Chapter 2
Research Methods
The Scientific Approach: A Search
for Laws
• Empiricism: testing hypothesis
• Basic assumption: events are governed by some lawful order
• Goals:
– Measurement and description
– Understanding and prediction
– Application and control
• Goal of theory testing in science: refutation not proving – Karl
Popper
What is Experimental Research?
• Explores cause and effect relationships
• Has control and experimental groups
• Laboratory experiments are good at controlling variables.
Implementing school uniforms causes
Less violence in school
Steps in Designing
an Experiment
1.
Hypothesis
2.
Design Study:
 Pick Population: Random Selection
then Random Assignment.
 Operationalize the Variables
 Identify Independent and
Dependent Variables.
 Look for Extraneous Variables
 Type of Experiment: Blind, Double
Blind etc..
3.
Gather Data
4.
Analyze Results
5.
Publish
Experimental Research:
Looking for Causes
• 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 manipulation
– 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 variable
• Control group – similar subjects who do not receive the
special treatment
– Logic:
• Two groups alike in all respects (random assignment)
• Manipulate independent variable for one group only
• Resulting differences in the two groups must be due to
the independent variable
• Extraneous and confounding variables
Experimental Designs:
Variations
• Expose a single group to two different conditions
–
Reduces extraneous variables
• Manipulate more than one independent variable
– Allows for study of interactions between variables
• Use more than one dependent variable
– Obtains a more complete picture of effect of the
independent variable
Figure 2.7 Manipulation of two independent variables in an experiment
Strengths and Weaknesses of
Experimental Research
• Strengths:
– conclusions about cause-and-effect can be drawn
– Probabilistic causality
• Weaknesses:
– artificial nature of experiments
– ethical and practical issues
Descriptive/Correlational Methods:
Looking for Relationships
• 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 2.9
Sample from a case
study – a descriptive
research method
Statistics and Research:
Drawing Conclusions
•
Statistics – using mathematics to organize,
summarize, and interpret numerical data
• Descriptive statistics: organizing and
summarizing data
• Inferential statistics: interpreting data and
drawing conclusions – use of probability
Descriptive Statistics: Measures
of Central Tendency
• 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?
Descriptive Statistics: Variability
• Variability = how much scores vary from each other and
from the mean
– Standard deviation = numerical depiction of
variability
• High variability in data set = high standard
deviation
• Low variability in data set = low standard deviation
Descriptive Statistics:
Correlation
• When two variables are related to each other, they are
correlated
• Correlation = numerical index of degree of relationship
– Correlation expressed as a number between 0 and 1
– Can be positive or negative
– Numbers closer to 1 (+ or -) indicate stronger
relationship
Figure 2.13 Positive and negative correlation
XX 2.14
Correlation: Prediction, Not
Causation
• Higher correlation coefficients = increased ability to
predict one variable based on the other
Example: 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
– Very low = less than 5 chances in 100/ .05 level
– Other factors might account for the result
Evaluating Research:
Methodological Pitfalls
• Sampling bias
• Placebo effects – is not always uniform – cost factors and
perceived pain
• Distortions in self-report data:
– Social desirability bias
– Response set
• Experimenter bias
– the double-blind solution
– Research protocol of clinical trial for drugs – FDA in U.S.
Ethics in Psychological
Research: Do the Ends Justify
the Means?
• 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