Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course VTV025f valid from Spring 2021

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  • English
  • Every other spring semester
  • 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.
  • - 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
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • Understand the fundamental concepts of theory of science and research process;
    Have theoretical knowledge of qualitative and quantitative methods for data analysis used in transportation research;
    Understand, based on the target of the investigation, which are the appropriate tools and methods, including the limits of their applicability;
    Have a clear picture of the applicability of data analysis and regression modelling in transport research and its multidisciplinary character;
    Interpret how the quality of data and method can affect reliability of the analysis.
Competences and Skills
  • For a passing grade the doctoral student must
  • Choose and apply qualitative/quantitative tools;
    Have a qualified communication with scientists of different disciplines other than the transport area, in the field of qualitative/quantitative methods, in order to maximize the research output of their work;
    Form an independent, qualified and modulated opinion on own research question (based on objective data);
    Be able to handle and learn further the most common software tools for data handling and analysis and data formats.
    Formulate a research question in a form that can be practically answered and which data and tools are necessary to answer it;
Judgement and Approach
  • For a passing grade the doctoral student must
  • Be able to critically analyse data in order to create added value and produce new knowledge;
    Be able to motivate the choice of method(s) most suitable for the given research question as well as discuss its drawbacks and limitations;
    Demonstrate an in-depth insight and ability to discuss how the quality of data and parameters of the analysis method can affects reliability of scientific research and quality of the outcome;
    Relate own research to broader context of research, methods and practice within the topic.
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”.
  • Failed, pass
Admission Requirements
  • no special requirements
Assumed Prior Knowledge
  • Basic statistic and informatic knowledge
Selection Criteria
  • PhD students in Transportation Engineering or related field
  • Washington, S., Karlaftis, Matthew G., Mannering, F. & Anastasopoulos, P.: Statistical and Econometric Methods for Transportation Data Analysis. Chapman and Hall/CRC, 2020. ISBN 9780367199029.
    Skoldberg, K.: Reflexive Methodology - New Vistas for Qualitative Research. SAGE Publishing, London, 2017. ISBN 9781473964242.
    Andersson, Ö.: Experiment! Planning, Implementing and Interpreting. Wiley, 2012. ISBN 9780470688250.
    Hennik, M., Hutter, I. & Bailey, A.: Qualitative research methods. SAGE Publishing, London, 2020. ISBN 9781412922265.
Further Information
Course code
  • VTV025f
Administrative Information
  • 2021-03-02
  • Gudbjörg Erlingsdóttir

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published
2021‑04‑20 2021‑06‑29

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