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Statistics

Statistics (Dr Sander van der Linden)

Postgraduate students who wish to consolidate their study of statistics are welcome to attend the lecture course provided for final year Psychology undergraduates by Dr Sander van der Linden.

Michaelmas

10 lectures [Ground Floor Lecture Theatre]
Monday 10:00-11:00 19, 26 Nov
Tuesday 10:00-11:00 9, 16, 23, 30 Oct, 6, 13, 20, 27 Nov
Statistics Demonstration Classes
 [Titan Teaching Rooms, New Museums Site]
Thursday 09:00-11:00 18 Oct, 1, 15, 29 Nov

This course has two aims. First, to teach the theory required for quantitative analysis of a research project using a computer package; second, to prepare for the compulsory statistics component of the examination, which may require analyses to be conducted with pen, paper and hand-calculator.

The lectures cover the theory, including worked examples of exam-style questions, and practical classes allow the techniques to be practised, both by hand and using a computer.

The first half of the term on statistics will cover the basics of quantitative analysis (much of this will be revision for students who have studied part I NST or PBS 5): Exploratory Data Analysis; confidence intervals and effect size; hypothesis testing & significance; t tests; Pearson’s χ2; correlation; linear regression as an ANOVA model.

The final weeks of the course focus on analysis of variance: One-way analysis of variance and the multiple comparison problem; introduction to factorial ANOVA and interactions; repeated measures ANOVA ANCOVA and multiple regression. The course will focus upon the correct interpretation of ANOVA tables and related computer outputs.


Suitable Textbooks:

Gravetter, F.J., & Wallnau, L.B. (2012). Statistics for the behavioral sciences (9th ed.). Belmont, Calif.: Wadsworth Cengage Learning. [Earlier editions are also acceptable. Covers same material, to the same level, as this course.] 


Howell, D. (2013). Statistical methods for psychology (8th ed.). Belmont, Calif.: Wadsworth Cengage Learning. [Fifth or later edition may be used. Slightly more advanced, with considerably more detail, especially on ANOVA methods, than you need for this course & exam; may be a useful resource for analysing research project data.]