• 1 Some fundamentals of empirical research
• 1.1 Introduction
• 1.2 On the relevance of quantitative methods in linguistics
• 1.3 The design and the logic of quantitative studies
• 1.3.1 Scouting
• 1.3.2 Hypotheses and operationalization
• 1.3.2.1 Scientific hypotheses in text form
• 1.3.2.3 Scientific hypotheses in statistical/mathematical form
• 1.4 Data collection and storage
• 1.5 The decision
• 1.5.1 One-tailed p-values from discrete probability distributions
• 1.5.2 Two-tailed p-values from discrete probability distributions
• 1.5.3 Significance and effect size
• 1.6 The design of a factorial experiment
• 2 Fundamentals of R
• 2.1 Introduction and installation
• 2.2 Functions and arguments
• 2.3 Vectors
• 2.3.1 Generating vectors
• 2.3.3 Working with vectors
• 2.4 Factors
• 2.4.1 Generating factors
• 2.4.3 Working with factors
• 2.5 Data frames
• 2.5.1 Generating data frames
• 2.5.3 Working with data frames
• 2.6 Lists
• 3 Descriptive statistics
• 3.1 Univariate descriptive statistics
• 3.1.1 Categorical variables
• 3.1.1.1 Central tendency: the mode
• 3.1.1.2 Dispersion: normalized entropy
• 3.1.1.3 Visualization
• 3.1.2 Ordinal variables
• 3.1.2.1 Central tendency: the median
• 3.1.2.2 Dispersion: quantiles etc.
• 3.1.2.3 Visualization
• 3.1.3 Numeric variables
• 3.1.3.1 Central tendency: arithmetic mean
• 3.1.3.2 Dispersion: standard deviation etc.
• 3.1.3.3 Visualization
• 3.1.3.4 Two frequent transformations
• 3.1.4 Standard errors and confidence intervals
• 3.1.4.1 Standard errors for percentages
• 3.1.4.2 Standard errors for means
• 3.1.4.3 Confidence intervals
• 3.2 Bivariate descriptive statistics
• 3.2.1 Categorical/ordinal as a function of categorical/ordinal variables
• 3.2.2 Categorical/ordinal variables as a function of numeric variables
• 3.2.3 Numeric variables as a function of categorical/ordinal variables
• 3.2.4 Numeric variables as a function of numeric variables
• 3.3 Polemic excursus 1: on ‘correlation’
• 3.4 Polemic excursus 2: on visualization
• 3.5 (Non-polemic) Excursus on programming
• 3.5.1 Conditional expressions
• 3.5.2 On loops
• 3.5.3 On not looping: the `apply` family
• 3.5.4 Function writing
• 3.5.4.1 Anonymous functions
• 3.5.4.2 Named functions
• 4 Monofactorial tests
• 4.1 Distributions and frequencies
• 4.1.1 Goodness-of-fit
• 4.1.1.1 One categorical/ordinal response
• 4.1.1.2 One numeric response
• 4.1.2 Tests for differences/independence
• 4.1.2.1 One categorical response and one categorical predictor (indep.samples)
• 4.1.2.2 One ordinal/numeric response and one categorical predictor (indep. samples)
• 4.2 Dispersion
• 4.2.1 Goodness-of-fit test for one numeric response
• 4.2.2 Test for independence for one numeric response and one categorical predictor
• 4.2.2.1 A small excursus: simulation
• 4.3 Central tendencies
• 4.3.1 Goodness-of-fit tests
• 4.3.1.1 One ordinal response
• 4.3.1.2 One numeric response
• 4.3.2 Tests for differences/independence
• 4.3.2.1 One ordinal response and one categorical predictor (indep. samples)
• 4.3.2.2 One ordinal response and one categorical predictor (dep. samples)
• 4.3.2.3 One numeric response and one categorical predictor (indep. samples)
• 4.3.2.4 One numeric response and one categorical predictor (dep. samples)
• 4.4 Correlation and simple linear regression
• 4.4.1 Ordinal variables
• 4.4.2 Numeric variables
• 4.4.3 Correlation and causality
• 5 Fixed-effects regression modeling
• 5.1 A bit on ‘multifactoriality’
• 5.2 Linear regression
• 5.2.1 A linear model with a numeric predictor
• 5.2.1.1 Numerical exploration
• 5.2.1.2 Graphical model exploration
• 5.2.1.3 Excursus: curvature and `anova`
• 5.2.1.4 Excursus: model frames and model matrices
• 5.2.1.5 Excursus: the 95%-CI of the slope
• 5.2.2 A linear model with a binary predictor
• 5.2.2.1 Numeric model exploration
• 5.2.2.2 Graphical model exploration
• 5.2.2.3 Excursus: coefficients as ‘instructions’
• 5.2.3 A linear model with a categorical predictor
• 5.2.3.1 Numeric model exploration
• 5.2.3.2 Graphical model exploration
• 5.2.3.3 Excursus: conflation, model comparison, and contrasts
• 5.2.4 Towards multifactorial modeling
• 5.2.4.2 Interactions
• 5.2.5 A linear model with two categorical predictors
• 5.2.5.1 Numeric model exploration
• 5.2.5.2 Graphical model exploration
• 5.2.5.3 Excursus: collinearity and VIFs
• 5.2.6 A linear model with a categorical and a numeric predictor
• 5.2.6.1 Numeric model exploration
• 5.2.6.2 Graphical model exploration
• 5.2.6.3 Excursus: post-hoc comparisons and predictions (from `effects`)
• 5.2.7 A linear model with two numeric predictors
• 5.2.7.1 Numeric model exploration
• 5.2.7.2 Graphical model exploration
• 5.2.7.3 Excursus: where are most of the values?
