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Quiz von Sinead Sherman, aktualisiert more than 1 year ago
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Erstellt von Sinead Sherman
vor fast 6 Jahre
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Frage 1
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This Figure shows a scatter plot of the relationship between scores on a reading test and scores on a writing test. Based on what you can see from the plot there is:
Image:
Fig1 (binary/octet-stream)
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A negative relationship between writing and reading scores.
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A positive relationship between writing and reading scores.
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No relationship between writing and reading scores.
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A weak relationship between writing and reading scores.
Frage 2
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A researcher explores the relationship between a measure of vanity and the number of selfies posted on-line in a month. The data showed that r = .50. We can therefore determine that Vanity accounted for what percentage of variance in number of selfies posted on-line in a month?
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25%
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50%
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0.5%
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5%
Frage 3
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The process to rule out the influence of one or more additional variables on the relationship between the X and the Y variable is:
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regression
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Yate's correction
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partial correlation
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bi-variate correlation
Frage 4
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When interpreting a correlation analysis, it is important to look at which of the following features?
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The strength of relationship
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The direction of relationship
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The statistical significance of relationship
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All of the above
Frage 5
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Which of the following does a scatterplot show?
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The mean value associated with two variables.
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The proportion of data falling into one of two categories.
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The frequency with which values appear in the data.
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Scores on one variable plotted against scores on a second variable.
Frage 6
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What does the calculation of Covariance tell us?
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Whether the scores we have on variables X and Y are normally distributed.
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The extent to which variability on one variable is associated with a predictable change in another variable before standardisation.
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The extent to which variability on one variable is associated with a predictable change in another variable after standardisation.
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The percentage of explained variance between the two variables of interest.
Frage 7
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The table below shows us the zero order correlations and the partial correlations between a measure of quality of life after undergoing cosmetic surgery and participants self-reported level of depression , whilst controlling for a measure of quality of life BEFORE undergoing cosmetic surgery. Which of the following is correct.
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The proportion of variance explained is reduced once we control for the variability of our measure of quality of life BEFORE undergoing cosmetic surgery.
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The proportion of variance explained increases once we control for the variability of our measure of quality of life BEFORE undergoing cosmetic surgery.
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Controlling for the variability of our measure of quality of life BEFORE undergoing cosmetic surgery has no effect on the proportion of variance explained.
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None of the other options are correct
Frage 8
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Which of the following is the best description of the difference between a positive and a negative correlation?
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A positive correlation shows that changes on X and Y variables are going in the same direction, whilst a negative correlation is showing us that changes on our X and Y variables are going in different directions.
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A positive correlation shows that changes on X and Y variables are going in the same direction, whilst a negative correlation is showing us that there is no variability on our X and Y variables.
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We get negative correlations when we control for the influence of a third variable, and the third variable shares a lot of the variance.
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A negative correlation is a weak one, whilst a positive correlation is a strong one.
Frage 9
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Which of the following statements is NOT true:
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The coefficient of determination is calculated by taking our equation for covariance and dividing by the Mean Score of the two variables of interest
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The coefficient of determination gives us a value relating to covariance that is in standardised units.
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The coefficient of determination is calculated by taking our equation for covariance and dividing by the Standard Deviation of the two variables of interest
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The correlation coefficient gives us a measure of the degree to which the two variables of interest vary together, in unstandardized units of measurement.
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