Chapter 4 (pp.65-89)
Variable is any event, situation, behavior, or individual characteristic that varies. Each variable represents a general class within which specific
instances will vary. The specific instances are called the levels or value of the variable. Each variable must have two or more levels or values.
e.g cognitive task performance, word length ,spatial density, intelligence, gender, reaction time, rat of forgetting, aggression, speaker credibility,
attitude change, anger, stress, age and self-esteem.
Variables can be classified into four general categories; situational variables, responses variables, participant or subject variables and mediating
Response variables are responses or behaviors of individuals, such as reaction time, performance on a cognitive task, and helping a victim in an
Participant or subject variables are individual differences; these are the characteristics of individuals, including gender, intelligence, and
personality traits such as extraversion
Mediating variables are psychological processes that mediate the effects of a situational variable on a particular response
e.g. Darley and Latane found that helping is less likely when there are more bystanders to an emergency. The mediating variable used to
explain this case is called diffusion of responsibility. When there are several bystanders, personal responsibility to help is diffused among all the
bystanders. However when a person is the only witness to the emergency, all of the responsibility falls on that person.
Operational definition of a variable is a definition of the variable in terms of the operations or techniques the researcher uses to measure or
e.g. 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, major health problems, job burnout, and so on.A researcher interested in stress would probably choose one stressor to study and
then develop operational definitions of that specific stressor. He or she would then carry out research investigations pertaining to both to the specific
stressor and to the more general concept of stress.
The task of operationally defining a variable forces scientists to discuss abstract concepts in concrete terms. It also helps us communicate our ideas to
others. If someone wishes to tell me about aggression, I need to know exactly what s meant by term because there are many ways of operating
RELATIONSHIPS BETWEEN VARIABLES
The relationship between two variables is the general way in which the different values of one variable are associated with different values of the
other variable. When both variables have values along a numeric scale, many different shapess can describe their relationship.
There are four common relationships in research; the positive linear relationship, the negative linear relationship and the curvilinear relationship and
no relationship between variables. ( see page 69 for figures ).
Positive linear relationship increases in the values of one variable are accompanied by increases in the values of the second variable.Apositive
relationship between communicator and persuasion; higher levels of credibility are associated with greater attitude change.
Negative linear relationship increases in the values of one variable are accompanied by decreases in the values of the other variable. Latane,
Williams and Harkins found that increasing the number of people working on a task may actually reduce group effort and productivity. The
researchers asked participants to clap and shout to make as much noise as possible.As the size of the group increased, the amount of noise made by
each individual decreased.
The positive and negative linear relationships just described are examples of a more general category of relationships described as
monotonic because the relationship between the variables is always positive or negative (It doesn’t change direction, see pg 71 for positive
Curvilinear relationship increases in the values of one variable are accompanied by both increases and decreases in the values of other variable
(basically the direction of the relationship changes at least once). This relationship is also referred to as nonmonotonic function. Figure 4.2 (pg 69)
shows a curvilinear relationship between complexity of visual stimuli and ratings of preferences for the stimuli. This particular relationship is called
inverted-U relationship. Increases in visual complexity are accompanied by increases in liking for the stimulus, but only up to a point. The
relationship then becomes negative, as further increases in complexity are accompanied by decreases in liking for the stimulus.
No relationship when there is no relationship between the two variables, the graph is simply a flat line. Unrelated variables vary independently of
one another. Figure 4.2 shows the relationship between crowding and task performance. Increases in crowding are not associated with any particular
changes in performance; thus, a flat line describes the lack of relationship between two variables.
In addition to knowing the general type of relationship between two variables, it is also necessary to know the strength of the relationship (the size of
the correlation between variables). Anumerical index of strength of relationship between variables is called a correlation coefficient
RELATIONSHIPSAND REDUCTION OF UNCERTAINTY
Uncertainty implies that there is randomness in events; scientist refers to this as a random variability or error variance in events that occur in the
world. Research is aimed at reducing random variability by identifying systematic relationship between variables.
Go to pg 72 and fill in the table identify the type of relationship NONEXPERIMENTALVS. EXPERIMENTAL METHODS
There are two general approaches to the study of relationships among variables, the nonexperimental method and the experimental method.
Nonexperimental method relationships are studied by making observations or measures of the variables of interest. That is, behavior is observed as
it occurs naturally. This method allows us to observe covariation between variables and this is we can call it the correlational method.
e.g. This may be done by asking people to describe their behavior, examining public records such as census data, or recording psychological
However, there is a weakness of this method when we ask questions about cause and effect. We know the two variables are related, but what can we
say about the casual impact of one variable on the other? There are two problems with making causal statements when the nonexperimental method is
used: first, it can be difficult to determine the direction of cause and effect and second, the third-variable problem-that is, extraneous variables may be
causing an observed relationship.
When the nonexperimental method is used, there is danger that no direct causal relationship exists between the two variables. Exercise may not
influence anxiety, and anxiety may have no causal effect on exercise. Instead, there may be a relationship between the two variables because some
other variable causes both exercise and anxiety. This is known as the third-variable problem. The fact that third variables could be operating is a
serious problem because they introduce alternative explanations. When we actually know that an uncontrolled third variable is operating, we can call
it a confounding variable.
Experimental method involves direct manipulation and control variables. This method reduces ambiguity in the interpretation of results. With the
experimental method, one