Intimate Relationships
Chapter 2
Tools for Studying
Intimate
Relationships
Thomas N. Bradbury
Benjamin R. Karney
© 2010, W. W. Norton & Company, Inc.
The Importance of Relationships
• Relationships matter for people’s physical and
psychological well-being.
• People want to know what is related to
relationship satisfaction and relationship
stability.
Asking and Answering Questions
• The problem in the area of relationships is not
a lack of information, but too much
information and too much advice.
• How can the accurate information be
separated from the inaccurate information?
Asking and Answering Questions
The Key of Methodology
• The key to separating accurate from
inaccurate information is a focus on
methodology.
• Methodology asks: How was the information
gathered? How were the conclusions made?
Scientific Method
• One means of gathering information, making
conclusions, and testing those conclusions
• Key ingredients: theory, hypothesis,
operationalization, measurement, study
design, data analysis, revision of theory
The Scientific Method
Theories and Hypotheses
• Theory: general explanation
• Must be falsifiable
– For instance, one theory concerning relationship
satisfaction and stability is attachment theory.
– This theory states that the nature of people’s
romantic relationships is associated with the
nature of their parental relationships.
– It is a general answer to the issue of relationship
satisfaction and stability, and many specific
predictions can come from it.
Theories and Hypotheses
• Hypothesis: Specific, testable prediction that
comes from a theory and generally involves
the prediction that two or more variables
will be related, or that two or more groups
will be different
– For instance, a hypothesis that comes from
attachment theory is that people with a secure
attachment to their parents will also tend to
have a secure attachment to their relationship
partner.
Choosing a Measurement Strategy
• Operationalization: Specific, concrete
way of thinking about a psychological
construct
• There are many different ways to
operationalize the same psychological
construct.
– For instance, relationship satisfaction can be
operationalized in terms of mood when with
partner, desire to be with partner, overall
feelings concerning partner, and
relationship.
Choosing a Measurement Strategy
• Construct validity: The degree to which the
operationalization used reflects the
psychological construct of interest
– For instance, an operationalization of relationship
satisfaction in terms of the degree to which
partners’ clothes match would not have construct
validity.
Choosing a Measurement Strategy
• Measurement: A means of collecting data
using the operationalization of the
psychological construct
– Could be in the form of self-report
• Must watch out for social desirability concerns
• Could be an open-ended or a fixed response
• Could be omnibus or global
– Could be in the form of observation
• Inter-rater reliability is key
– Could be in the form of a physiological
response
Memory Biases in Self-reports
Designing the Study
• Correlational study design: People are
measured as they are. It examines the degree
to which variables are related to each other.
– May be positively correlated (e.g., coffee drinking
and energy) or negatively correlated (e.g., coffee
drinking and sleep)
– Cannot make causal conclusions
– May be cross-sectional
– May be longitudinal
• May use a daily diary
Different Types of Correlations
Designing the Study
• Experimental research: Rather than
measuring people as they are, the
researchers first put them into different
groups, using random assignment.
• Allows for causal conclusions
• Involves: independent variable (cause
that is tested) and dependent variable
(effect that is tested)
The Elements of a True Experiment
Designing the Study
• Archival research: Use of pre-existing data or
information to see if variables are related or
groups are different
– For instance, obituaries may be used to examine
whether married people live longer than single
people.
Summary of Research Designs
Choosing Whom to Study
• Sample: People from whom data are
collected
• Population: Group about which the
researcher wants to draw conclusions
• The sample must match the population:
– For instance, it would not make sense to
collect data from a sample of dating couples
in order to make conclusions about the
population of married couples.
Drawing Conclusions
• Research hypothesis: Prediction that comes
from theory, frequently referred to simply as
the hypothesis
• Null hypothesis: “No difference” hypothesis,
opposite of the null hypothesis; a prediction
of no association between variables or no
difference between groups
The Logic of Data Analysis
• The researcher assumes the null
hypothesis is true, collects data, and
examines how likely it would be to get
those data if the null hypothesis were
true.
• If this likelihood is low enough (smaller
than 5% or .05), the researcher
concludes that the null hypothesis is not
true and consequently, the research
hypothesis is supported.
An Example of Data Analysis
• You hypothesize that females are more
likely to break up with males than males
are to break up with females.
• The null hypothesis predicts that females
and males are equally likely to initiate
breakups.
An Example of Data Analysis,
Continued
• You collect data and find that 90% of the females
and 1% of the males in your sample have
initiated a breakup.
• If the null hypothesis were true:
– In the population males and females would be
equally likely to initiate breakups.
– By chance, you could have happened to find a sample
where more females than males break up with their
partners.
• Is this likely?
An Example of Data Analysis,
Continued
• If the null hypothesis were true:
– It would be very unlikely to find a sample with so
many females who initiate breakups and so many
males who do not initiate breakups.
– It would be so unlikely, in fact, that the researcher
would reject the null hypothesis.
• The researcher would conclude that the null
hypothesis is false and that the research
hypothesis is true.
– The researcher would conclude that females are more
likely than males to initiate breakups.
In Research Terms
• The likelihood of finding the data that were
found in a study if the null hypothesis is true =
p.
• Any time p < .05, the null hypothesis is
rejected.
• When this happens, the research hypothesis is
supported and the result is statistically
significant.
Ethical Issues
• Researchers are obligated to make sure that:
– Participants’ time isn’t wasted (i.e., studies have
to be well-designed).
– Participants’ answers aren’t shared with others,
thanks to confidentiality and anonymity.
– Participants aren’t harmed by participating in
studies.
– Participants know what to expect (i.e., they are
asked for informed consent):
• Participants are not told about the hypothesis –
instead, they are told what participating in the study
will be like.
The Need for Many Studies
• Researchers test aspects of theories through
many different studies.
• The more studies there are that demonstrate
results supporting the theory, the more
confidence researchers have in the theory.
• If study results contradict a theory, the theory
is modified and the modified theory is tested.
Additional Art for Chapter 2
Figure 2.3
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Table 2.1
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Table 2.2
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Table 2.3
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Figure 2.4
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Figure 2.6
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Table 2.4
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Figure 2.7
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Figure 2.8a
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Figure 2.8b
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Figure 2.9 a
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Figure 2.9 b
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This concludes the presentation slides for
Chapter 2: Tools for Studying Intimate Relationships
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