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IBM® SPSS® Amos™ 28

In the output from the example, the 90% confidence intervals for the standardized estimates appear as follows.

Standardized Regression Weights: (Group number 1 - Model 1)

 

 

 

 

 

 

 

Parameter

Estimate

Lower

Upper

P

visperc

<---

spatial

.703

.506

.906

.001

cubes

<---

spatial

.654

.475

.799

.001

lozenges

<---

spatial

.736

.535

.926

.001

paragraph

<---

verbal

.880

.794

.943

.001

sentence

<---

verbal

.827

.749

.890

.001

wordmean

<---

verbal

.841

.741

.919

.001

 

Correlations: (Group number 1 - Model 1)

 

 

 

 

 

 

 

Parameter

Estimate

Lower

Upper

P

spatial

<-->

verbal

.487

.277

.705

.002

 

The confidence interval for the correlation between spatial and verbal, for example, is [.277, .705]. Since the confidence interval does not include zero, you would reject the hypothesis that the correlation is zero in the population, using a two-sided test with a significance level of .10. To carry out a similar two-sided test with a significance level of .05, you would need to request a 95% confidence interval (Confidencebc 95). You can also refer to the value in the "p" column. Each "p" value reveals indirectly how small the confidence level would have to be to yield a confidence interval that includes the value zero. A value of p in the "p" column indicates that a 100(1-p)% confidence interval would have one of its end points at zero. In this sense, a p value can be used to test the hypothesis that an estimate has a population value of zero. For example, the correlation between spatial and verbal has a p value of .002, which implies that a 99.8% confidence interval would have its lower boundary at zero. In other words, a confidence interval at any conventional confidence level, such as .95 or .99, would not include zero, and you would reject at any conventional significance level the hypothesis that the correlation is zero in the population.

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