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What Is Science?
L01: Describe the criteria that distinguish scientific observation and reasoning
from casual, everyday thinking.
How Do Psychologists Conduct Research?
How Do We Study the Effects of Time?
How Do We Draw Conclusions From Data?
L02: Articulate how science uses testable and falsifiable hypotheses, data
collection, peer review, and replication to evaluate theories explaining
psychological phenomena.
How Can We Conduct Ethical Research?
Note about the image on this slide: Scientific methods, including brain imaging,
have allowed researchers to pinpoint structural and functional differences
between the brains of fluent speakers and people who stutter.
From early childhood throughout our lives, humans are highly motivated to
understand, explain, and predict the world around us.
People are especially motivated to understand why people think and act they
way they do – the subject of psychology.
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Psychology addresses questions about people’s thoughts and behaviors. For
example, social psychologists are very interested in understanding, explaining
and predicting prosocial behavior – helping other people.
There are many ways of knowing or understanding the world. As noted in
Chapter 1, introspection is an important way of knowing in philosophy.
Feelings and intuitions are ways of knowing in literature and religion.
Imagine a situation like this one, where the woman in the car is lost and needs
help. Who will stop to help her, and who will not? Can we explain why some
people are more likely to help than others?
People often think of “science” in terms of subjects, like biology, chemistry and
physics. Science is actually another way of knowing, with a method for
deciding whether claims about reality are true or not. Scientific methods can
be used to study human behavior as well the physical world.
We will keep using this example as we work through issues in Psychology
research methods.
Science is especially useful for gaining knowledge about cause-effect
relationships.
The key features of science are systematic observation and experimentation.
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Normal, day-to-day observations are often biased by our expectations,
feelings, and beliefs.
When possible, observers should not know which experimental conditions the
people they observe are in, so that their expectations cannot influence the
observation. When neither the participant nor the observer know which
condition each participant is in, it is called a “double-blind” procedure.
Science has rules that help remove subjective biases, and researchers are
expected to provide evidence that their observations are objective.
One aspect of systematic observation is setting rules in advance for what
observations will be included in a study. For example, you might notice times
when your friends do helpful things, but that is not very systematic. Instead, a
researcher might send research assistants to several public locations for set
periods of time, with instructions to record specific helping behaviors.
Some kinds of observations, such as physiological responses or computerbased reaction times, are inherently more objective than human observation.
Another aspect of systematic observation is making sure multiple observers
agree on what they are seeing. This is discussed under “reliability” below.
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As we shall see shortly, the term “experiment” has a specific meaning in
science. In a more general sense, though, experimentation refers to the
process of generating a testable hypothesis based on your theory, and then
collecting systematic observations, or “data,” to see whether the hypothesis is
supported.
The theory is used to generate a hypothesis – an “if-then” statement about
what should happen in a specific research scenario if the theory is true. Good
hypotheses are: (1) falsifiable, which means it is possible for systematic
observations to disconfirm the hypothesis, and (2) testable, which means that
the hypothesis can be evaluated using currently available research methods.
(Students brainstorm hypotheses suggested by their theories of helping.)
The process of science begins with a theory – a proposed set of facts and
relationships between facts that explain and predict observed phenomena. In
Psychology, theories often propose cause-effect relationships that explain
people’s behavior. (Students brainstorm variables predicting helping.)
Once you have a hypothesis, you can collect systematic observations, a.k.a.
“data,” and see whether the data are consistent with the hypothesis or not. If
so, the theory is supported. If not, the theory must be modified or discarded.
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It’s not enough for scientists to collect data. Other procedures are in place to
help ensure that the knowledge generated by science can be trusted.
The researchers John Darley and Dan Batson tested three separate theories of
helping in a classic study of prosocial behavior. The 3 theories were: (1)
People who are thinking about the moral injunction to help others will be more
helpful; (2) People who are more religious are more helpful; and (3) People
who are under time pressure will be less helpful.
Before a study can be published it is subjected to peer review. The study report
is sent to other scientists who comment on its strengths and weaknesses. If
reviewers find problems with the methods, or feel conclusions are not justified
based on the data, they may recommend against publication.
Once a study is published, other researchers may try to replicate or re-create it
to see if they get the same results. If the theory is correct other researchers
should get roughly the same results if they do the same study.
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Darley and Batson designed a study to test all three theories at once.
Participants were 40 male students at a seminary. Before the testing session,
participants filled out questionnaire measures of religiosity. At the testing
session, each participant was instructed to prepare a talk to give in another
building: the talk was either on the parable of the “Good Samaritan,” or on jobs
for seminary students. He was give one of three instructions about time
pressure: that he was early and there was plenty of time, that he was just on
time, or that he was late and would have to hurry.
The 3 hypotheses were:
(1) Participants planning a speech on the Good Samaritan will be more helpful
than those planning a speech on jobs for seminary students;
(2) People who score higher on various aspects of religiosity will more helpful
than those with lower scores; and
(3) People who have plenty of time will help more than those in a hurry.
On the way to give the talk, each participant passed a man slumped in a
doorway, coughing and groaning.
