Personality, 9e Jerry M. Burger

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Personality, 9e
Jerry M. Burger
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use.
Personality Research
Methods
Chapter 2
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use.
Chapter Outlines
 Hypothesis-testing approach
 Case study method
 Statistical analysis of data
 Personality assessment
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use.
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Hypothesis-Testing Approach
 Theory: General statement about the
relationship between constructs or events
 Differ in the range of events or phenomena they
explain
 Characteristics of a good theory
 Parsimonious - Explains the phenomenon in
simple terms
 Useful - Generates testable hypothesis
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Hypothesis-Testing Approach
 Hypothesis: Formal prediction about the
relationship between two or more variables
that is logically derived from the theory
 A theory is unaccepted if empirical
investigations consistently fail to confirm
predictions
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Figure 2.1 - Example of the
Hypothesis-Testing Approach
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Types of Experimental
Variables
 Independent: Determines how the
groups in the experiment are divided
 Is manipulated by the experimenter
 Known as treatment variable
 Dependent: Measured by the investigator
and used to compare the experimental
groups
 Known as outcome variable
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Interaction of Experimental
Variables
 How one independent variable affects the
dependent variable depends on the other
independent variable
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Manipulated Versus Nonmanipulated
Independent Variables
Manipulated independent
variable
 Begins with a large number of
participants
 Randomly assigns participants
to experimental groups
 Researchers assume that all the
differences will be evened out
Nonmanipulated independent
variable
 Exists without the researcher’s
intervention
 Investigator does not randomly
assign participants to a condition
 Researcher cannot assume the
people in the two groups are
identical
 Difficult to find cause-and-effect
relationships
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Prediction Versus Hindsight
 Accurate predictions can be made if a
scientist has a legitimate theory
 Purpose of research is to provide support
for a hypothesis
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Prediction Versus Hindsight
 Researchers:
 Generate a theory
 Make a hypothesis
 Collect data that supports or opposes
hypothesis
 Unpredicted findings by the researchers are
the basis for future hypotheses and research
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Replication
 Examines participant populations different
from those used in the original research
 Helps to determine whether the effect
applies to larger number of people or is
limited to the kind of individuals used in the
original sample
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Replication
 Determining the strength of an effect by
how often it is replicated is difficult
 File Drawer problem
 Investigators publish and report research only
when they find significant effects
 Failed attempt at replication makes researcher
to decide something has gone wrong
 Leads research being stored in a file drawer and
never reported
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Case Study Method
 In-depth evaluation of individuals
 Participant is a psychotherapy client
suffering from a problem that interests the
investigator
 Researcher records in detail:
 Person’s history
 Current behavior
 Changes in behavior
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Case Study
 Data are usually descriptive
 Psychologists rely on in-depth analysis of
the patients while formulating ideas about
personality
 Helped behaviorists to illustrate:
 Various aspects of their theories
 Effectiveness of their therapies
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Limitations of Case Study
Method
Generalizing from a single individual to other people
Determining cause-and-effect relationships with the case
study method
Interference of investigator's subjective judgments with
scientific objectivity
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Strengths of the Case Study
Method
Offers insight into the richness of a person's life
Valuable for generating hypotheses about the nature of human
personality
Acts as a useful research tool
Appropriate in examining a rare case
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Strengths of the Case Study
Method
Suitable for arguing that the individual being studied is normal on
the dimensions of interest
Illustrates a treatment
Demonstrates possibilities
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Statistical Analysis of Data
 Types of statistical tests appropriate for
different types of data and research designs
 Analysis of variance
 Chi-square test
 Correlation coefficient
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Statistical Analysis of Data
 Statistical significance
 The difference between two averages is large
enough to consider that it was not caused by
chance but reflects a true difference between
two observations
 Size of the difference is indicated through statistical
values known as effect size indicators
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Statistical Analysis of Data
 Correlation coefficients
 Statistical test that helps understand the
relationship between two measures
 Statistical data is reduced to a single number
that ranges from 1.00 to -1.00
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Figure 2.3 - Three Possible Relations
Between Loneliness and Depression
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Reliability
 Extent to which a test measures consistently
 Determined by calculating test-retest reliability
coefficient
 Factors contributing to poor consistency
 Vague test questions or scoring procedures
 Fluctuation in response due to taker’s mood
 Internal consistency
 All items on the test measure the same thing
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Reliability
 Internal consistency coefficient
 High coefficient indicates that most of the items
are measuring the same concept
 Low coefficient suggests items are measuring
more than one concept
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Validity
 Extent to which a test measures what it is
designed to measure
 Easy to determine for some kinds of tests
 Face validity
 Way to decide whether a test measures what it
says it measures is to look at the test items
 Less certain than with more straightforward
measures
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Validity
 Congruent validity
 Extent to which scores from the test correlate
with other measures of the same construct
 Otherwise known as convergent validity
 Discriminant validity
 Extent to which a test score does not correlate
with the scores of theoretically unrelated
measures
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Validity
 Behavioral validation
 Step in determining the construct validity of a
test
 Test scores predicting relevant behavior is
important
 Usefulness of the test must be questioned if test
scores cannot predict behavior
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use.
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