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Chapter 2 The Research Enterprise in Psychology


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Title: Chapter 2 The Research Enterprise in Psychology

Chapter 2 The Research Enterprise in Psychology
The Scientific Approach A Search for Laws
  • Basic assumption events are governed by some
    lawful order
  • Goals
  • Measurement and description
  • Understanding and prediction
  • Application and control

Tools of the Trade Definitions, Data, Journals,
and Methods
  • Operational definitions are used to clarify what
    variables mean
  • Statistics are used to analyze data and decide
    whether hypotheses were supported
  • Findings are shared through reports at scientific
    meetings and in scientific journals
  • Research methods general strategies for
    conducting scientific studies

Figure 2.1 Theory construction. A good theory
will generate a host of testable hypotheses. In a
typical study, only one or a few of these
hypotheses can be evaluated. If the evidence
supports the hypotheses, our confidence in the
theory they were derived from generally grows. If
the hypotheses are not supported, confidence in
the theory decreases and revisions to the theory
may be made to accommodate the new findings. If
the hypotheses generated by a theory
consistently fail to garner empirical support,
the theory may be discarded altogether. Thus,
theory construction and testing is a gradual
Figure 2.2 Flowchart of steps in a scientific
investigation. As illustrated in a study by Cole
et al. (1996), a scientific investigation
consists of a sequence of carefully planned
steps, beginning with the formulation of a
testable hypothesis and ending with the
publication of the study, if its results are
worthy of examination by other researchers.
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Experimental Research Basics
  • Experiment manipulation of one variable under
    controlled conditions so that resulting changes
    in another variable can be observed
  • Detection of cause-and-effect relationships
  • Independent variable (IV) variable manipulated
  • Dependent variable (DV) variable affected by
  • How does X affect Y?
  • X Independent Variable and Y Dependent Variable

Experimental and Control Groups The Logic of the
Scientific Method
  • Experimental group subjects who receive some
    special treatment in regard to the independent
  • Control group similar subjects who do not
    receive the special treatment
  • Logic
  • Two groups alike in all respects (random
  • Manipulate independent variable for one group
  • Resulting differences in the two groups must be
    due to the independent variable

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Experimental Designs Variations
  • Expose a single group to two different
  • Reduces extraneous variables
  • Manipulate more than one independent variable
  • Allows for study of interactions between
  • Use more than one dependent variable
  • Obtain a more complete picture of effect of IV

Figure 2.6 Manipulation of two independent
variables in an experiment. As this example
shows, when two independent variables are
manipulated in a single experiment, the
researcher has to compare four groups of subjects
(or conditions) instead of the usual two. The
main advantage of this procedure is that it
allows an experimenter to see whether two
variables interact.
Strengths and Weaknesses of Experimental Research
  • Strength
  • conclusions about cause-and-effect can be drawn
  • Weaknesses
  • artificial nature of experiments
  • ethical and practical issues

Descriptive/Correlational Methods Looking for
  • Methods used when a researcher cannot manipulate
    the variables under study
  • Naturalistic observation
  • Case studies
  • Surveys
  • Allow researchers to describe patterns of
    behavior and discover links or associations
    between variables but cannot imply causation

Figure 2.10 Comparison of major research
methods. This chart pulls together a great deal
of information on key research methods in
psychology and gives a simple example of how each
method might be applied in research on
aggression. As you can see, the various research
methods each have strengths and weaknesses.
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Statistics and Research Drawing Conclusions
  • Statistics using mathematics to organize,
  • and interpret numerical data
  • Descriptive statistics
  • organizing and summarizing data
  • Inferential statistics
  • interpreting data and drawing conclusions

Descriptive Statistics Measures of Central
  • Measures of central tendency typical or average
    score in a distribution
  • Mean arithmetic average of scores
  • Median score falling in the exact center
  • Mode most frequently occurring score
  • Which most accurately depicts the typical?

Figure 2.11 Measures of central tendency. The
three measures of central tendency usually
converge, but that is not always the case, as
these data illustrate. Which measure is most
useful depends on the nature of the data.
Generally, the mean is the best index of central
tendency, but in this instance the median is more
Descriptive Statistics Variability
  • Variability how much scores vary from each
    other and from the mean
  • Standard deviation numerical depiction of
  • High variability in data set high standard
  • Low variability in data set low standard

Descriptive Statistics Correlation
  • When two variables are related to each other,
    they are correlated.
  • Correlation numerical index of degree of
  • Correlation expressed as a number between 0 and 1
  • Can be positive or negative
  • Numbers closer to 1 ( or -) indicate stronger

Figure 2.13 Positive and negative
correlation. Notice that the terms positive and
negative refer to the direction of the
relationship between two variables, not to its
strength. Variables are positively correlated if
they tend to increase and decrease together and
are negatively correlated if one tends to
increase when the other decreases.
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Figure 2.14 Interpreting correlation
coefficients. The magnitude of a correlation
coefficient indicates the strength of the
relationship between two variables. The sign
(plus or minus) indicates whether the correlation
is positive or negative. The closer the
coefficient comes to 1.00 or 1.00, the
stronger the relationship between the variables.
Figure 2.15 Three possible causal relations
between correlated variables. If variables X and
Y are correlated, does X cause Y, does Y cause X,
or does some hidden third variable, Z, account
for the changes in both X and Y? As the
relationship between smoking and depression
illustrates, a correlation alone does not provide
the answer. We will encounter this problem of
interpreting the meaning of correlations
frequently in this text.
Correlation Prediction , Not Causation
  • Higher correlation coefficients increased
    ability to predict one variable based on the
  • SAT/ACT scores moderately correlated with first
    year college GPA
  • 2 variables may be highly correlated, but not
    causally related
  • Foot size and vocabulary positively correlated
  • Do larger feet cause larger vocabularies?
  • The third variable problem

Inferential Statistics Interpreting Data and
Drawing Conclusions
  • Hypothesis testing do observed findings support
    the hypotheses?
  • Are findings real or due to chance?
  • Statistical significance when the probability
    that the observed findings are due to chance is
    very low

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Evaluating Research Watch for Methodological
  • Sampling bias
  • Placebo effects
  • Distortions in self-report data
  • Social desirability bias
  • Response set
  • Experimenter bias and the double-blind solution

Ethics in Psychological Research Do the Ends
Justify the Means?
  • The question of deception
  • The question of animal research
  • Controversy among psychologists and the public
  • Ethical standards for research the American
    Psychological Association
  • Ensures both human and animal subjects are
    treated with dignity

Figure 2.17 Ethics in research. Key ethical
principles in psychological research, as set
forth by the American Psychological Association
(1992), are summarized here. These principles are
meant to ensure the welfare of both human and
animal subjects.