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The “victim” coded whether each participant tried to help him, and if so, how.
After a first offer, the victim said he just needed to take a pill and rest.
Data supported one hypothesis, but not the others. Helping did not depend
upon which topic the participant was planning to speak on. This suggests that
salience of norms for helping does NOT actually make people more helpful.
Helping also was not predicted by religiosity. None of the religiosity scales
used in the study was associated with higher amounts of helping
However, time pressure DID affect helping. Those who were told they had
plenty of time offered more help than those told they had to hurry.
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LO3: Explain the main goal of descriptive research methods and the ways in
which case studies, naturalistic observations, and surveys approach this goal.
Descriptive methods are used when researchers are still developing their
theories. Observations in descriptive studies may not test a specific
hypothesis, but instead be used to build preliminary evidence.
LO4: Differentiate the key features, strengths, and limitations of correlational
and experimental methods, and apply this distinction in designing a new study
or interpreting the results of a study.
Case studies are in-depth examinations of one person or a small number of
unusual people. For instance “H.M.,” a patient with amnesia resulting from
brain surgery, was studied intensively for more than 50 years.
The process of science is used to link systematic observations to our theories
about human behavior. Methods for collecting observations fall into three main
categories: descriptive, correlational, and experimental methods.
Each has strengths and limitations, and is useful in different situations.
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Naturalistic studies document the behavior of people or animals in their natural
setting. For example, Jane Goodall recorded the behavior of chimpanzees in
Tanzania for long periods of time.
In the Darley and Batson study a correlational method was used to ask
whether more religious people were more helpful.
Surveys are used to ask large numbers of people questions about their
characteristics, attitudes, beliefs, and activities. When surveys are used to
estimate the characteristics of a large group of people, it is important that the
people who take survey are as representative of that larger group as possible.
In this kind of study, the both variables are measured directly as they occur in
participants. The researchers did not try to manipulate or change their
participants’ religiosity – they just measured it and then used statistical
analyses to see whether religiosity predicted helping.
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One strength of correlational designs is that they can be used when you cannot
practically or ethically manipulate the theorized causal variable.
In contrast, experimental methods CAN be used to demonstrate causal
relationships. The Darley and Batson study contained two experiments in one.
Let’s focus on the Time Pressure variable as a example. A true experiment has
several required features.
A limitation is that correlations cannot prove a causal relationship. If Darley and
Batson HAD found religiosity predicted helping, does that mean religion makes
people more helpful? Not necessarily! A third variable may have caused both.
For example, maybe participants who were raised in small towns tended to be
more religious, but also to be more helpful.
First, researchers must manipulate the theorized causal variable, or
“independent variable.” In the helping study, researchers told each participant
whether or not he was under time pressure.
Second, researchers must randomly assign participants to the different
experimental conditions. In the helping study, the researchers could not allow
participants to decide whether they were in a hurry or not, because people who
are in a hurry naturally might differ in lots of ways from those who are not.
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Third, researchers “control” or hold constant every aspect of the situation other
than the IV. Variables that differ between the conditions, but are not part of the
hypothesis, are “confounds.” Confounds can cause differences in the DV that
have nothing to do with the independent variable. For example, if most of the
participants in the “Late” condition had been tested in the morning, and those
in the “Early” condition were tested in the afternoon, it might be that people are
more helpful in the afternoon than in the morning and time pressure has
nothing to do with it. Fortunately this was not a problem in Darley and Batson’s
study.
Fourth, researchers measure the theorized effect, or “dependent variable.” In
the helping study researchers measured the amount of help offered to the
victim by each participant.
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LO5: Compare and contrast cross-sectional versus longitudinal methods for
studying change in psychological processes over time.
Cross-sectional methods take a “slice” across different ages at the same point
in time. For example, you could do IQ tests for people who are now in their
20s, 30s, 40s, and so on. Such studies typically find that reasoning skills drop
with increasing age.
In addition to these three methods, special methods are needed when
researchers want to study how psychological processes develop or change
over time.
For example, what happens to people’s reasoning skills as they age? What
kind of study could answer this question?
Experimental methods have limitations. Because researchers must control so
many aspects of the situation, the situations can be somewhat artificial. Also, it
is unethical or impossible to manipulate many independent variables that
would be interesting to study.
However, experiments are the only methods that can be used to support a
strong claim of a cause-effect relationship between variables.
Cross-sectional methods have the advantages of being relatively easy and
inexpensive, and of controlling for historical events. However, participants in
each age group come from different cohorts who grew up in different eras.
Maybe education has been improving over the last century in ways that
improve each new cohort’s performance on IQ tests, but the intelligence of
individual people remains stable over time.
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Longitudinal methods gather data from the same set of people over a long
period of time. Longitudinal studies find that reasoning skills stays stable into
the 60s, and even then drop only slightly. Cohort effects are not a problem in
longitudinal methods, but these studies are difficult and expensive, and
apparent effects of time/age may be due to historical events that had nothing to
do with age.
LO6: Define reliability and validity, and evaluate a specific operational measure of a
variable with respect to these criteria.
