Knowledge Assessment

For this Knowledge Assessment, you calculate the concurrent validity
coefficient between a predictor scale and criterion measure in the
dataset provided. First, you will be guided through the process of how
to create new variable scales. Then, you calculate the validity measure
on one of the scales.

The MoneyData.sav dataset that you have been provided contains three scales that measure financial attitudes:

• LIFESTYLE (L1 to L6) measures the desire for a luxurious lifestyle
• DEPENDENCE (D1 to D6) measures the tendency to depend on others
  for financial support (high scores) vs. supporting others (low scores)
• RISKTAKING (R1 to R6) measures the tendency to take financial risks in investments and careers

Create Three New Variables Showing the Scores on These Three Scales

To create the RISKTAKING scale, click TRANSFORM>COMPUTE VARIABLE.
In the “Target Variable” field, type “RISKTAKING.” In the “Numeric
Expression” field, type SUM(R1 TO R6).

To create the DEPENDENCE scale click TRANSFORM>COMPUTE VARIABLE.
In the “Target Variable” field, type “DEPENDENCE.” In the “Numeric
Expression” field, type SUM(D1 TO D6).

On the LIFESTYLE items, item L6 (“I’d rather have a modest lifestyle
because it is less stressful”) is scored in the reverse direction from
the other items. People endorsing this item want a less extravagant lifestyle; endorsing the other items suggests the desire for a more
extravagant lifestyle. The scoring on this item needs to be reversed.
To create the reversed L6 item click TRANSFORM>COMPUTE VARIABLE. In
the “Target Variable” field, type “L6R.” In the “Numeric Expression”
field, type “6 – L6.” By subtracting the item responses from six, they
are reversed: 5 becomes 1, 4 becomes 2, etc. To create the LIFESTYLE
scale, click TRANSFORM>COMPUTE VARIABLE. In the “Target Variable”
field, type “LIFESTYLE.” In the “Numeric Expression” field, type SUM(L1
TO L5, L6R).

Calculate a Validity Measure for One of the Scales

There are a number of other variables in the data file, such as
income, sex, age, and marital status. Create a hypothesis about an
expected correlation. Here is an example: You might expect financially
dependent people to have lower incomes. So, you would predict a negative
correlation between DEPENDENCE and participant income (INC1). If you
use SPSS to calculate the correlation between Dependence and income,
(ANALYZE>CORRELATE>BIVARIATE ) you get r = – .192, p < .001. This confirms the hypothesis and gives evidence for the validity of the Dependence scale.

Think of another relationship that might support the validity of one
of the scales and then test your hypothesis using the data. You will
need to submit:

• Your validity hypothesis and a brief explanation about why you expect the hypothesis to be supported
• The results of your statistical test of your validity hypothesis
• Your conclusion about validity, given the results of your statistical test