The purpose of this course is to introduce students to the major concepts and tools for collecting, analyzing, and
drawing conclusions from data in the behavioural sciences, particularly psycholinguistics. Students learn to handle
data in SPSS, run relevant descriptive and inferential statistical analyses and interpret and report statistics in APA
style. Participants are exposed to a variety of conceptual themes and statistical tests, as listed below:

• Overview of methods of data collection in the social sciences and experimental linguistics, descriptive

versus inferential statistics

• Planning and conducting surveys and experiments, different types of data (nominal, interval, ordinal, ratio

data), Likert-scale, importing data from Excel or E-Prime into SPSS (pitfalls: commas, dots, etc.)

• Sampling and experimentation: Planning and conducting a study, designs (within- and betweenparticipant

designs; power, advantages, disadvantages of these designs, aspects to consider), DV/IV

• Exploring data: describing patterns and departures from patterns, splitting data, selecting cases
• Visualizing relationships in data, constructing and interpreting graphical displays of distributions of

univariate data (dotplot, stemplot, bar diagram, histogram, cumulative frequency plot)

• Traditional statistical inference, probability, one-tailed, two-tailed, Type I and II errors, misinterpretation

of the concept of probability, Bayes’ Law

• Mean, median, mode, range, StDev, SE, variance. Trimming data, handling RT data, mean or median, rules

and restrictions of trimming data, outliers (Z-transformation, SD criteria).

• The normal distribution (Shapiro-Wilk test, Kolmogorov-Smirnov test, Z-transformation and logtransformation

in psycholinguistic experiments), ANOVA assumptions

• Summarizing distributions of univariate data
• Parametric tests: independent/dependent samples t-test, ANOVA, ANCOVA, Linear regression, Multiple

linear regression, Levene’s test of homogeneity, post-hoc tests in ANOVA and corrections (LSD, Tukey,
Scheffe)

• Non-parametric tests: Friedman’s ANOVA, Kruskal-Wallis H, Mann-Whitney U, Wilcoxon signed ranks test,

Chi-square test, McNemar’s test, adjustments for multiple comparisons (Bonferroni, Holm-Bonferroni
sequential correction)

• Exploring bivariate data
• Exploring categorical data (binary Logistic Regression)
• Correlation: Spearman’s rho (nonparametric), Pearson (parametric), partial correlation; Fisher’s Z test (FZT

Computator)

• Different Effect-size measures (G-Power analysis, Cohen’s d, partial Eta squared: ηp

2; Prajapati et al., 2010)

• Measures of effect size (MES; Hentschke and Stüttgen, 2011; Prajapati et al., 2010)
• Multivariate data: Cluster analysis techniques and dendrograms (HCA, 2-step, K-means)
• item- and participant-based analyses, linear mixed-effects models
• generating the SPSS syntax file

References:
Hentschke H., Stüttgen M.C. (2011). Computation of measures of effect size for neuroscience data sets. European Journal of
Neuroscience, 34: 1887.
Prajapati, B., M. Dunne, Armstrong, R. (2010). Sample size estimation and statistical power analyses.
Optometry Today, 16.