Valid from: Spring 2021
Decided by: Gudbjörg Erlingsdóttir
Date of establishment: 2021-03-02
Division: Traffic and Roads
Course type: Third-cycle course
Teaching language: English
The aim of the course is to aid the participants in getting a holistic view on theory of science and research process, as well as to provide advanced knowledge of quantitative methods for data analysis and an introduction of qualitative analysis. The course has an applied character with clear focus on problems common for the domain of transportation research that can be approached with a quantitative method. Besides the theoretical knowledge, the course participants will acquire practical experience in handling common software tools (Access, Excel, R, SAS, SPSS, SQL) and data formats and will learn to make data screening and data quality checks, and to calibrate regression models.
Knowledge and Understanding
For a passing grade the doctoral student must
Competences and Skills
For a passing grade the doctoral student must
Judgement and Approach
For a passing grade the doctoral student must
- Week 1. Theory of Science: • Methodology • Ontology • Epistemology • Hermeneutics • Positivism • Theoretical underpinnings of research - Week 2. Research methods and experiment setup: • Qualitative vs. quantitative methods • Controlled experiments vs. observational studies • Cross-sectional vs. before/after analysis - Week 3. Qualitative tools: • Interviews • Observations • Text analysis • Discourse analysis - Week 4. Quantitative tools#1: • Descriptive statistics • Data exploration and cleaning • Excel, Access, SQL - Week 5. Quantitative tools#2: • Interval estimation, Hypothesis testing, population comparisons • SPSS - Week 6. Quantitative tools#3: • Continuous variable models • Linear regression • Panel data analysis • Latent variable models • Time series • SAS - Week 7. Quantitative tools#4: • Discrete variable models • Logistic regression • Count data models • Ordered probability models • R - Week 8. Other statistical methods: • Random parameter models • Latent class models • Bivariate and multivariate dependent variable models • Bayesian statistical methods • Association rules • Classification trees - Weeks 9-10. Project work (based on own data and topic): • Formulate research question • Explore data/formulate assumptions • Choose the right method (qualitative or quantitative) that fit data and research question • Apply the method and get results • Explain results and their limitations
Types of instruction: Lectures, seminars, exercises, project. In the beginning of each theme, the participants receive instructions and reading materials, as well as practical exercises in which the new knowledge is applied. This is followed by an interactive session with the tutor in which the theoretical and exercise material are discussed. Some themes include also practical training in use of software tools. In the final assignment, the participants will apply the knowledge obtained in the course on their own research topics and datasets. They will formulate a research question, choose and motivate the method and tool to address it, perform the analysis and discuss its quality, reliability and limitations.
Examination formats: Oral exam, written assignments.
To get approved on the course, presence on all class meetings required and all home exams must be “passed”.
Grading scale: Failed, pass
Examiner:
Admission requirements: no special requirements
Assumed prior knowledge: Basic statistic and informatic knowledge
Selection criteria: PhD students in Transportation Engineering or related field
Course coordinator: Carmelo D'agostino <carmelo.dagostino@tft.lth.se>