Im studying for my Psychology class and dont understand how to answer this. Can you help me study?
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:
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 (Id 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).
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:
| Description | |
|---|---|
| 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:
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 (Id 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:
|
| Description | |
|---|---|
| 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:
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 (Id 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:
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