Sunday, September 16, 12
Chapter 2: Studying Behaviour Scientifically
SCIENTIFIC PRINCIPLES IN PSYCHOLOGY
1. List three scientific attitudes: curiosity, skepticism, and open-mindedness
2. What is a hypothesis? How is this different from a theory? A hypothesis is a specific prediction about some phenomenon that often takes
the form of an “If-Then” statement. For examples, “In an emergency, IF multiple bystanders are present, THEN the likelihood that any one
bystander will intervene is reduced.” A hypothesis is different form a theory, because a theory is a set of formal statements that explains
how and why certain events are related to one another. Theories are broader than hypotheses, and in psychology, theories typically specify
lawful relations between certain behaviours and their causes. Scientists use theories to develop new hypotheses. Thus, the scientific
process becomes self-correcting. When research consistently supports the hypotheses derived from a theory, then confidence in the theory
increases. By contrast, if predictions made by the theory are NOT supported, then it must be modified or ultimately discarded.
3. What are the five* steps in gathering scientific evidence?
The five steps in gathering scientific research are as follows:
One: IDENTIFY the question of interest
Two: GATHER information and FORM a hypothesis
Three: TEST the hypothesis by conducting research
Four: ANALYZE data, DRAW tentative conclusions, and REPORT findings
Five: BUILD a body of knowledge (ask further questions, conduct more research, develop and test theories)
4. Why are some of the problems with hindsight understanding?
Some of the problems with hindsight (after-the-fact) understanding is that related past events can be explained in many creative,
reasonable, and sometimes contradictory ways. There is no way to determine which-if any- of the alternatives is correct.
5. Define the following terms/concepts:
Law of parsimony: If two theories can explain and predict the same phenomena equally well, the simpler theory is the preferred one.
Variable: any characteristic or factor that can vary. People's sex, height, hair colour, age, income, and grade point average (GPA) are
variables: They vary from one person to another, and many also vary within a given person over time.
Operational definition: Because any variable may mean different things to different people, scientists must define their terms clearly. And
when conducting research, scientists must also define variables operationally. An operational definition defines a variable in terms of the
specific procedures used to produce or measure it. Operational definitions translate abstract concepts into something observable and
To illustrate, suppose we want to study the relation between stress and academic performance among college students. How shall we
operationally define our variables? “Academic performance” could mean a single test score, a course grade, or one's overall GPA. For our study,
let's operationally define it as students' final exam scores in an introductory chemistry course. We also have many options for operationally
defining exam stress. How might you operationally define “exam stress” at a biological, psychological, and environmental level of analysis?
Social dersirability bias: the tendency to respond in a socially acceptable manner rather than according to how one truly feels or behaves.
Researchers can minimize social desirability bias by wording questions so that social desirability is not relevant or, if that is impossible, by
guaranteeing respondents anonymity and confidentiality so they can respond honestly without fear of future consequences.
Descriptive research: The most basic goal of science is to describe phenomena. In psychology, descriptive research seeks to identify how
humans and other animals behave, particularly in natural settings. It provides information about the diversity of behaviour and may yield clues
about potential cause-effect relations that are later tested experimentally. Case studies, naturalistic observation, and surveys are research
methods commonly used to describe behaviour.
The presence of multiple bystanders produced a diffusion of responsibility, a psychological state in which each person feels decreased
personal responsibility for intervention
Theory development is the strongest test of scientific understanding because good theories generate an integrated network of predictions.
A good theory has several important characteristics:
It incorporates existing facts and observations within a single broad framework. In other words, it organizes information in a meaningful
It is testable. It generates new hypotheses and predictions whose accuracy can be evaluated by gathering new evidence (Figure 2.3).
The predictions made by the theory are supported by the findings of new research.
It conforms to the law of parsimony: If two theories can explain and predict the same phenomena equally well, the simpler theory is the
METHODS OF RESEARCH
1. Describe each of the following research methods. What are some of the strengths and limitations of each method?
Case study method: A case study is an in-depth analysis of an individual, a group, or an event. By studying a single case in detail,
researchers typically hope to discover principles of behaviour that are true for people or situations in general. Data may be gathered through
observation, interviews, psychological tests, physiological recordings, and task performance, or from archival records. Case studies have several
advantages. First, when a rare phenomenon occurs, this method enables scientists to study it closely. Second, a case study may challenge the
validity of a theory or widely held scientific belief. Third, a case study can be a vibrant source of new ideas and hypotheses that subsequently
may be examined by using more controlled research methods. However, case studies have several limitations. First, they are a poor method for determining cause-effect relations. Second, case study findings may not generalize to other people or situations. Third, observers may not be
objective in gathering and interpreting the data.
