College aantekeningen
Notes Designing Social Research
These are my notes from the lectures of Designing Social Research, I also added pictures for more clarity
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Notes Designing Social ResearchLecture 1 – What is science?
Characteristics of scientific research
Science: systematic exploration, testing and validation of knowledge.
Empirical reality: empirical is real, accumulation of knowledge by going off of what we
already know: standing on shoulders of giants.
Way of working in scientific research:
1. Theory – logic explanation or prediction
2. Data collection – observation in a systematic way (methodology)
3. Data analysis – comparing what is logically expected with what is actually observed
Principle of falsification: all knowledge is uncertain; explanations are true until they are
refuted (Karl Popper).
- Finding a counterexample to test knowledge.
Spurious relationship: coincidence, false/fake relationship.
- You have to look for logical relationships.
The cycle of research
1. Problem: topic that is being studied.
2. Observation
3. Inductive research: there is little or no knowledge on the problem, observing reality
to learn and to gather facts and to learn patterns.
a. From specific observations (=empirical research) to the discovery of a pattern
among all the given events (=axiom, theory building).
4. Axioms: descriptions, statements, the accepted truth.
5. Theory: existing ideas.
6. Hypothesis: prediction of the outcome.
7. Deductive research: using existing theories on the topic to predict the outcome of
the research.
a. From a pattern that is logically expected (=hypothesis, based on theory) to
observations (=empirical research) that test whether the expected pattern
occurs.
8. Testing: testing the predictions.
Theory and variables, conceptual model
Scientific theory: an interconnected, coherent system of premises which aim to describe,
explain or predict certain phenomena.
3 elements:
- Assumptions: basic ideas about nature of mankind (e.g. rational).
- Model: variables and relations.
o Variable: the external expression of the phenomenon that you are studying
(e.g. what someone looks like).
- Hypotheses: predictions, to be tested.
,Example: public service motivation
Public service motivation: an individual’s predisposition to respond to motives grounded
primarily or uniquely in public institutions and organisations (Perry & Wise, 1990)
4 motives:
- A deep desire to make a difference.
- An ability to have an impact on public affairs.
- A sense of responsibility and integrity.
- A reliance on intrinsic rewards as opposed to salary or job security.
Assumption: people are driven by motives (nature of mankind).
Model:
- Latent variable: PSM (= sum score, scale or index).
- 4 dimensions: 4 motives (each dimension is a variable in its own).
Hypothesis: predicts under which conditions the independent variable (X) will have a
directed effect on the dependent variable (Y).
- Applied to PSM:
o People with higher PSM will work more often in the public sector.
o Extrinsic rewards (money) will be appreciated less by people with higher PSM
than people with lower PSM (because of intrinsic motivation = mechanism).
PSM: independent variable X.
Work in public sector (+) and extrinsic rewards (-): dependent variable Y.
Variables and attributes:
Dependent variable is influenced by the independent variable: X Y
Variables can be expressed:
- In numbers (quantitative), for example: age in years, or a score on a Likert scale (5
points: disagree – agree)
- Non-numerically (qualitative), for example: opinions, intangible variables that do not
add up (e.g., political parties that you voted for)
Quantitative variables have different measurement levels, which has implications for
analysis and use of statistics.
Example: use of theory and variables
Which 4 variables are taken from the theory on sushi to predict this?
- The food needs to be part of a cuisine or culture.
- The food needs to be accessible.
- The food needs to be cheap.
- The food needs to be tasty.
, Problem definition and hypothesis
Problem definition: start of every research
- Aim: to acquire more knowledge (but what kind of knowledge?: test something,
describe something, solve something)
- Question: topic of research
o Main or central question
o Subquestions
The hierarchy of knowledge aims (less important to most important):
- Exploration: what happened in this case?
- Descriptive: what characteristics does this phenomenon have?
- Explanatory: why did this happen?
- Testing: did this phenomenon occur more often since a certain date?
- Diagnosing: which factors lead to more success or failure in this situation?
- Design/prescriptive: how can this problem be solved?
- Evaluative: how should the implementation of this change be evaluated?
Counterfactual: what would have happened if?
Aim:
- Choose the highest aim possible for your topic, based on the current state of
knowledge (hence you must look into scientific literature first).
- Don’t use words like ‘research, gain insight, or investigate’ because they are too
generic. Use the aims from the table.
Mistakes often made in the main question:
- Question does not fit with the aim of the research.
- Too many questions in one.
- Imprecise formulation.
- Too generalised questions (limit to what you study).
Subquestions:
- How many? No fixed rules but dependent upon:
o The number of variables (more variables = more subquestions)
o The research aim (higher aim = more subquestions).
- Criterion of parsimonity: no more or less subquestions than needed to answer the
main question.
- Subquestions give structure to research, but subquestions do not necessarily give
structure to the report, and do not necessarily have to be answered consecutively.
- Usually, there are theoretical and empirical subquestions.
Lecture 2 – Measurement
Operationalization: what am I going to study?
- What do the variables look like in reality?
- Make theoretical variables measurable (observable in empirical reality).
- Three steps:
o Definition (given in theory)
o Choosing indicators (operationalizations) for variables: dependent,
independent, control variables (variables you include because they might play
a role, they are not the variables you are interested in)
o Determine the values of the indicators (quantitative or qualitative)
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