Question

Based on this scenario:

A study has results that seem fine, but there is no clear association to social change. What is missing? A correlation test was conducted to determine whether a relationship exists between level of income and job satisfaction. The sample consisted of 432 employees equally represented across public, private, and non-profit sectors. The results of the test demonstrate a strong positive correlation between the two variables, r =.87, p < .01, showing that as level of income increases, job satisfaction increases as well.

Ho do I evaluate the sample size?

How do I evaluate the statements for meaningfulness?

How do I evaluate the statements for statistical significance?

How do I provide an explanation of the implications for social change?

Answer 1

a)

The sample size is the number of elements/people involved in experiment for observation and data collection.

In this example, its 432 as this many employees were involved for collecting data.

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b)

As the correlation coefficient is high, it means that the two variables 'level of income' and 'job satisfaction' are highly correlated. And as the correlation is positive, it means that one variable increases with increase in another variable and vice versa.

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c)

Note thatt he p-value tells us about the statistical significance of a result. This is necessary to do because sometimes the validity of result is not justified or the result might be giving wrong information about subject of experiment.

A small p-value here suggests that the result (correlation coefficient value) is significant at 0.01 level of significance. It means that there is less than 1% chance that this result was obtained by chance. There is at least 99% chance that the correlation actually exists between these two variables.

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d)

From the result obtained we can say that the job satisfaction increases with increase in level of income. So, for firms to keep their employees satisfied, can try to increase their salary. This would be a social implication of the result.