 See Canvas for details.	3%	Week 03	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 3 See Canvas for details.	3%	Week 04	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 4 See Canvas for details.	3%	Week 05	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 5 See Canvas for details.	3%	Week 06	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 6 See Canvas for details.	3%	Week 07	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 7 See Canvas for details.	3%	Week 08	See Canvas for details.
Outcomes assessed:
Assignment	Assignment See Canvas for details.	25%	Week 09 Due date: 28 Apr 2023 at 23:59 Closing date: 19 May 2023	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 8 See Canvas for details.	3%	Week 10	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 9 See Canvas for details.	3%	Week 11	See Canvas for details.
Outcomes assessed:
Tutorial quiz	Quiz 10 See Canvas for details.	3%	Week 12	See Canvas for details.
Outcomes assessed:
= hurdle task ?

Assessment summary

Research participation: Research participation is not compulsory. If you do not complete any or all of your 5 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).
Quizzes: If you do not complete any of these quizzes, you will not receive the marks associated with it. If you successfully apply for special consideration for a quiz, you will receive a marks adjustment. If, however, you miss 7 or more quizzes, you will be required to complete an alternative task, which will be an oral assessment.
Assignment: If you do not complete the assessment, you will not receive the marks associated with it. If you have successfully applied for special consideration, the outcome will be a replacement oral assessment.
Final exam: If you do not complete the assessment, you will receive an Absent Fail (AF) grade for this unit. If you have successfully applied for special consideration, the outcome will be a replacement final exam to be completed during the replacement exam period. If you do not complete the first replacement exam and have Special Consideration approved for a second replacement, this second replacement will be an oral 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	Introduction to quantitative methods and analysis	Lecture (3 hr)
Week 02	Descriptive statistics and the normal distribution	Lecture (3 hr)
Week 02	Descriptive statistics and the normal distribution	Tutorial (2 hr)
Week 03	Research designs and null hypothesis significance testing	Lecture (3 hr)
Week 03	Research designs and null hypothesis significance testing	Tutorial (2 hr)
Week 04	z-tests	Lecture (3 hr)
Week 04	z-tests	Tutorial (2 hr)
Week 05	Introduction to qualitative research	Lecture (3 hr)
Week 05	Introduction to qualitative research	Tutorial (2 hr)
Week 06	Design, data collection and analysis in qualitative research	Lecture (3 hr)
Week 06	Design, data collection and analysis in qualitative research	Tutorial (2 hr)
Week 07	One- and related-samples t-tests	Lecture (3 hr)
Week 07	One- and related-samples t-tests	Tutorial (2 hr)
Week 08	Independent-samples t-tests	Lecture (3 hr)
Week 08	Independent samples t-tests	Tutorial (2 hr)
Week 09	One-way analysis of variance	Lecture (3 hr)
Week 09	One-way analysis of variance	Tutorial (2 hr)
Week 10	Interpreting and evaluating the quality of qualitative research	Lecture (3 hr)
Week 10	Interpreting and evaluating the quality of qualitative research	Tutorial (2 hr)
Week 11	Two-way analysis of variance	Lecture (3 hr)
Week 11	Two-way analysis of variance	Tutorial (2 hr)
Week 12	Correlation and regression	Lecture (3 hr)
Week 12	Correlation and regression	Tutorial (2 hr)
Week 13	Chi-square tests and other research methods topics	Lecture (3 hr)
Week 13	Chi-square tests and other research methods topics	Tutorial (2 hr)

Attendance and class requirements

For each weekly learning module, pre-recorded lectures and exercises will be made available each Monday. It is expected you will spend approximately two hours on this material. Each Tuesday, there will be a live lecture that will involve revision and the opportunity to ask questions. Each week there will be one 2-hour tutorial, which will be face-to-face unless you have been approved to enrol as a remote student.

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

There are no required readings in this unit. Two recommended resources are the two versions of David Howell’s texts:

Howell, D. C. (2012). Statistical methods for psychology (8th ed.). Belmont, CA: Wadsworth Cengage Learning.
Howell, D. C. (2017). Fundamental statistics for the behavioral sciences (9th ed.). Belmont, CA: Wadsworth Cengage Learning.
- For those students who have done no statistics before (PSYC1001/1002 not included) and are apprehensive, the Fundamental textbook is recommended. For those who have some statistical training, the Statistical methods textbook is more advanced and a valuable reference for further study in Psychology. Earlier editions of the textbooks are suitable. See Canvas for updated pricing and library availability information.

For Statistics, useful resources are:

Aron, A., Aron, E. N., & Coups, E. (2013). Statistics for psychology (6th ed.). Upper Saddle River, NJ: Prentice Hall.
*Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). New York: Sage.
King, B. M., & Minium, E. W. (2003). Statistical reasoning in psychology and education (4th ed.). New York: Wiley.
Wheelan, C. (2013). Naked statistics: Stripping the dread from the data. New York: Norton.

*This text is useful for understanding statistics as well as SPSS.

For Research Methods, useful resources are:

Gravetter, F. J., & Forzano, L. B. (2016). Research methods for the behavioral sciences (5th ed.). Stamford, CT: Cengage Learning.
Mitchell, M, L., & Jolley, J. M. (2012). Research design explained (8th ed.). Belmont, CA: Thomson Wadsworth.
*Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77-101.
*Chiovitti, R. F., & Piran, N. (2003). Rigour and grounded theory research. Journal of Advanced Nursing, 44, 427-435.
*Gale, N. K., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013). Using the framework method for the analysis of qualitative data in multi-disciplinary heath research. BMC Medical Research Methodology, 13, 117. https://doi.org/10.1186/1471-2288-13-117 (Links to an external site.)
*Holloway, I., & Todres, L. (2003). The status of method: Flexibility, consistency and coherence. Qualitative Research, 3, 345-357.

*These resources are useful for qualitative research methods.

For using SPSS, some useful resources are:

Allen, P., Bennett, K., & Heritage, B. (2014). SPSS statistics version 22: A practical guide (3rd ed.). Melbourne, VIC, Australia: Cengage.
Pallant, J. F. (2013). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (5th ed.). Crows Nest, NSW, Australia: Allen & Unwin.

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 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.

This unit has been substantially revised in 2023, based on student feedback accumulated over several years. The learning outcomes are unchanged and the lecture and tutorial content largely the same, but the delivery format and assessments are different. Student feedback is welcome.

Additional information

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

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

PSYC2012: Statistics and Research Methods for Psych

Semester 1, 2023 [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

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect