RESEARCH
CH. 1 SCEINCE AND SOCIAL RESEARCH
 A scientific assertion must have both: logical and empirical support
 Epistemology: The science of knowing; System of knowledge
 Methology: The science of finding out; procedures of scientific
investigation
Agreement reality: “The things everybody knows”; Things we know as part and
parcel of the culture we share with those around us – Tradition and authority –
Errors in casual human inquiry:
 Tradition: Each of us inherits a culture made up of knowledge and values
 Authority: New knowledge appears every day, but you just belief in those,
who have a big authority, e.g. scientists or Docents
 Perhaps they push us in the wrong direction in our own inquiry
 Inaccurate Observation
Different than in scientific observation: no conscious plan
 Overgeneralisation
Replication: Repeating a research study to test and either confirm or
question the findings of an earlier study
 Selective Observation
 Illogical Reasoning
 Logical reasoning is a conscious activity for scientists
The Foundation of Social Science: “What is and Why?”
 3 major aspects: Theory, Data collection & Data analysis
Theory: A systematic explanation for the observations that relate to a particular
aspect of life, e.g. political revolution  providing systematic explanations
Data collection: deals with the observational aspect
Data analysis: looks for pattern in observation & compares what is logical with what
is observed
Attributes: Characteristics/Descriptions of people or things
Variable: a logical set of attributes, e.g. the variable SEX is made up of the attributes
MALE and FEMALE  Social research: Study of variables and their relationships
Independent variable: determines, explains the dependent variable  Cause
Dependent variable: Result, outcome
Idiographic and Nomothetic Explanation
 Why did you choose UTWENTE and not UNI COLOGNE?
Idiographic: (unique, separate) Attention to explain one case fully
 I have more friends in Enschede, I like the Campus there, It is not as far away as Cologne,…
Nomothetic: seeks to explain a class of situations economically, using only one or
just a few explanatory factors; it speaks implicit of the relationship between
variables
 Students living In Enschede within a distance of 50 miles will relatively be more inclined to
study at the Utwente than Students living farther away (Just a hyphothesis!!)
Inductive and deductive Theory/Explanations
Induction/inductive: general principles are developed from specific observations;
moves from a set of specific observations to the discovery of a pattern
 “Whether”/Testing  “Why”/Theory
Deductive: specific expectations of hypothesis are developed on the basis of general
principles
 “Why”/Theory  Hypothesis  “Whether”/Testing
Like a circle
Determinism vs. Agency (free will)
Question whether humans are determined by their particular environment or
whether they feel and act out of their personal choice
Tolerance for ambiguity: The ability to hold conflicting ideas in your mind
simultaneously, without denying or dismissing any of them
Statements of value  normative
Statements of fact  empirical
CH. 2 SOCIAL INQUIRY: ETHICS AND POLITICS
 Voluntary Participation!
 No harm to the Participants!
Informed Consent: A norm in which subjects base their voluntary participation in
research projects on a full understanding of the possible risks involved
 Anonymity and Confidentiality
Anonymity: neither the research nor the readers can identify a given response with
a given respondent
Confidentiality: the researcher can identify a given person´s responses but promises
not to do so publicly
 Deception
Debriefing: Interviewing subjects to learn about their experience of participation in
the project (Often the case, if it is possible, that they are damaged)
Unobtrusive research: Methods of studying social behaviour without affecting it
CH. 3 INQUIRY, THEORY AND PARADIGMS
Paradigm: A model or frame; they provide ways of looking/observe/understand but
they don’t explain – “The way things are”
 THEORIES explain some aspects of social life
Macrotheory and Microtheory:
Macrotheory: A theory aimed at understanding the “big picture” of institutions,
whole societies and the interactions among them, e.g. Marx´s examination of the
class struggle
Microtheory: A theory aimed at understanding social life at the intimate level of
individuals/small groups, e.g. examining how the play behaviour of girls differ from
these of boys
Forms of Paradigms:
Positivism: by Comte, this philosophical system is grounded on the rational
proof/disproof of scientific assertions, assumes a knowable, objective reality
Conflict Paradigm: A Paradigm that views human behaviour as attempts to dominate
others or avoid being dominated by others
Symbolic Interactionism: A Paradigm that views human behaviour as the creation of
meaning through social interactions, with those meanings conditioning subsequent
interactions
Structural Functionalism: A Paradigm that divides social phenomena into parts, each
of which serves a function for the operation of the whole
Feminist Paradigms: Paradigms that (1) view and understand society through the
experiences of women and/or (2) examine the generally deprived status of women
in society
Critical Race Theory: A paradigm grounded in race awareness and an intention to
achieve racial justice
Interest Convergence: The thesis that majority group members will only support the
interests of minorities when those actions also support the interests of the majority
group
Postmodernism: A Paradigm that questions the assumptions of positivism and
theories describing an “objective” reality
Critical Realism: A Paradigm that holds things as real insofar as they produce effects
Elements of Social Theory
Hypothesis: an expectation about the nature of things derived from a theory. It is a
statement of something that ought to be observed in the real world if the theory is
correct
Operationalization: the process of developing operational definitions involved in
measuring variables
 Operational definition: the concrete and specific definition of something in terms
of something
Observation: looking at the world and making measurements of what is seen
Null Hypothesis: hypothesis that suggests that there is no relationship between the
variables under study
Theory explains observations by means of concepts
 Concepts are abstract elements representing classes of phenomena in the study
 Variables are special forms of concepts
 Axioms and Postulates are fundamental assertions, taken to be
true, on which a theory is grounded
 From these we might proceed to Propositions
 Propositions are specific conclusions, derived from
the axiomatic groundwork about the relationship
among concepts
Premodern view: one side is the right side  SUBJECTIVE
Modern view: there is no right, no wrong, e.g. the picture of Picasso is neither pretty
nor ugly
Postmodern view: Is what we see real?  OBJECTIVE
CH. 4 PURPOSE AND DESIGN OF RESEARCH PROJECTS
Purposes of Research
1. Exploration
 to explore a topic
 relatively new topic/subject
 many vague questions, no clear concept
What, When, Where, How, What is so?
2. Description
 Describe situations and events
 there is no question about a relationship between variables
What?
3. Explanation
 Discovery and reporting of relationships among different aspects of the
phenomenon
Why?
 Predictive question: WHAT will happen?  Future
 Remedy selection: WHICH solutions will work best under the
circumstances?
 Design questions: HOW to solve a specific problem?
 Evaluation questions: DID the solution indeed SOLVE the problem?
Criteria for Nomothetic Causality
Correlation: variables have to be correlated
 Changes in one is correlated with changes in the other
 Particular attributes of one variable are associated with ones of the other
variable
Time order: the cause has to precede the effect
Nonspuriousness: the effect cannot be explained in terms of some third variables
 Spurious relationship: A coincidental statistical correlation between two
variables, shown to be caused by some third variables (storks, babies)
Nomothetic causal analysis and Hypothesis Testing
Statistical significance: The chance you are willing to take that a given relationship
might have been caused by chance in the selection of subjects for study
Units of Analysis: The what or whom being studied, most of the time individual
people
 Possible pitfalls in dealing with Units of Analysis
 Ecological Fallacy: drawing conclusions about individuals solely from the
observation of groups
 Reductionism: try to explain a particular phenomena in terms of limited
and/or low-order concepts
The Time Dimension
Cross-sectional studies: A study based on observations representing a single point in
time
Longitudinal study: A study design involving the collection of data at different points
in time
 Trend study: Study in which a given characteristic of some population is
monitored over time
 Cohort study: Study in which some specific subpopulation, or cohort , is studied
over time, although data may be collected from different members in each set of
observations, e.g. people married in 1987
 Panel study: Study in which data are collected from the same set of people (same
panel) at several points in time
CH. 5 SAMPLING LOGIC
Nonprobability sampling: Technique in which samples are selected in some way not
suggested by probability theory
 Purposive (judgemental) sampling: Sampling in which the units to be observed
are selected on the basis of the researcher´s judgement about which ones will be
most useful to representive
 Snowball sampling: Sampling whereby each person interviewed may be asked to
suggest additional people for interviewing; often the case in field research
 Quota sampling: Sampling in which the units are selected into a sample on the
basis of prespecific characteristics, so that the total sample will have the same
distribution of characteristics assumed to exist in the population studied
Informant: Someone who is well versed in the social phenomenon that you wish to
study und who is willing to tell you what he or she knows about it – not a
respondent, a person that provide information about him-/herself
Probability sampling: The general term for samples selected in accord with
probability theory, typically involving some random-selection mechanism
Representativeness: The quality of a sample (the group) of having the same
distribution of characteristics as the population from which it was selected, e.g. if a
population has got 50% women, the sample has to be formed with nearly 50%
women, too
EPSEM (equal probability of selection method): Sample in which each member of a
population has the same chance of being selected into it
Element: That unit about which information is collected and that provides the basis
of the analysis – not units of analysis, which are used in data analysis!
Population: The group or collection that we´re interested in generalizing about
Study population: The aggregation of elements from which a sample is actually
selected
Random selection: Sampling method in which each element has an equal chance of
selection independent of any other event in the selection process, e.g. flipping a
coin, when trying to flip a set of “heads”
Sampling unit: That element or set of elements considered for selection in some
stage of sampling
Parameter: The summary description of a given variable in a population
Statistic: The summary description of a given variable in a sample, used to estimate
a population parameter
Sampling error: The degree of error to be expected by virtue of studying a sample
instead of everyone. For probability sampling the maximum error depends on three
factors: the sample size, the diversity and the confidence level
Sampling frame: The list of units composing a population, from which a sample is
selected, e.g. students selected from a student roster
_____________________
Simple random sampling: you list each member of the population and pick random
numbers representing different people in this population  each individual has the
same chance to be selected; most of the time more practical if a good sampling
frame exists and if the sample is geographically focused
Convenience sampling: you ask people walk past or you talk the next 20 products of
the production line, self-selection bias, when people participate because they have
an interest in the research or whatever,…
Systematic sampling: you choose a starting point of random and then systematically
pick objects at a certain number apart, e.g. every 7th  problem: certain types of
objects can be picked more or less often
Cluster sampling: A multistage sampling in which natural groups (clusters) are
sampled initially, with the members of each selected group being subsampled
afterward, e.g. you might select a sample of U.S. Universities and from these schools
you select again some students
Stratified sampling: just like cluster sampling – difference: the groups are chosen
specifically to represent different characteristics within the population, e.g. ethnics,
age. Within each group a sample is taken, sometimes in proportion to the group size
 VERY representative
direct observable: gender, coulour,…
indirect observable: age, nationality,…
construct: IQ, satisfaction,..
CH. 6 FROM CONCEPT TO MEASUREMENT
Conceptualization: The mental process whereby fuzzy and imprecise
notions/concepts are made more specific and precise. So you want to study
prejudice – What to you mean by prejudice? Are there different kinds? What are
they?
Indicator: An observation that we choose to consider as a reflection of a variable we
wish to study, e.g. attending religious services might be considered as an indicator of
religiosity
Specification: The process through which concepts are made more specific
Levels of Measurement:
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Nominal: categorical, refer most of the time to NAMES –
NOMINAL/descriptions/labels; NO order, NO mean or average, e.g. Sex,
chocolate preference
Ordinal: have a meaningful order, but the intervals between the scales might
not be equal! E.g. Rank or satisfaction, or the people in the run competition
Interval: have an order too and the attributes have equal distances between
them, e.g. the gab between 19°C and 20°C is as big as the gap between 45°C
and 46°C, but e.g. in an IQ-test we cannot say that a person with an IQ of 100
is twice intelligent than a person with an IQ of 50 BECAUSE THERE IS NO
ZERO-POINT
Ratio: The same like interval, with the difference that there is a “zero-point”
available in ratio, e.g. income or age
Reliability:
The quality of measurement method that suggests that the same data would have
been collected each time in repeated observations of the same phenomenon 
Would the people after 3 months still say that they drank 54 units of alcohol?
 Test-Retest Method
 Split-Half Method: For example you have 10 questions that refer to classify
people whether they are studying hard or not. You split the questions in two
5Q-sets and the people have to answer both. If the outcome is that the
people referring to the first that do study hard and referring to the other not,
than it is not reliable!
Validity:
(e.g. your IQ seems to be a more valid measurement regarding your intelligence than
the hours you spend on learning in the library)
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Face Validity: The quality of an indicator that makes it seem reasonable
measure of some variable, e.g. the frequency of attendance at religious
services measure somehow how religious you are
Criterion-related validity: based on external criterion, e.g. the validity of a
written driving test is determined by relationship between the scores people
get on the test and their subsequent driving records  driving ability is the
criterion
Construct validity: based on the logical relationship among variables; the
degree to which a measure relates to other variables as expected within a
system of theoretical relationships
Content validity: refers to how much a measure covers the range of meanings
included within a concept, e.g. a test of mathematical ability cannot be
limited to addition but also needs subtraction,…
Internal validity: Is there a causal relationship in the sample you have studied? (time
order, direction, no 3rd variable)
External validity: can he found causal relationship be generalized towards the
population?
CH. 8 SURVEYS
Respondent: A person who provides data for analyses by responding to a survey
questionnaire
Bias: That quality of a measurement device that tends to result in a
misrepresentation of what is being measured in a particular direction, e.g. a question
like “Don´t you…?”
Contingency question: A survey question intended for only some respondents,
determined by their responses to some other questions
CH. 9 EXPERIMENTS AND EXPERIMENTATION
Pretesting: The measurement of a dependent variable among subjects
Posttesting: The remeasurement of a dependent variable among subjects after they
have been exposed to an independent variable
Experimental group: in experimentation, a group of subjects to whom an
experimental stimulus is administered
Control group: in experimentation, a group of subjects to whom no experimental
stimulus is administered and who should resemble the experimental group in all
other respects. The comparison of these two groups at the end of the experiment
points to the effect of the stimulus
Double-blind experiment: An experimental design in which neither the subjects nor
the experimenters know which is the exp. and which the cont. group
Randomization: A technique for assigning experimental subjects to experimental
and control groups randomly
Matching: In connection with experiments, the procedure whereby pairs of subjects
are matched on the basis of their similarities on one or more variables, and one
member of the pairs is assigned to the exp. and the other to the cont. group
The 3 Pre-Experimental-Designs
1. One-shot case study: the researcher measures a single group of subjects on a
dependent variable after the administration of some experimental stimulus
 e.g. a man who does sport is observed in a trim shape, but we have not
made an pre-test
2. One-group pre-test-post-test design: adds an pre-test to the experimental
group but lacks on a control-group
 e.g. an overweight man who does sport is later observed in a trim shape,
but it can also be, that he gets very ill and looses therefore lot of weight
3. Static-group comparison: experimental and control groups, but no pre-tests
 e.g. a man who does sport is observed to be in a trim shape while one who
doesn´t is observed to be overweight
Internal Invalidity: Refers to the possibility that the conclusions drawn from the
experiment results may not accurately reflect what went on in the experiment itself
 Present whenever anything other than the stimulus can effect the dependent
variable
External Invalidity: Refers to the possibility that conclusions drawn from
experimental results may not be generalizable to the “real” world
CH. 11 PARADIGMS, METHODS, ETHICS OF QUALITATIVE FIELD RESEARCH
Reactivity: The problem that the subjects of social research may react to the fact of
being studied, thus altering their behaviour from what it would have been normally
Ethnomethodology: An approach to the study of social life that focuses on the
discovery of implicit, usually unspoken assumptions and agreements; this method
often involves the international breaking of agreements as a way of revealing their
existence
Case study: The in-depth examination of a single instance of some social
phenomenon, such as a village, a family,…
Focus group: A group of subjects interviewed together, prompting a discussion.
Market researchers, who ask a group of consumers to evaluate a product or discuss
a type of commodity, for example, frequently use the technique
CH. 12 EVALUATION RESEARCH: TYPES, METHODS AND ISSUES
Evaluation Research: Research undertaken for the purpose of determining the
impact of some social intervention, such as a program aimed at solving a social
problem
Quasi-Experiment: Nonrigorous inquiries somewhat resembling controlled
experiments but lacking key elements such as pre- and post-testing and/or control
groups
Time-Series Design: A research design that involves measurements made over some
period, such as the study of traffic accident rates before and after lowering the
speed limit
Nonequivalent control group: A control group that is similar to the experimental
group but is not created by the random assignment of subjects. This sort of control
group differs significantly from the experimental group in terms of the dependent
variable
Multiple time-series designs: The use of more than one set of data that were
collected over time, as in accident rates over time in several states or cities, so that
comparisons can be made
CH. 14 ANALYZING QUANTITATIVE DATA
Mode: Most frequent
Mean: sum up everything and divide by number of cases
Median: (Number of cases+1)/2 = X  look up what Person X had
CH. 15 ORIGINS AND PARADIGM OF THE ELABORATION MODEL
Elaboration Model: A logical Model for understanding the relationship between two
variables by controlling for the effect of a third one
Test variable: A variable that is held constant in an attempt to clarify further the
relationship between two other variables. Having discovered a relationship between
e.g. EDUCATION and PREJUDICE, we might hold SEX constant by examining the
relationship between EDUCATION and PREJUDICE among men only and among
women only. In this example SEX would be the test variable
Partial Relationship: In the elaboration model, this is the relationship between two
variables when examined in a subset of cases defined by a third variable. Beginning
with a zero-order relationship between political party and attitudes towards
abortion, for example, we might want to see whether the relationship held true
among both men and women. The relationship found among men and … women
would be the partial relationship
Zero-order relationship: In the elaboration model, this is the original relationship
between two variables, with no test variables controlled for
Replication: After introduction of the test variable, the original bivariate relationship
does not change
Addition: After introduction of the test variable, the original bivariate relationship
does not change, but the test variable is also related to dependent variable
Full Explanation: After introduction of the test variable, the original bivariate
relationship completely disappears. The test variable explains both the original
independent and the original dependent variable
Partial Explanation: After introduction of the test variable, the original bivariate
relationship becomes weaker. The third variable is also related to both the original
independent and the original dependent variable
Interpretation: After introduction of the test variable, the original bivariate
relationship completely disappears. The test variable is explained by the original
independent and explains the dependent variable
Partial Interpretation: After introduction of the test variable, the original bivariate
relationship becomes weaker. The test variable is also explained by the original
independent and explains the dependent variable
Specification (or Interaction): After introduction of the test variable, the original
bivariate relationship becomes weaker or completely disappears for one of the
values of the test variable, but it remains or becomes stronger for the other