1 Measures and Strengths of Association Remember that while we may find two variables to be involved in a relationship, we also want to know the strength of the association. Each type of variable has its own measure to determine this though. Three measures will be discussed in this paper, Lambda, Gamma, and Pearson’s r. Lambda Lambda is a measure of association which should be used when both variables are nominal. Essentially this means that knowing a person’s attribute on one variable will help you guess their attribute on the other (Babbie et al., 2014). Gamma Gamma can be used for two ordinal variables or one nominal and an ordinal. Unlike lambda, gamma indicates a strength of an association and a direction. The closer to -1.00 or +1.00, the stronger the relationship, whereas the closer to 0 the weaker the relationship. You can determine the direction of a relationship the following way: A negative association is indicated by a negative sign. This means that as one variable increases the other decreases- the variables are moving away from each other. For example, as social class increases, prejudice decreases. On the other hand, a positive association, indicated by a plus or positive sign, means that both variables change in the same direction, either increase or decrease. For example, as social class increases, so too does prejudice or as social class decreases, so too does prejudice. Correlation Coefficient- Pearson’s r Pearson’s r, also known as the correlation coefficient, is the test measure used to determine the association between variables that measure at the interval-ration level. This measure is similar to Gamma in how it can be understood and establish direction. Strength of Association Value of Measures of Association None 0.00 Weak- uninteresting association + or - .01 to .09 Moderate- worth making note of + or - .10 to .29 Evidence of a strong association- extremely interesting + or - .30 to .99 Perfect- strongest association possible 1.00 2 Measure of Association in SPSS Analyze – Descriptive Statistics – Crosstabs Place your dependent variable in the Row and your independent variable in the Column. Select Statistics to choose which test you will run for the measure/strength of association. You will select Lambda for nominal variables, Gamma for ordinal variables, or correlations for the Pearson’s r for Interval Ratio variables. 3 Measure of Association in SPSS- Understanding Output Lambda The test above is looking at the relationship between one’s political affiliation and their race. We look at the value .036, which is the measure when political party is the DV (see in table). This means that we can improve our guessing of political affiliation 4% is we know the person’s race. Based on our notes above, this is a pretty weak relationship and an uninteresting association ove ...

1. 1
Measures and Strengths of Association
Remember that while we may find two variables to be involved
in a relationship, we also want to
know the strength of the association. Each type of variable has
its own measure to determine
this though. Three measures will be discussed in this paper,
Lambda, Gamma, and Pearson’s r.
Lambda
Lambda is a measure of association which should be used when
both variables are nominal.
Essentially this means that knowing a person’s attribute on one
variable will help you guess their
attribute on the other (Babbie et al., 2014).
Gamma
Gamma can be used for two ordinal variables or one nominal
and an ordinal. Unlike lambda,
gamma indicates a strength of an association and a direction.
The closer to -1.00 or +1.00, the

2. stronger the relationship, whereas the closer to 0 the weaker the
relationship. You can
determine the direction of a relationship the following way:
A negative association is indicated by a negative sign. This
means that as one variable increases
the other decreases- the variables are moving away from each
other. For example, as social
class increases, prejudice decreases. On the other hand, a
positive association, indicated by a
plus or positive sign, means that both variables change in the
same direction, either increase or
decrease. For example, as social class increases, so too does
prejudice or as social class
decreases, so too does prejudice.
Correlation Coefficient- Pearson’s r
Pearson’s r, also known as the correlation coefficient, is the test
measure used to determine the
association between variables that measure at the interval-ration
level. This measure is similar
to Gamma in how it can be understood and establish direction.
Strength of Association

3. Value of Measures of Association
None 0.00
Weak- uninteresting association + or - .01 to .09
Moderate- worth making note of + or - .10 to .29
Evidence of a strong association- extremely interesting + or -
.30 to .99
Perfect- strongest association possible 1.00
2
Measure of Association in SPSS
Analyze – Descriptive Statistics – Crosstabs
Place your dependent variable in the Row and
your independent variable in the Column.
Select Statistics to choose which test you will
run for the measure/strength of association.

4. You will select Lambda for nominal variables,
Gamma for ordinal variables, or correlations
for the Pearson’s r for Interval Ratio variables.
3
Measure of Association in SPSS- Understanding Output
Lambda
The test above is looking at the relationship between one’s
political affiliation and their race. We
look at the value .036, which is the measure when political
party is the DV (see in table). This
means that we can improve our guessing of political affiliation
4% is we know the person’s race.
Based on our notes above, this is a pretty weak relationship and
an uninteresting association

5. overall. Would we still continue? Yes, maybe. It is important
to note that this is one element of
the larger picture we are searching for.
Gamma
The relationship between one’s race and their support for
affirmative action policies is shown
above. Gamma is used because support for affirmative action
policies is an ordinal variable even
though race is nominal. The value of -.377 means that knowing
a person’s race would improve
our estimate of his or her preference to affirmative action by
37%. Based on the chart above,
this relationship is strong and extremely interesting. The
negative sign would be significant and
considered if the IV were ordinal and able to ‘move’ in any
direction. However, a person can not
‘move’ among race categories.
4
Pearson’s r

6. For this test we are looking at the correlation between a
person’s age and the number of hours
they spend watching television. Keep in mind that the survey
only questioned adults over the
age of 18. So, if you are interested in seeing number of hours
for children, this is not a good data
set for that. We want to square the correlation here (.130 x
.130) to determine the coefficient of
determination. This new measure = .0169. We interpret this as
approximately 2% of the
variation in time watching television is determined by a
person’s age.
Fast-forward Thinking
This data, coupled with the statistic from the test of
significance (ex. Chi-square, t Test, or
ANOVA), will help you to interpret your findings:
Strength of
Association
Statistical
Significance
Interpretation

7. Comment
Strong Significant Knowledge of the IV improves prediction of
DV, AND
the relationship can be generalized from the sample
to the population.
Look what I have
discovered.
Strong Nonsignificant Although knowledge of the IV improves
prediction of
the DV in the sample, the relationship cannot be
generalized to the population.
Close, but no
luck…
Weak Significant Knowledge of the IV is of little help in
predicting the
DV but may be generalized to the population.
That’s no big
deal.
Weak Nonsignificant Knowledge of the IV provides little help
in the
prediction of the DV in the sample and cannot be
generalized to the population.
Back to the
drawing board
completely.
Copied from Babbie, E. et al. (2013). Adventures in Social

8. Research. (8th ed.). Thousand Oaks:
SAGE.