The ideal design for studying change over time is mixed longitudinal design, in
which multiple cohorts are studied longitudinally
Scientific studies use strict methods for systematically observing behavior. The result is
a large amount of data. In order to explain the meaning of those data in terms of the
theory being tested, researchers must do two things.
LO7: Differentiate the kind of information conveyed by descriptive versus inferential
statistics in describing scientific data, and interpret the meaning of specific statistics
(i.e., mean, correlation coefficient).
They must evaluate the quality of the operational measures they used of each variable,
in terms of reliability and validity.
They must summarize the data using descriptive and inferential statistics.
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In designing a study to test a theory, researchers must find a concrete way to
manipulate or measure the variables in that theory. This is called an
“operational measure.” For example, Darley and Batson operationalized
“helping” in their study by how much effort each participant put into helping the
man coughing in the doorway.
Reliability is the extent to which an operational measure is consistent across
time and/or observers. If you stepped on a bathroom scale and it gave you one
number, and five minutes later it gave you a very different number, you would
not trust it – it would be unreliable. The same is true of a test or questionnaire.
In reporting a study, researchers must give evidence that their operational
measures can be trusted. Evidence must document the reliability and validity
of the measures.
Researchers need to demonstrate that the same or similar measure, given to
the same person under the same or similar conditions, would produce the
same result. When human observations are used this means showing that
different observers reach the same conclusions about what they see.
What evidence should Darley and Batson have provided for the reliability of
their helping measure? (Note: they did not document inter-rater reliability; only
the “victim” rated helping).
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Validity is the extent to which an operational measure captures the underlying
concept that it is supposed to measure.
Once researchers have collected their data, they must analyze the data. You
have often heard of “statistics” that are used to summarize study data.
However, there are two very different kinds of statistics, and both are needed
in order to draw conclusions from a study.
This can involve invoking the obvious meaning of the measure, or “face
validity,” showing that the measure correlates with other measures of the same
concept, or “construct validity,” and/or showing that the measure predicts
relevant outcomes, or “predictive validity.”
Just focusing on face validity, how valid do you think stopping to help a
coughing person is as a measure of helpfulness? What other evidence of
validity could the researchers have provided?
Descriptive statistics summarize the data that were actually collected in a
study, including the central tendency and distribution of each variable, and how
strongly variables are related to each other.
Inferential statistics are used to extend conclusions based on actual study data
to larger populations that were not included in the study. Specifically, inferential
statistics assess the probability that the sample of people studied were
somehow different from the larger population, just by chance.
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Descriptive statistics simply summarize each variable, and the relationships between
variables, as they are observed in the actual study data.
Descriptive statistics can also represent the relationship between two variables
in the study data. For example, the correlation coefficient is a number between
-1.00 and +1.00 that indicates how strongly two variables are associated; zero
means no association, and more extreme values mean a strong association.
For each variable, we are interested in the central tendency or “typical” score. The
mean – the mathematical average of all scores on that variable - is the most commonly
reported measure of central tendency. The median is the “halfway point” between the
upper and lower half of scores, and the mode is the most common value or score. In
the Darley and Batson study, mean helping was about 1.8 on the 0-5 scale.
We are also interested in the distribution of scores for each measure, or variability –
how much did people differ from each other? The most common measure of variability
is the standard deviation.
In this graph, the correlation coefficient is +.XX, indicating that high school
GPA is positively associated with Math SAT scores.
Not enough info is provided about the source to get the actual correlation
coefficient in this graph
In a typical or “normal” distribution of scores, about 2/3 of scores will fall within one
standard deviation of the mean.
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Descriptive statistics only tell us what is happening in the people researchers actually
studied. However, the goal is to draw conclusions about wider populations of people.
LO8: Evaluate specific psychology studies in terms of the ethical guidelines for
using human and animal participants in research.
Inferential statistics estimate the probability that the sample of people in the study
differs in important ways from the larger population of interest. When researchers
collect a sample of people to study they are trusting to luck that they represent the
population well, but they can never be sure.
“Statistical significance” refers to the probability that apparent relationships between
variables, observed in the study data, were only the result of sampling error and are
NOT true in the population. In the Darley and Batson study this probability was less
than 5% for the association between Time Pressure and Helping, so researchers trust
that the association is probably real.
Before doing a study at all, researchers must show that it will not cause harm
to the participants – whether those participants are humans or non-human
animals.
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Incentives must be reasonable for the time and effort of participation, not
coercive.
Research must offer a clear long-term benefit to humans or other animals.
Informed consent must be obtained, so participants know what they are
agreeing to. If participants must be deceived for the study to work, they should
be informed of the deception after the study in a “debriefing.”
Steps must be taken to minimize any harm to participants, and any possible
harm must be justified on the basis of the value of the research.
Researchers must also guarantee the confidentiality of study information.
Discuss possible ethical dilemmas arising from the Darley & Batson helping
study, and ways to resolve.
Animals must receive high-quality care, including housing, food, and veterinary
treatment.
All possible steps must be taken to minimize the animals’ pain and suffering.