Concurrent Validity Coefficient

Concurrent Validity Coefficient

Instructions

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

Multiple Attempts    Not allowed. This test can only be taken once.

Force Completion     This test can be saved and resumed later.

Question Completion Status: 

QUESTION 1

  1. Submit: Your validity hypothesis and a brief explanation about why you expect the hypothesis to be supported.

 

 

 

 

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QUESTION 2

  1. Submit: The results of your statistical test of your validity hypothesis.

 

 

 

 

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 QUESTION 3

  1. Submit: Your conclusion about validity given the results of your statistical test.

 

 

 

 

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Solution 

Q1.

The scale “LIFESTYLE” is created by the summing the variables L1, L2, L3, L4, L5 and -L6 which thus implies that a higher value in the LIFESTYLE scale is representative of more extravagant and luxurious lifestyle of the participant.

I would expect a person endorsing an extravagant and luxurious life to have higher income.

Thus, I would test the validity hypothesis of a positive correlation between the scale LIFESTYLE and the participant’s income (INC1).

Q2.

Using SPSS to compute the correlation between LIFESTYLE and Participant’s income, the following result is obtained:

Correlations
Participant’s income lifestyle
Participant’s income Pearson Correlation 1 .138**
Sig. (1-tailed) .000
N 1146 1146
lifestyle Pearson Correlation .138** 1
Sig. (1-tailed) .000
N 1146 1146
**. Correlation is significant at the 0.01 level (1-tailed).

The correlation between LIFESTYLE and participant income (INC1) is 0.138

The significance level (p-value) is less than 0.001

Q3.

This confirms the hypothesis that there exists a statistically significant positive correlation between the participant’s income and the scale LIFESTYLE since the Pearson correlation value is 0.138 and p-value obtained is less than 0.001

Thus, this also gives an evidence for the validity of the LIFESTYLE scale.