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Research Methodology and Descriptive Statistics Summary test 1
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Summary of the book of Babbie and the microlectures. Units 1 t/m 11, 23
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Research Methodology and Descriptive Statistics Summary test 2
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Summary units Test 2 Research Methodology and Descriptive statistics (Unit 12,14-22)Unit 12
Axiom/postulate = fundamental assertions, taken to be true, on which a theory is grounded.
Proposition = Specific conclusion, derived from axiomatic groundwork, about the relation among
concepts -> From propositions we can derive hypotheses.
Hypothesis = A specified testable expectation about empirical reality, that follow from a more
general proposition (a statement). -> Research is designed to test hypotheses.
3 main elements in the traditional model of science
(Not accurately anymore for scientific research)
1. Theory -> Hypothesis
2. Operationalization (= one step beyond conceptualization. Process of developing
operational definitions, or specifying the exact measurement operations) -> Operational
definitions
3. Observation -> Confirm or disconfirm hypothesis
Disconfirmability = Possibility of falsification
-> Essential quality in any hypothesis. If there is no chance that the hypothesis can be disconfirmed, it
hasn’t said anything meaningful.
Hypothesis testing = Observations aimed at finding out if a statement is true.
Null hypothesis = Assumption that there is no relationship between two variables in the total
population. A rejected null hypothesis means variables are related.
2 routes to construct social theories:
Both of these routes are based on logic and observation.
1. Deductive reasoning: Theory -> testable hypothesis (p.53)
2. Inductive reasoning: Observations -> search for patterns (p.56)
-> In quantitative study search for relationship between variables.
-> Most qualitative research is oriented toward the inductive approach.
Big data = Extremely large data sets generated through the automatic monitoring of ongoing
processes.
Independent variable = Causes or determines a dependent variable. It is independent of other
variables in the study. Often referred to as ‘x’.
Dependent variable = The effect. It depends on or is caused by another variable.
Often referred to as ‘y’.
Variable x explains y. Researchers are most interested in the dependent variable.
When two dichotomous variables are shown in a cross table, the dependent variable is shown in the
rows, and the independent variable in the columns. The column percentages are computed.
Purposes of social research:
- Exploratory = Mapping out a topic for further study
- Describing = Empirical descriptions
- Explanatory = Providing reasons for phenomena in the form of causal relationships.
, 4 dialects in research approaches:
- Idiographic = Explains one case fully. Idio- means unique, separate -> personally
- Nomothetic = Identify a few causal factors. Explains a class of situations or events rather
than a single (detailed) one.
- Induction = The logical model in which general principles are developed, starting with
observations.
- Deduction = The logical model in which specific expectations of hypotheses are developed,
starting with a theory (general principles).
Example normative question/conceptual question:
Start with normative: Is focussing on sustainability important? Why?
Conceptual question: What do we mean with sustainability? (‘meeting the needs …. Of
future generations to meet their needs’)
RQ: Why are future generations important?
Both questions proceed the empirical research.
Van der Kolk (2017) p. 2-6
Types of statements:
1. Normative (ethics)
2. Positive
- Analytical (true or not true) -> Math, conceptual analysis, etc.
- Synthetic/empirical (may be true or not true) -> Social science, physics, etc.
-> Synthetic/empirical requires social research.
Causal diagram = Shows time order and the direction (or sign) of hypothesis.
+ means more ‘x’ is connected to more ‘y’.
- means more ‘x’ is connected to less ‘y’.
- Look at the time order: First ‘x’ happens, then the effect ‘y’ takes place.
-> Y depends on X.
2 directions (signs) of relationships:
1. Positive linear relationship = Higher values of independent variable(s) lead to higher values
of dependent variable(s).
2. Negative linear relationship = Higher values of independent variable(s) lead to lower values
of dependent variable(s).
In a hypothesis you need to be clear about:
1. Units of analysis
2. Dependent and independent variable(s)
3. Type of these variables (measurement level)
4. Type of relationship (direction/sign)
Bivariate = Only 2 variables. Mostly one dependent and one independent.
Trivariate = Relates 3 variables
Multivariate = Relates more than 3 variables
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