Question

Describe Construct Validity of Measures, Face validity, Content validity, Predictive validity, Concurrent validity, Convergent validity and Discrimination validity using the Independent and Dependent Variables from the class project as examples. Also, submit a list of potential Confounding Variables which you would want to exclude, manage or explain relating to the class project.

Answer 1

1) Convergent Validity (within larger Construct Validity) - refers to indicators / questionnaire items of a specific construct / variable are converging towards / loading a large portion of the variance that is in common (there are several ways to measure convergent validity, 1 of them is to check Average Variance Extracted (AVE) 0.5 (Hair et al., 1998)

2) Discriminant Validity (within larger Construct Validity) - refers to indicators / questionnaire items within a construct / variable are strongly associated with each other, but are distinctly from other constructs / variables' indicators / questionnaire items (to determine there is discriminant validity, square roots of AVEs should be higher than the correlations between constructs / variables)

3) Content Validity - refers to what extent the measurement is representing the content (this can be determined by panel of judges' opinions)

4) Face Validity - can be determined by basic walk through the questionnaire items whether they are intended to measure a concept

5) Concurrent Validity (within larger Criterion Validity) - refers to the questionnaire's capability that can differentiate respondents who are known to be different e.g. Theory-X vs Theory-Y workers (to determine concurrent validity, check the scores from different groups of respondents in which they should scored differently)

6) Predictive Validity (within larger Criterion Validity) - refers to the questionnaire's capability to differentiate respondents with reference to future criterion e.g. interview's aptitude test predicting future job performance (this can be determined by by checking the scores of good vs poor performers which should be different)

Examples of Confounding Variable:

1. A mother's education

Suppose a study is done to reveal whether bottle-feeding is related to an increase of diarrhea in infants. It would appear logical that the bottle-fed infants are more prone to diarrhea since water and bottles could easily get contaminated, or the milk could go bad. However, the facts are that bottle-fed infants are less likely to get diarrhea than breast-fed infants. Bottle feeding actually protects against illness. The confounding variable would be the extent of the mother's education on the matter. If you take the mother's education into account, you would learn that better educated mothers are more likely to bottle-feed infants.

2. Weather

Another example is the correlation between murder rate and the sale of ice-cream. As the murder rate raises so does the sale of ice-cream. One suggestion for this could be that murderers cause people to buy ice-cream. This is highly unlikely. A second suggestion is that purchasing ice-cream causes people to commit murder, also highly unlikely. Then there is a third variable which includes a confounding variable. It is distinctly possible that the weather causes the correlation. While the weather is icy cold, fewer people are out interacting with others and less likely to purchase ice-cream. Conversely, when it is hot outside, there is more social interaction and more ice-cream being purchased. In this example, the weather is the variable that confounds the relationship between ice-cream sales and murder.

3. Slanted wood

Another example is the relationship between the force applied to a ball and the distance the ball travels. The natural prediction would be that the ball given the most force would travel furthest. However, if the confounding variable is a downward slanted piece of wood to help propel the ball, the results would be dramatically different. The slanted wood is the confounding variable that changes the outcome of the experiment.