TERMS FOR EXAM RMDS
Lecture 1 | Unit 1 | Babbie Ch. 1 p. 5-14
Empirical research question: are answered through observing.
Design and (cycle of) decision-making:
1. Problem & need analysis: causes of the problem, consequences of the problem, how
big is the problem?
2. Find & design options (which options have been used by others?)
3. Ex ante options evaluation: is research to check whether possible solutions might
actually work (so before making a decision). We compare the options that we have.
4. Choice (make a choice in options and we pick one as our solution)
5. Implementation/process evaluation: did it go as planned? Checking whether the
solution was implemented (why (not)).
6. Ex post evaluation/Outcome evaluation (effect / impact research): did the
implementation of the chosen option work out the way as planned and intended?
Wheel of science / empirical cycle: question à theory à research design à data collection
à data analysis à answers/knowledge à question à etc.
• Not logical.
Deduction: from the general to the specific. It moves from a pattern that might be logically
or theoretically to observations that test whether the expected pattern actually occurs.
• From ‘why’ to ‘whether’.
• Specific expectations are developed on the basis of general principles.
• Swans are white. Let’s go to 3 parks to see the swans. You can not focus on the
population. You focus a small group.
Induction: from a set of observations to the discovery of a pattern. General principles are
developed from specific observations.
• Example: Swans are white. A lot of swans are white. Are all swans are white?
• From ‘whether’ to ‘why’.
Confirmation bias: we only see what we want to see à why we have procedure in empirical
research. With a good research design you can make sure it doesn’t happen.
Lecture 2 | Unit 2 | Babbie, Ch. 1 p. 14-28, Ch. 4 p. 89-93
Normative: what should be the case?
Empirical: a question that can be answered with observation.
• Explanatory: answers questions of why. Cause/effect.
o One variable is (mostly) cause and other effect.
• Descriptive: answers questions of what, where, when and how.
Describe/explanation.
Conceptual: what is the meaning of the case? What is the meaning of the word?
• Start of research
, Unit (of analysis): who or what being studied/who or what we are comparing to each other.
Most typical units of analysis are individual people.
• Units of observation: units we get the information from.
• Units of analysis: units we are interested in.
Variable (attributes / values): logical sets of attributes.
• The variable sex is made up of the attributes male and female.
• Attributes: are characteristics or qualities that describe an object. Example: female,
Asian, conservative, intelligent.
• Attributes are the categories that make up a variable.
Setting: Europe à The Netherlands à Enschede
• Narrow research down.
Lecture 3 | Unit 3 | Babbie p. 97- 105, pp. 139-143
Mutually exclusive: make sure that the person can answer that question within one answer.
Exhaustive / Complete: it should include all possible answers. Can everybody give an
answer?
Dichotomy (dummy variable): variable whose attributes only had two variables, but not
ordered.
Nominal measure: variables whose attributes are different from ONE another. More than
two but not ordered.
• Examples: gender, birthplace, religious affiliation.
• Functional department of an organization.
Ordinal measure: variables with attributes we can logically rank order.
• Examples: social class, conservatism, job satisfaction, educational level.
• More or less; first, second, etc; high, medium, low; maten S,M,L,XL.
• There is an order, but the distance between them is unknown.
Interval measure: rank ordered variable + equal distances between attributes.
• No zero point.
• Example: iQ and temperature. The distance between 17 and 18 degrees is the same
as that between 89 and 90.
• Comparing people in terms of an interval variable: they are different from each other
(nominal) and one is more than the other (ordinal). We can say ‘how much’ more.
Ratio measure: a variable with attributes that are nominal, ordinal and interval + a true zero
point.
• Example: age, Kelvin temperature scale, income, number of times married, number
of times attending religious services during a particular period of time.
Lecture 1 | Unit 1 | Babbie Ch. 1 p. 5-14
Empirical research question: are answered through observing.
Design and (cycle of) decision-making:
1. Problem & need analysis: causes of the problem, consequences of the problem, how
big is the problem?
2. Find & design options (which options have been used by others?)
3. Ex ante options evaluation: is research to check whether possible solutions might
actually work (so before making a decision). We compare the options that we have.
4. Choice (make a choice in options and we pick one as our solution)
5. Implementation/process evaluation: did it go as planned? Checking whether the
solution was implemented (why (not)).
6. Ex post evaluation/Outcome evaluation (effect / impact research): did the
implementation of the chosen option work out the way as planned and intended?
Wheel of science / empirical cycle: question à theory à research design à data collection
à data analysis à answers/knowledge à question à etc.
• Not logical.
Deduction: from the general to the specific. It moves from a pattern that might be logically
or theoretically to observations that test whether the expected pattern actually occurs.
• From ‘why’ to ‘whether’.
• Specific expectations are developed on the basis of general principles.
• Swans are white. Let’s go to 3 parks to see the swans. You can not focus on the
population. You focus a small group.
Induction: from a set of observations to the discovery of a pattern. General principles are
developed from specific observations.
• Example: Swans are white. A lot of swans are white. Are all swans are white?
• From ‘whether’ to ‘why’.
Confirmation bias: we only see what we want to see à why we have procedure in empirical
research. With a good research design you can make sure it doesn’t happen.
Lecture 2 | Unit 2 | Babbie, Ch. 1 p. 14-28, Ch. 4 p. 89-93
Normative: what should be the case?
Empirical: a question that can be answered with observation.
• Explanatory: answers questions of why. Cause/effect.
o One variable is (mostly) cause and other effect.
• Descriptive: answers questions of what, where, when and how.
Describe/explanation.
Conceptual: what is the meaning of the case? What is the meaning of the word?
• Start of research
, Unit (of analysis): who or what being studied/who or what we are comparing to each other.
Most typical units of analysis are individual people.
• Units of observation: units we get the information from.
• Units of analysis: units we are interested in.
Variable (attributes / values): logical sets of attributes.
• The variable sex is made up of the attributes male and female.
• Attributes: are characteristics or qualities that describe an object. Example: female,
Asian, conservative, intelligent.
• Attributes are the categories that make up a variable.
Setting: Europe à The Netherlands à Enschede
• Narrow research down.
Lecture 3 | Unit 3 | Babbie p. 97- 105, pp. 139-143
Mutually exclusive: make sure that the person can answer that question within one answer.
Exhaustive / Complete: it should include all possible answers. Can everybody give an
answer?
Dichotomy (dummy variable): variable whose attributes only had two variables, but not
ordered.
Nominal measure: variables whose attributes are different from ONE another. More than
two but not ordered.
• Examples: gender, birthplace, religious affiliation.
• Functional department of an organization.
Ordinal measure: variables with attributes we can logically rank order.
• Examples: social class, conservatism, job satisfaction, educational level.
• More or less; first, second, etc; high, medium, low; maten S,M,L,XL.
• There is an order, but the distance between them is unknown.
Interval measure: rank ordered variable + equal distances between attributes.
• No zero point.
• Example: iQ and temperature. The distance between 17 and 18 degrees is the same
as that between 89 and 90.
• Comparing people in terms of an interval variable: they are different from each other
(nominal) and one is more than the other (ordinal). We can say ‘how much’ more.
Ratio measure: a variable with attributes that are nominal, ordinal and interval + a true zero
point.
• Example: age, Kelvin temperature scale, income, number of times married, number
of times attending religious services during a particular period of time.
