Course code:INFOARM
Credits:7.5 ECTS
Period:periode 2 (week 46 t/m 5, dwz 12-11-2007 t/m 1-2-2008; herkansing week 11)
Timeslot:A
Participants:up till now 23 subscriptions
Schedule:Dit is een oud rooster!
formgrouptimeweekroomteacher
college   wo 09-1146-51,2-4 BBL-416 Linda van der Gaag
Peter de Waal

practicum   wo 11-1341-51,2-4 BBL-519
groep 1 wo 11-1346-51,2-4 BBL-515
groep 2 wo 11-1346 BBL-412
47-51,2-4 BBL-516
groep 3 wo 11-1346-51,2-4 BBL-518
werkcollege   ma 11-1347-51,2-4 BBL-416
Contents:The aim of the course Get acquainted with and get understanding of several important multivariate statistical techniques . Successively the following subjects will be discussed:
• fundamental statistical concepts/elementary probability topics
• correlation and regression analysis|
• analysis of variance (one-way ANOVA, multi-way ANOVA, ANCOVA, repeated measures, multivariate ANOVA )
• discriminant analysis
• factor analysis (principal component analysis)
• cluster analysis
• multidimensional scaling/correspondence analysis
Prior statistical / methodological knowledge and skills
Some general knowledge of the following topics is required prior to the course. See below. Some of these topics will re-appear again in ARM, they will be reviewed thoroughly or even be treated in more depth in Kachigan. Students with no introductory knowledge of the following statistical topics are strongly advised not to take the ARM-course, but to take a more introductory course first.

Descriptive statistics:
• measures of central tendency (mean, median, modus), measures of disperion (range, variance, standard deviation, IQR), percentile values, kurtosis and skewness, frequency distributions, relative frequency and cumulative frequency, z-scores, empirical and theoretical distributions, the normal distribution, visualisation techniques (barcharts, piecharts, histograms, boxplots, scatterplots)
Elementary inferential statistics:
• sample versus population, sampling distributions, standard errors, central limit theorem, z-test, one sample t-test, degrees of freedom, the logic of hypothesis testing (significance level, p-value, one-sided and two sided testing, type 1 and type 2 errors) parameter estimation (confidence intervals), parametric versus non-parametric statistics
Statistical tests for two of more variables:
• chi-squared analysis, correlation analysis, two-group t-test, paired t-test, one-way ANOVA, simple regression-analysis
SPSS
• The student must have a working knowledge of SPSS 10.0 or higher to make datafiles, manipulate data (recode, compute, etc.), vizualize data, perform the above mentioned statistical techniques and interpret the SPSS outcome

Literature:
• Sam K. Kachigan (1991). Multivariate Statistical Analysis: A Conceptual Introduction. New York: Radius Press. ISBN 0-942154-91-6 (This may change, for the latest information consult the webpage of the course)
• Handouts of the lectures
• SPSS handouts
Course form:Lecture once a week and an exercise class once a week. Practical assignments have to be handed in every week.
Exam form:The examination comprises two parts:
• T: A written examination covering all topics discussed in the lectures. T is a "closed-book-exam", only a pocket calculator is allowed
• P: Each week the student is expected to perform a practical assignment. The results of these assignments are to be handed in and the student will receive a grade

The student has passed the course if

• 0.7*T + 0.3*P > 5.5, and
• P >= 5
• T >= 5
• A re-examination is only possible for T.