Based on the finding of a Pearson Correlation by creating a r value. Report the

Question

Based on the finding of a Pearson Correlation by creating a r value. Report the finding and interpret the statistic in terms of effect size and R2.

Correlations

Scores on SSIS-RS

Scores on SSIS-RS

Scores on SSIS-RS

Pearson Correlation

1

.800**

Sig. (2-tailed)

.000

N

98

98

Bootstrapb

Bias

0

-.001

Std. Error

0

.056

BCa 95% Confidence Interval

Lower

.

.701

Upper

.

.897

Scores on SSIS-RS

Pearson Correlation

.800**

1

Sig. (2-tailed)

.000

N

98

98

Bootstrapb

Bias

-.001

0

Std. Error

.056

0

BCa 95% Confidence Interval

Lower

.701

.

Upper

.897

.

**. Correlation is significant at the 0.01 level (2-tailed).

b. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Correlations

Scores on SSIS-RS

Scores on SSIS-RS

Scores on SSIS-RS

Pearson Correlation

1

.800**

Sig. (2-tailed)

.000

N

98

98

Bootstrapb

Bias

0

-.001

Std. Error

0

.056

BCa 95% Confidence Interval

Lower

.

.701

Upper

.

.897

Scores on SSIS-RS

Pearson Correlation

.800**

1

Sig. (2-tailed)

.000

N

98

98

Bootstrapb

Bias

-.001

0

Std. Error

.056

0

BCa 95% Confidence Interval

Lower

.701

.

Upper

.897

.

**. Correlation is significant at the 0.01 level (2-tailed).

b. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Explanation / Answer

Pearson Correlation, effect size r= 0.800 which shows a strong relationship. The relationship between two variables is generally considered strong when their r value is larger than 0.7. Moreover, it is also significant at P = 0.01.

The coefficient of determination is the proportion of variance in one variable that is "explained" by the other variable and is calculated as the square of the correlation coefficient r^2.

r^2 = 0.8^2 = 0.64 and it can also be expressed in percentage as 64%.

A Pearson's product-moment correlation was run to assess the relationship between SCORES ON SSIS-RS of 98 people. There was a strong positive correlation between them, r(96) = .800, p < 0.01, with scores on SSIS-RS explaining 64% of the variation in Scores of SSIS-RS.

The bootstrap confidence interval shows that we can be 95% confident that the effect size r is between approximately 0.701 and 0.897

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