PSY9170 – Quantitative Analysis I
Schedule, syllabus and examination date
Course content
The course is part I of a course in the analysis of problems that can be elucidated by quantitative data, and is organized around the general linear model. Part II is PSY9175
Based on specific problems (the participants' own data if possible), topics such as formulation of problems, operationalization of theoretical concepts, measurement, analysis of variation and covariation between observed variables and common ways of presenting results from such analyses are reviewed.
The course assumes knowledge of elementary quantitative analyses and elementary statistical methods.
Learning outcome
The course will have an overarching "variance analysis perspective" in the sense that understanding/explaining variation in observed variables will be an overarching goal. Models involving latent variables and covariance models are discussed in other courses at the department and in part 2 of this course. In analyzing all issues, emphasis will be placed on discussions of the substantive interpretation of the parameters in the linear models.
Day 1 (5 hours): "Multiple regression analysis" (with emphasis on data from passive observation).
Analysis of issues involving unconditional, conditional and mediated effects in linear models. The analysis methods will be based on linear regression analysis with both quantitative and qualitative (dichotomous) explanatory variables.
Day 2 (5 hours): "Multiple Regression Analysis" continued.
Analysis of problems involving moderated effects (interaction) and problems involving non-linear relationships between variables - analyzed by linear models (polynomial regression models).
Day 3 (5 hours): "Analysis of Variance" (with emphasis on data from experimental designs).
Analysis of the same type of problems as on days 1 and 2, but with qualitative explanatory variables. Discussion of experimental designs versus designs based on passive observation (discussion of "internal validity").
Day 4 (5 hours): "Analysis of Variance" continued.
Analysis of designs involving repeated measures ("repeated measures anova").
Discussion of assumptions related to the analysis of ordinary linear models and statistical hypothesis testing. Discussion of alternative methods for statistical hypothesis testing when one must assume violations of critical assumptions - with particular emphasis on computer intensive methods ("bootstrap" and "monte-carlo" methods).
Day 5 (5 hours): "Logistic regression analysis".
Analysis of the same type of problems as the previous days (unconditional, conditional, mediated and moderated effects), but in situations where the dependent variable is a dichotomous variable.
Admission to the course
The course is aimed at Ph.d. candidates. The Department of Psychology's own Ph.d. candidates will have first priority, then Ph.d. candidates from other institutions, then other applicants.
Ph.d. candidates from the Department of Psychology register for the course via StudentWeb. Please contact the administration if you experience problems with registration.
Other applications are made via this online form.
The registration period is stated in the online form and you will receive an email shortly after the application deadline if you are offered a place in the course.
Everyone must be registered in Studentweb before the first day of classes.
Formal prerequisite knowledge
Admission to a Ph.d. program.
Teaching
Attendance at all classes is mandatory. Maximum absence is 20%.
The analysis tool SPSS is used for all analyses and the analyses are demonstrated by the course leader and carried out by the participants. The classes take place in rooms where all participants have access to a computer.
See the course's semester page for the timetable.
Examination
Approval of the course requires participation in all lectures and written submission of an analysis of a problem that can either be based on data from your own research project or an analysis of a distributed "case". Delivered via Inspera.
Examination support material
No aids are allowed.
Language of examination
You can answer the exam in Norwegian, Swedish, Danish or English.
Grading scale
The course uses a pass/fail grading scale. Read more about the grading scale.
More about examinations at UiO
- Use of sources and citations
- How to use AI as a student
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.