Knowledge Assessment

Knowledge Assessment

Description

 

 

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

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:

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

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:

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

  Excellent Good Fair Poor
Main Posting 45 (45%) – 50 (50%)

Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources.

 

Supported by at least three current, credible sources.

 

Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

 40 (40%) – 44 (44%)

Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module.

 

At least 75% of post has exceptional depth and breadth.

 

Supported by at least three credible sources.

 

Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

 35 (35%) – 39 (39%)

Responds to some of the discussion question(s).

 

One or two criteria are not addressed or are superficially addressed.

 

Is somewhat lacking reflection and critical analysis and synthesis.

 

Somewhat represents knowledge gained from the course readings for the module.

 

Post is cited with two credible sources.

 

Written somewhat concisely; may contain more than two spelling or grammatical errors.

 

Contains some APA formatting errors.

 0 (0%) – 34 (34%)

Does not respond to the discussion question(s) adequately.

 

Lacks depth or superficially addresses criteria.

 

Lacks reflection and critical analysis and synthesis.

 

Does not represent knowledge gained from the course readings for the module.

 

Contains only one or no credible sources.

 

Not written clearly or concisely.

 

Contains more than two spelling or grammatical errors.

 

Does not adhere to current APA manual writing rules and style.
Main Post: Timeliness 10 (10%) – 10 (10%)

Posts main post by day 3.

 0 (0%) – 0 (0%) 0 (0%) – 0 (0%) 0 (0%) – 0 (0%)

Does not post by day 3.
First Response 17 (17%) – 18 (18%)

Response exhibits synthesis, critical thinking, and application to practice settings.

 

Responds fully to questions posed by faculty.

 

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

 

Demonstrates synthesis and understanding of learning objectives.

 

Communication is professional and respectful to colleagues.

 

Responses to faculty questions are fully answered, if posed.

 

Response is effectively written in standard, edited English.

 15 (15%) – 16 (16%)

Response exhibits critical thinking and application to practice settings.

 

Communication is professional and respectful to colleagues.

 

Responses to faculty questions are answered, if posed.

 

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

 

Response is effectively written in standard, edited English.

 13 (13%) – 14 (14%)

Response is on topic and may have some depth.

 

Responses posted in the discussion may lack effective professional communication.

 

Responses to faculty questions are somewhat answered, if posed.

 

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

 0 (0%) – 12 (12%)

Response may not be on topic and lacks depth.

 

Responses posted in the discussion lack effective professional communication.

 

Responses to faculty questions are missing.

 

No credible sources are cited.
Second Response 16 (16%) – 17 (17%)

Response exhibits synthesis, critical thinking, and application to practice settings.

 

Responds fully to questions posed by faculty.

 

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

 

Demonstrates synthesis and understanding of learning objectives.

 

Communication is professional and respectful to colleagues.

 

Responses to faculty questions are fully answered, if posed.

 

Response is effectively written in standard, edited English.

 14 (14%) – 15 (15%)

Response exhibits critical thinking and application to practice settings.

 

Communication is professional and respectful to colleagues.

 

Responses to faculty questions are answered, if posed.

 

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

 

Response is effectively written in standard, edited English.

 12 (12%) – 13 (13%)

Response is on topic and may have some depth.

 

Responses posted in the discussion may lack effective professional communication.

 

Responses to faculty questions are somewhat answered, if posed.

 

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

 0 (0%) – 11 (11%)

Response may not be on topic and lacks depth.

 

Responses posted in the discussion lack effective professional communication.

 

Responses to faculty questions are missing.

 

No credible sources are cited.
Participation 5 (5%) – 5 (5%)

Meets requirements for participation by posting on three different days.

 0 (0%) – 0 (0%) 0 (0%) – 0 (0%) 0 (0%) – 0 (0%)

Does not meet requirements for participation by posting on 3 different days.
Total Points: 100