The aim of the course

1. 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

2. Get acquainted with and get understanding of several advanced and current methodological topics, using a topic list 'capita selecta research methods and methodology' : varying from classical topics such as (experimental) research designs, validity, reliability, generalisability, causality and confounding, to more current topics such as meta-analyses, multi level research, grounded theory and action research.

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

Elementary Research Methodology

• The research process
• Conceptualisation and measurement: levels of measurement (nominal,ordinal, interval, ratio), measurement validity (face validity, content validity, criterion validity, construct validity), reliability (alternate-forms reliability, test-retest reliability, inter-item reliability, inter-observer reliability)
• Sampling methods: probability sampling (simple random, systematic random, cluster, stratified) versus non-probability sampling (quota,snowball,purposive, availability)
• Research design (experimental versus observational, causation and confounding, internal validity versus external validity, threads to internal validity)
• Experiments (pre-experiments, quasi experiments, pure experiments)
• Survey-research
• Basics of qualitative research (comparison with quantitative research, validity and reliability, participant observation, intensive interviewing, focus groups)

 

http://www.cs.uu.nl/education/vak.php?vak=INFOARM&jaar=2010