This course provides an overview of quantitative research methods with a particular focus on applications in studies of entrepreneurship and innovation. A basic understanding of quantitative methods, including training in statistics and probability theory, is an expected prerequisite.
Participants are given an opportunity to broaden and deepen their understanding of what tools are available for common research problems, with an emphasis on specification, estimation, interpretation and valuation of various forms of mathematically formulated models. The overall ambition is for course participants to gain a useful overview of how data can be analysed using methods that are used in contemporary state-of-the-art research on entrepreneurship and innovation.
Learning objectives
After having completed the course, participants should gain the ability to:
- identify appropriate quantitative methods in addressing different types of research questions
- make informed methods choices given the available data and the data-generating process
- assess methodological choices in contemporary quantitative research papers
- conduct research on entrepreneurship and innovation using quantitative methods
Course overview
The course is taught in modules. The first four modules are structured around lectures and exercises. In the last third of the course, the content will be prioritized according to participant demand and interests. These ’on demand’ modules are run in seminar form.
Each module contains a combination of lectures and assignment work. Exercises held during the course are designed to help participants develop the necessary expertise for the final, summative assessment. In lectures, the statistical software STATA (latest edition) will be used to illustrate and demonstrate course content. When working with assignments, however, participants may choose to work in R or Python instead.
- What do we need quantitative methods for?
– Cause and effect
– Models as tools
– Mixed methods approaches
– Developing, positioning and publishing quantitative research - Regression methodology: a walkthrough of models, approaches and options
– OLS
– Regression models for binary outcomes (Logit, Probit, LPM)
– Regression models for censored and truncated data
– Regression models for multi-item discrete choice outcomes (multinomial logit, nested logit)
– Pooled Cross-sections and Panel Data Models
– Complementary regression models: survival analysis and duration models, structural equation models, factor analysis - The research survey as a tool in quantitative research
– The use of surveys in innovation and entrepreneurship research
– Opportunities and limitations
– Sampling
– Survey design
– Scale development, question formulation
– Factor analysis - Towards causal analysis
– Granger causality
– Matching
– Sample Selection and Heckman Models
– Endogeneity and Instrumental Variables Estimation
– Difference-in-Difference - Key alternatives to regression
– The language of prediction
– Training/evaluation sample split
– Machine learning techniques
– Configurational approaches (qualitative comparative analysis
COMPULSORY ELEMENTS
Attendance is mandatory for all participants. The Course Director assesses if and how absence may be compensated.
EXAMINATION
Participants are expected to hand in four short assignments, that can be solved using STATA or comparable software (e.g. R studio). The first criteria for finishing the course is to have handed in four acceptable assignments.
The second and final part of the examination takes the form of a 3,000 word research paper, reflecting planned or potential use of quantitative methods in the context of the PhD project. The paper should cover a research question, research design, data, and findings. Participants may use a dataset that they intend to use in their own research.