EDUC 812 QUIZ 7: REGRESSION | QUESTIONS
AND ANSWERS | 2026 UPDATE | 100%
CORRECT.
SECTION 1: BIVARIATE REGRESSION & EFFECT SIZE
1. For a bivariate regression, the assessment of effect size is determined by:
• A) The F-ratio
• B) Pearson's r (Correct Answer)
• C) R-squared
• D) The p-value
Rationale: For bivariate regression, effect size is assessed using Pearson's r. This
measures the strength and direction of the linear relationship between two
variables .
2. A researcher found that the model's effect size was very large where r = .426.
What can be concluded?
• A) The effect size is small
• B) The effect size is medium
• C) The effect size is large (Correct Answer)
• D) The effect size is extremely large
Rationale: In bivariate regression, r = .426 indicates a large effect size. This is
based on Cohen's conventions for effect size interpretation where r > .37 is
considered large .
, 3. The researcher found that the model's effect size was very large where r =
.325. Furthermore, r² = .106. How much of the variance can be explained by the
linear combination of predictor variables?
• A) 32.5%
• B) 10.6% (Correct Answer)
• C) 3.25%
• D) 6.5%
Rationale: r² (the coefficient of determination) represents the proportion of
variance in the criterion variable that can be explained by the predictor variables.
When r² = .106, this means 10.6% of the variance is explained .
4. A researcher rejected the null where r(98) = .770, p < .001 and concluded that
the effect size was:
• A) Small
• B) Medium
• C) Large
• D) Very Large (Correct Answer)
Rationale: With r = .770, the effect size is considered "very large" according to
Cohen's conventions. The significant p-value indicates this result is unlikely to be
due to chance .
5. r = .672 can be interpreted as:
• A) 67.2% of the variance
• B) 45.2% of the variance (Correct Answer)
AND ANSWERS | 2026 UPDATE | 100%
CORRECT.
SECTION 1: BIVARIATE REGRESSION & EFFECT SIZE
1. For a bivariate regression, the assessment of effect size is determined by:
• A) The F-ratio
• B) Pearson's r (Correct Answer)
• C) R-squared
• D) The p-value
Rationale: For bivariate regression, effect size is assessed using Pearson's r. This
measures the strength and direction of the linear relationship between two
variables .
2. A researcher found that the model's effect size was very large where r = .426.
What can be concluded?
• A) The effect size is small
• B) The effect size is medium
• C) The effect size is large (Correct Answer)
• D) The effect size is extremely large
Rationale: In bivariate regression, r = .426 indicates a large effect size. This is
based on Cohen's conventions for effect size interpretation where r > .37 is
considered large .
, 3. The researcher found that the model's effect size was very large where r =
.325. Furthermore, r² = .106. How much of the variance can be explained by the
linear combination of predictor variables?
• A) 32.5%
• B) 10.6% (Correct Answer)
• C) 3.25%
• D) 6.5%
Rationale: r² (the coefficient of determination) represents the proportion of
variance in the criterion variable that can be explained by the predictor variables.
When r² = .106, this means 10.6% of the variance is explained .
4. A researcher rejected the null where r(98) = .770, p < .001 and concluded that
the effect size was:
• A) Small
• B) Medium
• C) Large
• D) Very Large (Correct Answer)
Rationale: With r = .770, the effect size is considered "very large" according to
Cohen's conventions. The significant p-value indicates this result is unlikely to be
due to chance .
5. r = .672 can be interpreted as:
• A) 67.2% of the variance
• B) 45.2% of the variance (Correct Answer)
