When we performed pair sample correlation by SPSS software, we find output corroboration value in negative (-0.27) significant value 0.030.
Providing the variables are both scale data (pearson correlation) and normally distributed, or ordinal data (spearman correlation) if rank : The null hypothesis (Ho) is that there is no significant correlation (rank correlation) between variables. You would accept this hypothesis if your sig. value > 0.05 (5% significant) or >0.01 (1% significant).
If your sig. value < 0.05 (as is the case here) you would fail to accept Ho (the Null) and therefore the Alternate (H1) is more acceptable that there is significant correlation or rank correlation (as sig. 0.03 < 0.05). This will not hold for 1% level as sig. of 0.03 is still larger than 0.01
As you observe significant correlation the strength of that correlation is 0.27 and the relationship between the two variables is negative. the r-squared value is 0.073 which indicates that only 7.3% of your observed relationship can be explained by the data
It is significant at the 5% level and the relationship between the two variables is 7.3% explanatory from the data. If this is scale data - as well as the normality check you may also need to include comment on the cronbach's alpha of the data set to indicate the reliability.
You could add a bit more, to interpret the negative correlation, saying that as one variable increases, the other decreases...
Dear Dr. Robert James
I am very much thankful to you for valuable answer. My another set of data out, Correlation coefficient is (-0.267) at the 0.01 level (2-tailed), significant value is 0.000
It mean that, relation between two variables is 7.128% at 1% significant level.
it my statement is correct or not?
Please reply
Almost, - you Fail to Accept Ho and accept H1 (there is significant correlation at 1% level), and relationship between two variables is explained 7.128% from the data
The direction of the relationship is important as well, not just the magnitude of the relationship...
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