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