Courses

Course code and title: IPN 1024 Statistics I, and IPN1121 SPSS I

Coordinator and lecturer: Dr. Trung Dung Tran

Content:
This course consists of two parts. During the first part of the course, students will study the foundations of inferential statistics. A great deal of emphasis will be placed on the logic behind the statistical reasoning process. During the second part of the course, students will be familiarised with several statistical techniques often used in the field: t-tests, ANOVA and X2 tests.  In the parallel SPSS practical, students will be given the opportunity to apply these techniques to several real data sets. The subjects covered in the second part of this course will consistently be linked to the basic terms that were explained in the first part of the course.
The focus is on statistical concepts and techniques that play a role in summarizing and describing observed variables and relationships between variables, as well as generalizing the results for a larger group of people than the observed group. The first theme of this course is to summarize the observed data. The second theme is the testing concept. The third theme pertains to various basic statistical techniques that are used to analyse observed data.

Course code and title: IPN 2028 Statistics II, and IPN 2135 SPSS II

Coordinator and lecturer: Dr. Nick Broers

Contents:

Within psychology, there is a tradition of experimentally oriented research, although quasi-experiments and correlational research also frequently occur. The data to be analysed are often quantitative, such as test scores and response times. The most accepted statistical analysis method for quantitative data from experimental research is analysis of variance (ANOVA), and the most common for correlational research is regression analysis. During this course, students familiarise themselves with the logic and application possibilities of analysis of variance and, to a lesser degree, with regression analysis. Treatment of these topics will build on one-way ANOVA and regression analysis as taught in the first academic year. The guiding principle here is the distinction between within subjects (WS) and between subjects (BS) designs, and the distinction between experimental, quasi-experimental and correlational research.

Course code and title: IPN3008 Statistics III, and IPN 3201 SPSS III

Coordinator and lecturer: Dr. Jan Schepers

Contents:
The goal of this course is twofold. On the one hand, it supplements Statistics II; that is the analysis of two-way designs with a dichotomous instead of quantitative dependent variable. On the other hand, the emphasis lies on the analysis of tests and questionnaires. In this way, this course provides students a solid statistical preparation for the course ‘Psychodiagnostics’. In this statistics course students will study three techniques spanning several weeks: logistic regression, reliability analysis and factor analysis. Logistic regression is the equivalent of ANOVA and regression analysis covered in ‘Statistics II’ if the dependent variable is dichotomous instead of continuous, such as recovery from disease or passing an exam. Reliability analysis is a classical psychometric method for analysing tests and questionnaires. Students receive a training in classical psychometrics and an introduction into modern psychometrics (the Rasch model), validity, and agreement between evaluators. Factor analysis is a method used to reduce a multitude of variables to a small number of underlying factors, e.g. to reduce the scores on various cognitive tests to few underlying dimensions such as verbal and spatial intelligence, or to reduce the many items in a personality questionnaire to a few sub-scales.

Course code and title: PSY4162 Applied Statistics I

Coordinator: dr. Jan Schepers
Lecturers: dr. Jan Schepers, dr. Nick Broers, prof. dr. Gerard van Breukelen

Content: 

The course consists of eight modules/units. The first four units (period 1) cover the following standard statistical methods: factorial ANOVA for between-subject designs, multiple linear regression, analysis of covariance (ANCOVA), multivariate ANOVA (MANOVA) and discriminant analysis. Students are assumed to have background knowledge of balanced two-way factorial ANOVA and multiple regression. These methods will be briefly reviewed. The following more advanced topics will then be covered: unbalanced factorial designs, contrast analysis, interaction, simple slope analysis, dummy coding, centering covariates, different coding schemes, collinearity and residuals checks and data transformations.

The last four units (period 2) cover classical repeated measures ANOVA for the one- and two-way within-subject design and the split-plot (between x within) design (units 5 and 6) and mixed (multilevel) regression for longitudinal studies (units 7 and 8). Special attention is given to: a) the choice between multivariate and univariate data formats and method of analysis, and the sphericity assumption; b) the distinction between the within-subjects and between-subjects part of a split-plot ANOVA, and how to obtain both using regression analysis; c) marginal models for repeated measures as an alternative to repeated measures ANOVA in cases of missing data and/or of within-subject covariates; d) the pros and cons of various models for the correlational structure of repeated measures, such as compound symmetry and AR1; e) the random intercept model for repeated measures as a method to include individual effects into models for longitudinal data (growth curves) or single trial analyses of lab data (response times, ERP, fMRI); f) combining the former with e.g. ARMA modelling to distinguish between inter-personal and intra-personal outcome variation; and g) random slope models for longitudinal data (individual differences in change over time) and single trial analysis (individual differences in stimulus effects).

Course code and title: PSY4163 Applied Statistics II

Coordinator: dr. Jan Schepers
Lecturers: prof. dr. Gerard van Breukelen, dr. Nick Broers

Content:

Theme 1 (Period 5)

Course lecturer: Gerard van Breukelen

Sample size calculation and nested designs: This course provides an introduction to sample size/power calculation for elementary and often encountered research designs in psychology and neuroscience. First, sample size calculation is explained and practiced for comparing two independent samples (e.g. parallel groups or between-subject design) and for comparing two dependent samples (e.g. crossover or within-subject design) on a quantitative dependent variable (outcome). Subsequently, this is extended to a) correlation between two quantitative variables, b) the comparison of two groups on a binary outcome, and c) two-way factorial designs (BS*BS, WS*WS, BS*WS). The opposite effects of a covariate on the sample size needed in randomized and nonrandomized studies are also explained and practiced. Finally, the data analysis and sample size calculation are covered for some popular nested designs, specifically cluster randomized trials and multicenter/multisite trials. Sample size calculations will be done with GPower and possibly some free software for nested designs, and with pencil-and-paper assignments.