Naturalistic observation: In naturalistic observation, the researcher observes behaviour as it occurs in a natural setting, and attempts to
avoid influencing that behaviour. In the real world, many variables simultaneously influence behaviour, and they cannot be disentangled with
this research technique. Bias in how researchers interpret what they observe is also possible. Finally, even the mere presence of an observer
may disrupt a person's or animal's behaviour. Thus, researchers may disguise their presence so that participants are not aware of being
observed. Fortunately, when disguise is not feasible, people and other animals typically adapt to and ignore the presence of an observer as time
passes. This process is called habituation, and researchers may delay their data collection until participants have habituated to the observers'
presence. Two key concepts in survey research are population and sample. A population consists of all the individuals about whom we are
interested in drawing a conclusion. Since it is impossible to study all subjects, a sample, a subset of individuals drawn from the larger
population of interest. To draw valid conclusions about a population from a survey, the sample must be representative: A representative
sampleis one that reflects the important characteristics of the population. To obtain a representative sample, survey researchers typically use a
procedure called random sampling, in which every member of the population has an equal probability of being chosen to participate in the
survey. A common variation of this procedure, called stratified random sampling, is to divide the population into subgroups based on such
characteristics as gender or ethnic identity. If the population is 45 percent male, then 45 percent of the spaces in the sample would be allocated
to men and 55 percent to women. Random sampling is then used to select the individual women and men who will be in the survey.
Survey research: In survey research, information about a topic is obtained by administering questionnaires or interviews to many people.
Political polls are a well-known example, but surveys also ask about participants' behaviours, experiences, and attitudes on wide-ranging
issues. When a representative sample is surveyed, we can be confident (though never completely certain) that the findings closely portray the
population as a whole. This is the strongest advantage of survey research. In contrast, unrepresentative samples can produce distorted results.
Other things being equal, large samples are better than small ones, but it is better to have a smaller representative sample than a larger,
Correlational research: has three componenets
The researcher measures one variable (X), such as people's birth order.
The researcher measures a second variable (Y), such as a personality trait.
The researcher statistically determines whether X and Y are related.
Remember that correlational research involves measuring variables, not manipulating them. In sum, we cannot draw causal conclusions from
correlational data, which is the major disadvantage of correlational research.
Experiment: In contrast to descriptive and correlational methods, experiments are a powerful tool for examining cause-and-effect relations.
2. What is a correlation coefficient? How would you calculate a correlation coefficient? What does it mean if 2 variables are positively
correlated? Negatively correlated?
A correlation coefficient is a statistic that indicates the direction and strength of the relation between two variables. A positive
correlation means that higher scores on one variable are associated with higher scores on a second variable. A negative
correlation occurs when higher scores on one variable are associated with lower scores on a second variable.
Correlation coefficients range from values of +1.00 to −1.00. The plus or minus sign tells you the direction of a correlation (i.e., whether
the variables are positively or negatively correlated). The absolute value of the statistic tells you the strength of the correlation. The closer
the correlation is to +1.00 (a perfect positive correlation) or −1.00 (a perfect negative correlation), the more strongly the two variables are
related. Therefore, a correlation of −0.59 indicates a stronger association between X and Y than does a correlation of +0.37. A zero
correlation (0.00) means that X and Y are not related statistically: As scores on X increase or decrease, scores on Y do not change in any
3. What conclusions can we draw based on correlation evidence?
Here is the third-variable problem: Z is responsible for what looks like a relation between X and Y (Figure 2.9c). As Z varies, it causes X to
change. As Z varies, it also causes Y to change. The net result is that X and Y change in unison, but this is caused by Z, not by any direct effect
of X or Y on each other. In sum, we cannot draw causal conclusions from correlational data, which is the major disadvantage of correlational
4. Describe the components of an experiment. What is the logic behind this type of research design?
An experiment has three essential characteristics:
The researcher manipulates (i.e., controls) one or more variables. In the simplest possible experiment, the researcher manipulates one
variable by creating two different conditions to which participants are exposed. For example, we could create a variable called “cellphone
use” by randomly assigning half of our participants to drive without talking on a cellphone and assigning the other participants to drive
while conversing on a hands-free cellphone. These would represent the two groups (conditions) in the experiment (i.e., Drive Only
condition; Drive + Cellphone Use condition).
The researcher measures whether this manipulation influences other variables (i.e., variables that represent the participants' responses).
For simplicity, let's focus on just one measure of driving performance, called “braking reaction time”: how quickly a driver depresses the
car's brake pedal when another vehicle in front of the car slows down.
The researcher attempts to control extraneous factors that might influence the outcome of the experiment. For example, while each participant
is driving, there will be no passengers and no CD or radio playing. It also would be ideal to expose the Drive + Cellphone Use and the Drive
Only participants to the same travel routes and also the same traffic and weather (e.g., temperature, visibility) condi