When people read, hear, or prepare research summaries,
they sometimes have misconceptions about what is or isn't "sound
practice" regarding the collection, analysis, and interpretation
of data. Here are some of these common (and dangerous) misconceptions
associated with the content of Chapter 3.
A correlation coefficient does a better job of summarizing
the strength and direction of a relationship between two variables than
does a scatter diagram.
If the correlation between the scores on two variables
is very high, then the two means must be very similar.
A correlation of .80 indicates twice the "relationship
strength" as compared to a correlation of .40.
A correlation never speaks to the notion of "cause
If a single outlier is removed from a very large
group, the value of r cannot change very much.
An r of -.90 signifies a "low" relationship.
If the correlation between two variables is equal
to +.50 for a subgroup of men, and if the correlation between these
same two variables is +.50 for a subgroup of women, then the correlation
between these two variables will be +.50 for the combined group of men
There are commonly agreed-upon guidelines that clarify
for researchers when they should use terms such as "strong,"
"moderate," and "weak" to describe relationship
A linear relationship between two variables exists
only if the dots in a scatter diagram all fall on a straight line.
If the researcher's data correspond to two variables
that are qualitative in nature, it's impossible to compute a correlation