Samenvatting
Samenvatting part 1 - Research Methodology and Descriptive Statistics ()
- Vak
- (202001402)
- Instelling
- Universiteit Twente (UT)
Een samenvatting/uitleg van alle key terms die voor part 1 van het tentamen zijn behandeld.
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Voorbeeld 2 van de 7 pagina's
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Voorbeeld van de inhoud
Key termsUnit 1: What is empirical research?
Research question
Design and (cycle of) decision-making
Problem & need analysis
Ex ante evaluation: Ex-ante is used most commonly in the commercial world, where results of a
particular action, or series of actions, are forecast (or intended)
Process evaluation
Ex post evaluation (effect / impact research): The ex post evaluation is a summative evaluation
conducted after the completion of policy interventions to demonstrate the achievements of policy
objectives, policy impacts and results to stakeholders and general public, increase the policy
transparency and to learn how to do policy better in the future.
Wheel of science / empirical cycle
Deduction: From big to smaller (starting with theory and see if there is an observation)
Induction: From small to bigger (starting from an observation and see if there is a theory)
Confirmation bias
Unit 2: What are clear research questions?
Research question
Normative: Normative research is about measuring, assessing and comparing goodness, value,
practicality and functionality. As 'normative' proposes it is about setting standards for behaviour by
setting norm by explicitly defining possible, desired or feasible way to act.
Conceptual: Conceptual research is defined as a methodology wherein research is conducted by
observing and analyzing already present information on a given topic
Empirical: Empirical research is research that is based on observation and measurement of
phenomena, as directly experienced by the researcher. The data thus gathered may be compared
against a theory or hypothesis, but the results are still based on real life experience.
Explanatory: Explanatory research is a research method used to investigate how or why something
occurs when only a small amount of information is available pertaining to that topic. It can help you
increase your understanding of a given topic.
Descriptive: Descriptive research is also used to compare how different demographics respond to
certain variables. For example, an organization may study how people with different income levels
react to the launch of a new Apple phone.
Exploratory research: Exploratory research investigates research questions that have not been
studied in depth. The preliminary results often lay the groundwork for future analysis. Explanatory
research questions tend to start with “why” or “how”, and the goal is to explain why or how a
previously studied phenomenon takes place.
Unit (of analysis): Who or what you want to know more of.
Variable (attributes / values): What you want to know from the unit of analysis.
Setting
, Unit 3: What are data?
Unit(s) of analysis
Unit(s) of observation: Who or what are you investigating to know more of the unit of
analysis.
Ecological fallacy: Ecological fallacies occur when we try to draw conclusions about
individuals based on data collected at the group level. For example, if a specific
neighborhood has a high crime rate, one might assume that any resident living in that
area is more likely to commit a crime.
Mutually exclusive: Only one answer is possible
Exhaustive / Complete: Every answer can be given
Dichotomy (dummy variable): Two options
Nominal measure: non-numeric, no order (eye color)
Ordinal measure: Numeric, not ordered (rankings)
Interval measure: Numeric, ordered, not a clear zero point (Celsius)
Ratio measure: Numeric, ordered, clear zero point (speed)
Codebook
Unit 4: Handling data with software – R
Statistical software
R
Rstudio
Global environment (linksboven)
Standard operators
Function
Object
Left assignment operator
Dataframe
Package (tidyverse f.e.)
Pipe operator (%>%)
Working directory
Unit 5: Conceptualizing constructs
Construct / concept / term: Theoretical concept, theme, or idea based on empirical
opservations
Dimension / facet / trait: Example: Dimensions of a personality (not measurable in one
question)
Conceptualization: Explaining what the concept exactly means
Operationalization: Explaining how to measure the different parts (facets) of a concept (incl
how people can answer)
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