RESEARCH METHOD COHEN ok

ASSUMPTIONS OF TESTS 591
Box 26.5
continued
Interval and ratio
Mode
Mean
Frequencies
Median
Chi-square
Standard deviation (a measure of the
dispersal of scores)
What is the average score for this group
Are the scores on a parametric test evenly distributed
Do scores cluster closely around the mean Are scores
widely spread around the mean Are scores dispersed
evenly Are one or two extreme scores (‘outliers’)
exerting a disproportionate influence on what are
otherwise closely clustered scores
Chapter 26
z-scores (a statistic to convert
scores from different scales, i.e. with
different means and standard
deviations, to a common scale, i.e.
with the same mean and standard
deviation, enabling different scores
to be compared fairly)
How do the scores obtained by students on a test
which was marked out of 20 compare to the scores by
the same students on a test which was marked out of
50
Pearson product-moment
correlation (a statistic to measure
the degree of association between
two interval or ratio variables)
Is there a correlation between one set of interval data
(e.g. test scores for one examination) and another set
of interval data (e.g. test scores on another
examination)
t-tests (a statistic to measure the
difference between the means of
one sample on two separate
occasions or between two samples
on one occasion)
Are the control and experimental groups matched in
their mean scores on a parametric test Is there a
significant different between the pretest and post-test
scores of a sample group
Analysis of variance (a statistic to
ascertain whether two or more
means differ significantly)
Are the differences in the means between test results
of three groups statistically significant
The type of tests used also vary according
to whether one is working with parametric or
non-parametric data. Boxes 26.4 and 26.5 draw
together and present the kinds of statistical tests
available, depending on whether one is using
parametric or non-parametric data, together with
the purpose of the analysis. Box 26.5 sets out
the commonly used statistics for data types and
purposes (Siegel 1956; Cohen and Holliday 1996;
Hopkins et al.1996).
Assumptions of tests
Statistical tests are based on certain assumptions. It
is important to be aware of these assumptions and
to operate fairly within them. Some of the more
widely used tests have the following assumptions
(Box 26.6).
The choice of which statistics to employ is not
arbitrary, but dependent on purpose.