- Code PSYC3018
- Unit Value 6 units
- Offered by School of Medicine and Psychology
- ANU College ANU Joint Colleges of Science
- Course subject Psychology
- Areas of interest Psychology
- Academic career UGRD
- Dr Daniel Skorich
- Mode of delivery In Person
First Semester 2023
See Future Offerings
In 2023, this course is on campus with remote adjustments only for participants with unavoidable travel restrictions/visa delays.
In this course, you will build on and consolidate knowledge gained in PSYC2009. The course is geared particularly towards fostering a deep understanding of Analysis of Variance (ANOVA) and Multiple Regression Correlation (MRC) approaches to data analysis. The primary focus of this course is on conceptual understanding of the various topics and the two approaches, with a secondary focus on implementation and use of ANOVA and MRC techniques (in JASP). In trying to achieve conceptual understanding, PSYC3018 makes explicit the conceptual and mathematical overlap between ANOVA and MRC, emphasizing the fundamental point that ANOVA is a special case of MRC. The lecture and lab programs are structured around emphasizing this overlap; ANOVA and MRC lectures are interspersed and will often draw on the same examples, and each lab will focus on analyzing the same data using both ANOVA and MRC techniques. Within this broad focus on ANOVA and MRC, you will also be presented with advanced material on: null hypothesis significance testing; the quantification of uncertainty; statistical prediction; effect sizes; interpretation of results; and the relationship between statistics and scientific understanding.
Upon successful completion, students will have the knowledge and skills to:
- Describe and explain the unique conceptual underpinnings of a variety of statistical techniques, including Analysis of Variance (ANOVA) and Multiple Regression Correlation (MRC), as well as their conceptual overlap.
- Choose and apply the appropriate statistical techniques for a variety of research designs.
- Implement statistical analyses in JASP.
- Read and interpret data analysis output in JASP and results reported in scientific articles.
- Set up, conduct, and report statistical analyses in a style appropriate for Psychology.
- JASP Assignment (20) [LO 1,3,4,5]
- Mid-Semester exam (20) [LO 1,2,4]
- Conceptual Assignment (35) [LO 1,2,5]
- Final Exam (25) [LO 1,2,4]
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The expected workload will consist of approximately 130 hours throughout the semester including:
- Face-to face component which will consist of 1 x 2 hour lecture per week plus 16 hours of labs spread across the semester.
- Approximately 90 hours of self-study which will include preparation for lectures, laboratory classes and other assessment tasks.
To be determined
Requisite and Incompatibility
Keppel, G., & Zedeck, S. (1989). Data analysis for research designs. Macmillan.
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|3917||20 Feb 2023||27 Feb 2023||31 Mar 2023||26 May 2023||In Person||N/A|