Chapter 8
Validity of Research Results
(Reminder: Don’t forget to utilize the concept maps and study questions as you study this
and the other chapters.)
In this chapter we discuss validity issues for quantitative research and for qualitative
research.
Validity Issues in the Design of Quantitative Research
On page 228 we make a distinction between an extraneous variable and a confounding
variable.
•
An extraneous variable
is a variable that
MAY compete
with the independent
variable in explaining the outcome of a study.
•
A confounding variable
(also called a third variable
) is an extraneous variable that
DOES
cause a problem because we know that it
DOES
have a relationship with
the independent and dependent variables. A confounding variable is a variable
that systematically varies or influences the independent variable and also
influences the dependent variable.
•
When you design a research study in which you want to make a statement about
cause and effect, you must think about what extraneous variables are probably
confounding variables and do something about it.
•
We gave an example of "The Pepsi Challenge" (on p. 228) and showed that
anything that varies with the presentation of Coke or Pepsi is an extraneous
variable that may confound the relationship (i.e., it may also be a confounding
variable). For example, perhaps people are more likely to pick Pepsi over Coke if
different letters are placed on the Pepsi and Coke cups (e.g., if Pepsi is served in
cups with the letter "M" and Coke is served in cups with the letter "Q"). If this is
true then the variable of cup letter (M versus Q) is a confounding variable.
•
In short we must always worry about extraneous variables (especially
confounding variables) when we are interested in conducting research that will
allow us to make a conclusion about cause and effect.
•
There are four major types of validity in quantitative research: statistical
conclusion validity, internal validity, construct validity, and external validity. We
will discuss each of these in this lecture.
Statistical Conclusion Validity
Statistical conclusion validity
refers to the ability to make an accurate assessment about
whether the independent and dependent variables are related and about the strength of
that relationship.
So the two key questions here are 1) Are the variables related? and 2)
How strong is the relationship?
•
Typically, null hypothesis significance testing (discussed in Chapter 16) is used to
determine whether two variables are related in the population from which the
study data were selected. This procedure will tell you whether a relationship is
statistically significant or not.
•
For now, just remember that a relationship is said to be statistically significant
when we do NOT believe that it is nothing but a chance occurrence, and a
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relationship is not statistically significant
when the null hypothesis testing
procedure says that any observed relationship is probably nothing more than
normal sampling error or fluctuation.
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 Spring '11
 Staff
 Qualitative Research, researcher, Causality, internal validity

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