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Anna Nagy (100)
Lecture

Chapter notes


Department
Psychology
Course Code
PSYB01H3
Professor
Anna Nagy

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Chapter 4: Studying Behavior
Variables
A variable is any event, situation, behavior, or individual characteristic that varies.
Examples of variables a psychologist might study include cognitive task
performance, word length, intelligence, gender, anger, stress, self esteem, age etc.
Each of these variables represents a general class within which specific instances
will vary. These specific instances are called the levels or values of the variable. A
variable must have two or more levels or values. For some variables, the values will
have true numeric, or quantitative, properties. Suppose that task performance is a
score on a 50- question cognitive test on which the values can range from a low of 0
correct to a high of 50 correct; these values have numeric properties.
The values of other variables are not numeric, but instead simply identify different
categories. An example is gender; the values for gender are male and female. These
are different, but they do not differ in amount or quantity.
Variables can be classified into 4 general categories:
1.Situational variables describe characteristics of a situation or environment e.g.
the length of words you read in a book, the number of bystanders to an
emergency.
2.Response variables the responses or behaviors of individuals, such as reaction
time, performance on a cognitive task, and helping a victim in an emergency.
3.Participant variables are individual differences including gender, intelligence
and personality traits
4.Mediating variables are psychological processes that mediate the effects of a
situational variable on a particular response. As an example, Darley (1968)
found that helping is less likely when there are more bystanders to an
emergency. A mediating variable called diffusion of responsibility was used to
explain this phenomenon.
Operational Definitions of Variables
It is important to know that a variable is an abstract concept that must be
translated into concrete forms of observation or manipulation. Thus, a variable such
as aggression, cognitive task performance, self esteem” or even word length
must be defined in terms of the specific method used to measure or manipulate it.
Scientists refer to the operational definition of a variable –a definition of the
variable in terms of the operations or techniques the researcher uses to measure or
manipulate it.
Variables must be operationally defined so they can be studied empirically. A
variable such as speaker credibility might be conceptualized as having two levels
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and operationally defined as a speaker described to listeners as a Nobel prize
recipient or as a substitute teacher.
There also may be several levels of abstraction when studying a variable. A variable
such as word length is concrete and easily operationalized in terms of numbers of
letters or syllables, but the exact words for the study must still be selected. The
concept of stress is very general and more abstract. When researchers study stress,
they might focus on any number of stressors such as noise, crowding etc. A
researcher interested in stress would probably choose one stressor to study and then
develop operational definitions of that specific stressor. The key point is that
researchers must always translate variables into specific operations to manipulate
or measure them.
Operational definitions also help us communicate our ideas to others. If someone
wishes to tell me about aggression, I need to know exactly what is meant by this
term because there are many ways of operationally defining it. For example,
aggression could be defined as the number of times a child punches an inflated
clown, or a score on personality measure of aggressiveness. Communication with
another person will be easier if we agree on exactly what we mean when we use the
term aggression in the context of our research.
There is a rarely a single, infallible method for operationally defining a variable. A
variety of methods may be available, each of which has advantages and
disadvantages. Because no one method is perfect, complete understanding of any
variable involves studying the variable using a variety of operational definitions.
Relationships between Variables
When both variables have values along a numeric scale, many different shapes can
describe their relationship. We begin by focusing on the four most common
relationships found in research:
1.Positive Linear Relationship increases in the values of one variable are
accompanied by increases in the values of the second variable.
2.Negative Linear Relationship increases in the values of one variable are
accompanied by decreases in the values of the other variable.
3.Curvilinear Relationship increases in the values of one variable are
accompanied by both increases and decreases in the values of the other variable.
In other words, the direction of the relationship changes at least once. This type
of relationship is sometimes reffered to as a nonmonotonic function.
4.No Relationship When there is no relationship between the two variables,
the graph is simply a flat line.
In addition to knowing the general type of relationship between two variables, it is
also necessary to know the strength of the relationship. That is, we need to know the
sixe of the correlation between the variables. A numerical index of the strength of
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relationship between variables is called a correlation coefficient. Correlation
coefficients are very important because we need to know how strongly variables are
related to one another.
Relationships and Reduction of Uncertainty
When we detect a relationship between variables, we reduce uncertainty about the
world by increasing our understanding of the variables we are examining. The term
uncertainty implies that there is randomness in events; scientists refer to this as
random variability or error variance in events that occur in the world. Research is
aimed at reducing random variability by identifying systematic relationships
between variables.
Suppose you ask 200 students at your school to tell you whether or not they like to
shop. Now suppose that 100 students said yes and 100 said no. This variability is
called random or error variance. It is called error only because we do not
understand it. If you walked up to anyone in your school and tried to guess whether
they liked shopping, you would have to make a random guess. However if we could
explain the variability, it would no longer be random.
Suppose you also asked people to indicate their gender. Now lets look at what
happens when you examine whether gender is related to shopping preference. There
are 100 males and 100 females in the study. 30 of the males say they like shopping
and 70 of the females say they like shopping. Have we reduced the random
variability? Yes. Before you had this information, there would be no way of
predicting whether a given person liked to shop. Now that you have the research
finding you can predict that any female likes to shop and any male does not; and you
will be right about 70% of the time.
Is there still random variability? Yes. You will be wrong 30% of the time and you
dont know when you will be wrong. Can you reduce this error variability? The quest
to do so motivates additional research. With further studies, you may be able to
identify other variables that are related to liking to shop. For example, variables
such as income, or age.
This discussion underscores once again that relationships between variables are
rarely perfect.
Non Experimental Versus Experimental Methods
There are two general approaches to the study of relationships among variables, the
non experimental method and the experimental method. With the non
experimental method, relationships are studied by making observations or
measures of the variables of interest. That is, behavior is observed as it occurs
naturally. This may be done by asking people to describe their behavior, directly
observing behavior, recording physiological responses or even examining public
records such as census data.
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