For the correlational relationship below, find at least one confound (also called third-variables) that could help us to explain this relationship (rather than assuming that a causal relationship between variables exists). Remember, confounds in correlational studies explain the relationship between variables because they cause both variables, rather than one or the other. Therefore, be sure to explain how a confound influences both variables in your responses. 1. The more a person weighs, the larger his or her vocabulary is. Should we promote weight gain among college students?
For the correlational relationship below, find at least one confound (also called third-variables) that could help us to explain this relationship (rather than assuming that a causal relationship between variables exists). Remember, confounds in correlational studies explain the relationship between variables because they cause both variables, rather than one or the other. Therefore, be sure to explain how a confound influences both variables in your responses.
1. The more a person weighs, the larger his or her vocabulary is. Should we promote weight gain among college students?
Correlation can be defined as the intensity or strength or power of relationship between variables. For example: one may be interested or curious to know whether the poor dietary habits and life expectancy are related or connected to each other. Through research, he come to know that there is a negative correlation between the poor dietary habits and life expectancy, meaning, when there is an increase in the value of poor dietary habits then the value of life expectancy decreases.
Correlation tell us about what will be the direction of the relationship, meaning whether it is positive or negative and also tell us about the strength, meaning the value of correlation always lie between -1 to +1.
- -1 indicates or shows the strong negative correlation between the variables.
- +1 indicates or shows the strong positive correlation between the variables.
- 0 indicates no correlation between variables.
Correlations are of 3 types: Positive, Negative and No correlation.
- Positive: When both the variables move or proceed or progress in a same direction.
- Negative: When both the variables move or proceed or progress in a different or inverse direction.
- No correlation: When there is no relationship between the variables. Here the value of correlation is 0.
Correlation does not mean that one variable is the cause or the reason of other variable. Research shows that some other variable or extraneous variable or third variable may be the cause or reason of both the variables.
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