TU Delft
Year
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NEDERLANDSENGLISH
Organization
2015/2016 Electrical Engineering, Mathematics and Computer Science Master Computer Science
IN4304
Empirical Research Methods
ECTS: 5
Responsible Instructor
Name E-mail
Dr.ir. W.P. Brinkman    W.P.Brinkman@tudelft.nl
Contact Hours / Week x/x/x/x
0/0/2/2
Education Period
3
4
Start Education
3
Exam Period
4
5
Course Language
English
Course Contents
The course focuses on two important elements of data science: data collection (e.g. setting up surveys and experiments) and data analysis (i.e. statistical analysis).

The main topics of study considered in light of the above learning outcomes are:
Research philosophy (e.g. positivism, empiricism, naturalism)
Formulating empirical research questions and conceptual research models
Causality effects and relationships
Validity and Reliability
Scales of measurement (e.g. nominal, ordinal, interval, ratio)
Sampling methods (e.g. experiment, survey, observations) and measure instruments (e.g. Likert scales, semantic differential, event versus time sampling)
Experimental design (e.g. within and between-subjects, factorial design, counter-balancing, Latin square)
Biases in empirical research approaches (e.g. confounding variables, statistical power)
Data preparation (e.g. standardization of data, reliability analysis, Inter-rater reliability)
Hypothesis testing, t-test, (M)ANOVA, correlation, regression analysis, logistic regression, post-hoc testing
Non-parametric approaches to data analysis
Study Goals
MAIN AIMS OF THE MODULE

To achieve understanding of empirical research methods and obtain practical experience with quantitative data analysis methods.

LEARNING OUTCOMES FOR THE MODULE

In providing the opportunity for students to develop and demonstrate understanding, knowledge and competence, the learning outcomes for the module are that students will be able to:

1. Recognise and begin to utilise appropriate strategies for carrying out empirical research for answering research questions
2. Appreciate how empirical research is conducted and findings can be evaluated
3. Understand key principles underlying statistical data analysis
4. Develop and apply appropriate research strategy and measure instruments
5. Successfully use statistical software tools to analyse data

Education Method
In the lectures, theories, principles and methods are presented and discussed. During the lectures class-demonstrations will be given on how statistical application such as SPSS or R can be used to analyse empirical data. In the practicum students work in small groups (2 to 3 students) on assignments and discuss them with an instructor. The instructors will also provide practical guidance on the use of SPSS and R.
Literature and Study Materials
Will be announced on blackboard
Books
Robson, C., (2002) Real world research: A resource for social scientists and practitioner-researchers (2nd or 3rd ed). Malden: MA, Blackwell.
Assessment
The module is assessed by coursework and an exam as follows:
(70%) Written Exam
(30%) Coursework Project (resulting in a report)