• 5.2.8 Interactions (yes, again)
• 5.3 Binary logistic regression
• 5.3.1 A binary logistic regression with a binary predictor
• 5.3.1.1 Numerical model exploration
• 5.3.1.2 Graphical model exploration
• 5.3.2 A binary logistic regression with a categorical predictor
• 5.3.2.1 Numerical model exploration
• 5.3.2.2 Graphical model exploration
• 5.3.3 A binary logistic regression with a numeric predictor
• 5.3.3.1 Numerical model exploration
• 5.3.3.2 Graphical model exploration
• 5.3.3.3 Excursus: on cut-off points
• 5.3.4 A binary logistic regression with two categorical predictors
• 5.3.4.1 Numerical model exploration
• 5.3.4.2 Graphical model exploration
• 5.3.5 Two more effects plots for you to recreate
• 5.4 Other regression models
• 5.4.1 Multinomial regression
• 5.4.1.1 A multinomial regression with a numeric predictor
• 5.4.1.2 A multinomial regression with a categorical predictor
• 5.4.1.3 Multinomial and binary logistic regression
• 5.4.2 Ordinal logistic regression
• 5.4.2.1 An ordinal regression with a numeric predictor
• 5.4.2.2 An ordinal regression with a categorical predictor
• 5.5 Model formulation (and model selection)
• 5.6 Model assumptions/diagnostics
• 5.6.1 Amount of data
• 5.6.2 Residuals
• 5.6.3 Influential data points
• 5.6.4 Excursus: autocorrelation/time & overdispersion
• 5.7 Model validation (and classification vs. prediction)
• 5.8 A thought experiment
• 6 Mixed-effects regression modeling
• 6.1 A very basic introduction
• 6.1.1 Varying intercepts only
• 6.1.2 Varying slopes only
• 6.1.3 Varying intercepts and slopes (correlated)
• 6.1.4 Varying intercepts and slopes (uncorrelated)
• 6.2 Some general MEM considerations (and revisiting Simpson’s paradox)
• 6.3 Linear mixed-effects modeling case study
• 6.3.1 Preparation and exploration
• 6.3.2 Model fitting and selection
• 6.3.3 Quick excursus on `update`
• 6.3.4 Model diagnostics
• 6.3.5 Model fitting and selection, part 2
• 6.3.6 A brief interlude
• 6.3.7 Model diagnostics, part 2
• 6.3.8 Model interpretation
• 6.3.9 A bit on MEM predictions
• 6.4 Generalized linear mixed-effects modeling case study
• 6.4.1 Preparation and exploration
• 6.4.2 Model fitting and selection
• 6.4.3 Model diagnostics
• 6.4.4 Model interpretation
• 6.5 On convergence and final recommendations
• 7 Tree-based approaches
• 7.1 Trees
• 7.1.1 Classification and regression trees
• 7.1.2 Conditional inference trees
• 7.2 Ensembles of trees: random forests
• 7.2.1 Forests of classification and regression trees
• 7.2.2 Forests of conditional inference trees
• 7.3 Discussion
• 8 References
• 8.1 Packages
• 8.2 Books, papers, …