Theme 2 (Period 6)

Course lecturer: Nick Broers

Structural equation modeling: Structural equation modeling (SEM) is an advanced multivariate method that is gaining importance in psychology but still requires special software (such as Lisrel, EQS, AMOS or Mplus). SEM is introduced in two units, starting with causal modelling and mediation analysis in cross-sectional research and then extending to longitudinal research and latent variables (factors). Special attention is given to identifying models, model equivalence, global and local goodness of fit indices, parsimony, model modification and cross- validation. Some concepts from matrix algebra are needed for SEM, and these will be briefly discussed without going into technical detail.

Course code and title: PSY4164 Applied Statistics II

Coordinator: dr. Jan Schepers
Lecturers: prof. dr. Gerard van Breukelen, dr. Jan Schepers

Content:

Theme 1 (Period 5)

Course lecturer: Gerard van Breukelen

Sample size calculation and nested designs: This course provides an introduction to sample size/power calculation for elementary and often encountered research designs in psychology and neuroscience. First, sample size calculation is explained and practiced for comparing two independent samples (e.g. parallel groups or between-subject design) and for comparing two dependent samples (e.g. crossover or within-subject design) on a quantitative dependent variable (outcome). Subsequently, this is extended to a) correlation between two quantitative variables, b) the comparison of two groups on a binary outcome, and c) two-way factorial designs (BS*BS, WS*WS, BS*WS). The opposite effects of a covariate on the sample size needed in randomized and nonrandomized studies are also explained and practiced. Finally, the data analysis and sample size calculation are covered for some popular nested designs, specifically cluster randomized trials and multicenter/multisite trials. Sample size calculations will be done with GPower and possibly some free software for nested designs, and with pencil-and-paper assignments.

Theme 3 (Period 6)

Course Lecturer: Jan Schepers

Resampling methods in statistics: Many modern statistical analyses make use of resampling methods in applications where theoretical statistics cannot readily provide answers for making statistical inferences from the data at hand. This elective provides an introduction to three important resampling methods, bootstrapping, permutation testing and cross- validation, for obtaining measures of accuracy for parameters of a model or for studying model fit. The methods will be practiced using the software R.

Course code and title: PSY4165 Applied Statistics II

Coordinator: dr. Jan Schepers
Lecturers: dr. Nick Broers, dr. Jan Schepers

Content:

Theme 2 (Period 6)

Course lecturer: Nick Broers

Structural equation modeling: Structural equation modeling (SEM) is an advanced multivariate method that is gaining importance in psychology but still requires special software (such as Lisrel, EQS, AMOS or Mplus). SEM is introduced in two units, starting with causal modelling and mediation analysis in cross-sectional research and then extending to longitudinal research and latent variables (factors). Special attention is given to identifying models, model equivalence, global and local goodness of fit indices, parsimony, model modification and cross- validation. Some concepts from matrix algebra are needed for SEM, and these will be briefly discussed without going into technical detail.

Theme 3 (Period 6)

Course Lecturer: Jan Schepers

Resampling methods in statistics: Many modern statistical analyses make use of resampling methods in applications where theoretical statistics cannot readily provide answers for making statistical inferences from the data at hand. This elective provides an introduction to three important resampling methods, bootstrapping, permutation testing and cross- validation, for obtaining measures of accuracy for parameters of a model or for studying model fit. The methods will be practiced using the software R.

Course code and title: PSY4617 Applied Statistics

Coordinator: Dr. Nick Broers

lecturers: dr. Nick Broers, dr. Wolfgang Viechtbauer (FHML), prof. dr. Gerard van Breukelen

Contents:

The course consists of six units. The first unit will focus on a review of multiple linear and logistic regression analysis, which will form the basis for most of the advanced techniques that will be covered in the remainder of this course. This general introduction into regression techniques is followed by a unit that focuses on ROC curve analysis. ROC curves are becoming increasingly more important to forensic psychologists, for instance, to help find optimal cut-off scores for instruments that should help decide on whether an institutionalized offender can be granted parole or leave, or for studying whether verbal veracity assessment tools can discriminate between truth or falsehood of incriminating or exculpatory statements. In studies on the accuracy of identification of crime suspects, a comparison of ROC curves can be used to decide which of several line-up procedures is superior in terms of maximizing correct and minimizing false identification rates. The third unit covers meta-analysis.

The last three units are devoted to mixed (multilevel) regression for nested designs and longitudinal studies. This mixed regression starts with a unit on marginal models for repeated measures (for instance, a time series of observations on institutionalized offenders receiving specialized treatment). Especially in cases of missing data or within-subject covariates, such models are known to be more efficient than traditional techniques such as repeated measures ANOVA. In this first of three units on multilevel regression, students are shown the pros and cons of various models for the correlational structure of repeated measures, such as compound symmetry and AR1. The second unit covers the random intercept model for repeated measures as a method to include individual effects in marginal models for longitudinal data (growth curves) or single trial analyses of lab data. Students learn how this can be combined with e.g. ARMA modelling to distinguish between interpersonal and intrapersonal outcome variation. The random intercept model will also be applied to a cluster randomised trial - i.e. an RCT where organisations, like institutions treating justice-involved adolescents, are randomised. The third and last unit on mixed regression covers random slope models for longitudinal data (individual differences in change over time), single trial analysis (individual differences in stimulus effects) and multicentre trials (RCT within each of a number of organisations).