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Summary Quantitative Research Methods (QRM) - ALL LECTURES & LITERATURE
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Indíya BruinsmaPremaster DBI 20-21
Summary Quantitative Research Methods
Module 1
Quantitative research methods use of statistical methods for estimating economic
relationships, testing economic theories, and evaluating and implementing government
and business policy.
Counterfactual what would have happened in the absence of an intervention (e.g.
corona).
Chapter 1.1
Belief bias effect in logical reasoning tendency to be influenced by the believability of
the conclusion even when we shouldn’t, does the conclusion feel true or false.
Simpson’s paradox refers to a phenomena whereby association between a pair of
variables (X, Y) reverses sign upon conditioning of a third variable, Z, regardless of the
value taken by Z.
9 e.g. when aggregated data shows a different situation (e.g. gender bias in college
applications) then disaggregated data (e.g. higher success rate for female applicants).
Social sciences focus on analyzing nonexperimental (observational) data:
Not accumulated through controlled experiments on individuals, firms, or
segments of the economy.
Experimental data collected in laboratory environments in natural sciences,
difficult to obtain in social sciences.
Often impossible, prohibitively expensive, or morally repugnant to conduct kinds
of controlled experiments needed to address economic issues.
When do we use quantitative research?
Descriptive – what? Inferential / relational – why?
Interested in quantitative answer Test relationships
Interested in numerical change Explain something
Characteristics of quantitative research:
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The research process:
Step 1: Research question
1. Exploratory / descriptive
2. Explanatory
Step 2:
1. Literature review gives you an overview of the research that has been conducted
in a field and helps to establish what knowledge is lacking.
9 Find theories identifies important variables, for what reasons they are
important, how they are interrelated and why, identifies conditions under which they
are (not) related.
2. Variables – e.g. impact of education on wages:
Wage = dependent variable
Education = independent / explanatory variable
Age, gender, job type, … = control variables
Control variables: variables that are likely to be correlated with the independent
and the dependent variables. Hence, you need to control for their impact.
3. Stating relationships hypotheses define expected relationships between
variables & test theory life is more complex than simple X Y relationships:
Moderator: influences relationship between the IV and DV by strengthening or
weakening it.
9 The combined effect of two variables on another is known as moderation, and in
statistical terms as an interaction effect.
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