# Psy302 Quantitative Methods

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Psy302 Quantitative Methods
QUIZ CHAPTER FIFTEEN

Opening Day, Today: 3:00 p.m.

1. A statistical procedure used to describe the strength and direction of the linear relationship between two factors is called effect size power hypothesis testing coincidence a correlation

1. A statistical procedure used to describe the strength and direction of the linear relationship between two factors is called effect size power hypothesis testing coincidence a correlation

The strength and direction of a correlation are measured by the correlation coefficient which is represented as: μ σ r cc

2. The strength and direction of a correlation are measured by the correlation coefficient which is represented as: μ σ r cc

3. A researcher measures the following correlation between cups of coffee consumed daily and daily work schedule. Which description best explains the relationship between these two factors? The more a person works, the more coffee he or she tends to drink. The less a person works, the more coffee he or she tends to drink. The more a person works, the less coffee he or she tends to drink. No linear pattern is evident.

3. A researcher measures the following correlation between cups of coffee consumed daily and daily work schedule. Which description best explains the relationship between these two factors? The more a person works, the more coffee he or she tends to drink. The less a person works, the more coffee he or she tends to drink. The more a person works, the less coffee he or she tends to drink. No linear pattern is evident.

4. With correlation data we cannot talk about:
a relationship between variables prediction one moving with another one variable causing another all of the above

4. With correlation data we cannot talk about:
a relationship between variables prediction one moving with another one variable causing another all of the above

5. R2 or ________ is the proportion of variance in one variable that can be explained by the second.
Times square the coefficient of determination the sum of squares The Chi Square test analysis of variance

5. R2 or ________ is the proportion of variance in one variable that can be explained by the second.
Times square the coefficient of determination the sum of squares The Chi Square test analysis of variance

6. Which of the following indicates the strongest correlation?

6. Which of the following indicates the strongest correlation?

7. The correlation coefficient ranges from –1. 0 to +1
7. The correlation coefficient ranges from –1.0 to +1.0, with values closer to ±1.0 indicating a more positive relationship between two factors a stronger relationship between two factors that two factors are less likely to be related that the correlation is due to outliers all of the above

7. The correlation coefficient ranges from –1. 0 to +1
7. The correlation coefficient ranges from –1.0 to +1.0, with values closer to ±1.0 indicating a more positive relationship between two factors a stronger relationship between two factors that two factors are less likely to be related that the correlation is due to outliers all of the above

8. The correlation coefficient measures the extent to which changes in one factor are _______ in a second factor. related to changes causing changes causing variability exactly mirrored all of the above

8. The correlation coefficient measures the extent to which changes in one factor are _______ in a second factor. related to changes causing changes causing variability exactly mirrored all of the above

9. The most common measure of effect size for the correlation coefficient is called
the coefficient of determination estimated Cohen's d the test statistic

9. The most common measure of effect size for the correlation coefficient is called
the coefficient of determination estimated Cohen's d the test statistic

10. The coefficient of determination is
a measure of effect size mathematically equivalent to eta- squared equal to the correlation coefficient squared all of the above

10. The coefficient of determination is
a measure of effect size mathematically equivalent to eta- squared equal to the correlation coefficient squared all of the above

The End