Chapter 4 Flashcards
Construct validity
the extent to which the measurement or manipulation of a variable accurately represents the theoretical variable being studied. In the case of measurement, is the measure that is used an accurate representation of the variable?
Internal validity
the accuracy of conclusions drawn about cause and effect
External validity
the extent to which a study’s findings can accurately be generalized to other populations and settings.
variable
Any event, situation, behavior, or individual characteristic that varies—that is, has at least two values.
operational definition
all of these:
1) Definition of a concept that specifies the method used to measure or manipulate the concept.
2) set of procedures used when you measure or manipulate the variable
A variable must have an operational definition to be studied
empirically
There are two important benefits in operationally defining a variable
1) the task of developing an operational definition of a variable forces scientists to discuss abstract concepts in concrete terms. The process can result in the realization that the variable is too vague to study. This realization does not necessarily indicate that the concept is meaningless, but rather that systematic research is not possible until the concept
can be operationally defined.
2) help researchers communicate their ideas with 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. 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.
a very important question arises once a variable is operationally defined
How good is the operational definition? How well does it match up with reality? How well does my average bowling score really represent my skill?
four most common relationships found in research
the positive linear relationship, the negative linear relationship, the curvilinear relationship, and no relationship between the variables. These relationships are best illustrated by line graphs
that show the way changes in one variable are accompanied by changes in a second variable.
nonmonotonic function
the direction of the relationship changes at
least once. Example: curvilinear
monotonic function
a relationship that does not change direction
curvilinear relationship
is called an inverted-U.
When there is no relationship between the two variables, the graph is simply a
flat line
correlation coefficient
A numerical index of the strength of relationship between variables
uncertainty
implies that there is randomness in events; scientists
refer to this as random variability in events that occur. Research can reduce random variability by identifying
systematic relationships between variables.
he relationship between the variables is stronger when
there is less
random variability
nonexperimental method
All of these:
1) relationships are studied by observing variables of interest. This may be done by asking people to describe their behavior, directly observing behavior, recording physiological responses, or even examining various public records such as census data. In all these cases, variables are observed as they occur naturally. A relationship between variables is established when the two variables vary together.
2) Use of measurement of variables to determine whether variables are related to one another. Also called correlational method.
experimental method
involves direct manipulation and control of variables. The researcher manipulates the first variable of interest and then observes the response.
There are two problems with making causal statements
when the nonexperimental method is used:
1) It can be difficult to determine the direction of cause and effect
2) researchers face the third-variable problem—that is, extraneous variables may be causing an
observed relationship
The problem of direction of cause and effect is not the most serious drawback to the nonexperimental
method, however. Scientists have pointed out, for example, that astronomers can make accurate predictions even though they often cannot manipulate variables in an experiment. In addition, the direction of cause and effect is often not crucial because, for some pairs of variables, the causal pattern may operate in
both directions.
For instance, there seem to be two causal patterns in the relationship between the variables of similarity and liking: (1) Similarity causes people to like each other, and (2) liking causes people to become more similar. In general, the third-variable problem is a much more serious fault of the nonexperimental method.
third variable
In descriptions of the relationship between two variables, a third variable is any other variable that is extraneous to the two variables of interest. True experiments control for the possible influence of third variables. Are sometimes referred to as extraneous variables
confounding variable
A variable that is not controlled in a research investigation. In an experiment, the experimental groups differ on both the independent variable and the confounding variable.
If two variables are confounded, they are
intertwined so you cannot determine which of
the variables is operating in a given situation. If income is confounded with exercise, income level will be an
alternative explanation whenever you study exercise. Fortunately, there is a solution to this problem: the
experimental method provides us with a way of controlling for the effects of third variables.
independent variable
The manipulated variable
