Introduction to a variety of advanced statistical methods used in psychology.
A practical introduction to a broad range of sophisticated statistical concepts and techniques used in psychological research. The emphases are on identifying research scenarios in which each concept or technique should be considered and on using appropriate statistical software to extract insights from a given data set.
|Paper title||Advanced Quantitative Methods|
|Teaching period||Semester 1 (On campus)|
|Domestic Tuition Fees (NZD)||$704.22|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- PSYC 461
Entry into Psychology 400-level normally requires a major in Psychology, a B+ average or higher in Psychology 300-level papers, and a pass in PSYC 311 Quantitative Methods. We highly recommend that students have completed PSYC 310. Students from other universities must show evidence of an equivalent level of competence.
Professor Jeff Miller - email@example.com
- More information link
View more information on the Department of Psychology's website
- Teaching staff
- Paper Structure
The paper will undertake a wide-ranging survey of statistical concepts and methods. Initially, fundamental statistical concepts will be re-examined within the context of ongoing controversies surrounding common statistical practices. Then, a wide range of advanced statistical techniques will be examined, focusing on (1) different types of questions that can be addressed; (2) types of data needed to address them; (3) how analyses can be conducted using appropriate statistical software; and (4) what the results mean.
PSYC 434 Paper Outline
The paper consists of a series of lectures, practical computer exercises, and homework exercises designed to give students both theoretical understanding of and practical experience with a range of advanced data analysis techniques including:
- Fundamental issues in hypothesis testing and replicability
- Categorical data analysis
- Nonparametric methods
- Logistic regression
- Factor analysis
- Multidimensional scaling
- Computer simulation
- Bootstrapping, Jackknifing, and permutation testing
Students will carry out the homework assignments in small groups with group membership varying across assignments. Experience has shown that students learn much about the topics from these group interactions. They also learn about how to work together with a colleague when analysing data, which is useful because most real-world research projects are analysed collaboratively.
- Homework assignments 50%
- Examination (2 hours) 50%
Course material will be provided electronically via Blackboard. Students should ensure that they know their user names and passwords and can access Blackboard before the start of classes.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Critical thinking, Information literacy.
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete this paper will:
- Understand how a variety of statistical techniques may be used in a range of experimental and applied situations (Information Literacy, Research)
- Assess the suitability of a variety of statistical techniques for the analysis of different types of data sets (Critical Thinking, Research)
- Evaluate the extent to which individual data sets satisfy the assumptions underlying different statistical techniques (Critical Thinking, Information Literacy)
- Independently select and apply an appropriate data analysis technique to answer a given research question; interpret the results of the analysis as they bear on that question (Communication, Critical Thinking, Research, Self- Motivation)