SECTION C (10 marks) A researcher has identifed the following variable for the p
ID: 3368889 • Letter: S
Question
SECTION C (10 marks) A researcher has identifed the following variable for the purpose of his study and came up with a following statement to be tested for any relationship between, a. starting salary and current salary, b. current salary and age, and c. age and number of days absent. He feed the data in SPSS and ran the co-relation test over it. Following output has generated by SPSs. On the basis of sPSS output you are required to interpret the results annual current absent in salary in salary in no.of days starting annual no.of days absent in 2016 Pearson Correlation 070 027 148 Sig. (2-tailed) .666 40 866 364 40 40 40 annual starting Peason Correlation salary in $1000's 070 1735( 056 000 733 40 40 3540 Sig. (2-tailed) 40 annual current salary in $1000's Pearson Correlation Sig. (2-tailed) .866 40 Pearson Correlation148 .364 40 40 40 40 0563540)1 age Sig. (2-tailed) 733 40 .025 40 4 " Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).Explanation / Answer
a)The correlation coefficient between annual starting salary and annual current salary is 0.735.and this correlation is significant at 0.01 level of significance(p value is 0.00).correlation coefficient of 0.735 indicates that there is strong positive correlation between annual starting salary and annual current salary.this means that if starting salary is high then current salary is also high.
b)The correlation coefficient between annual current salary and age is 0.354.this correlation is significant at 0.05 level of significance(p value is 0.025).The correlation coefficient of 0.354 indicates that there is weak positive correlation between age and current salary.
c)the correlation coefficient between age and number of days absent is -0.148.this correlation coefficient is insignificant(p value is 0.364).The correlation coefficient of -0.148 indicates that there is very weak negative correlation between age and number of days absent.
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