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Data Analysis with SPSSData Analysis with SPSS
One-way ANOVAOne-way ANOVA
Analyze Compare Means One-Way Anova
Example:
We want to examine whether there are significant differences in the monthly salary of employees from different age groups.
Dependent variable : Monthly SalaryIndependent variable : Age Group
MONTHLY SALARY OF RESPONDENT
AGE GROUP OF RESPONDENT
Dependent List
Factor
Press “Post Hoc” Multiple Comparisons Dialog Box
In this example, I have chosen “Scheffe”. Then press “Continue”
Press “OK” to execute
Oneway
ANOVA
MONTHLY SALARY OF RESPONDENT
351.208 2 175.604 132.032 .000
889.778 669 1.330
1240.987 671
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
F = 132.032, Sig. = .000
Shows that the mean salary of the three age groups are significantly different
We do not know which group means are different, post hoc test will indicate this
Post Hoc Tests
Multiple Comparisons
Dependent Variable: MONTHLY SALARY OF RESPONDENT
Scheffe
-1.081* .111 .000 -1.35 -.81
-2.003* .123 .000 -2.30 -1.70
1.081* .111 .000 .81 1.35
-.922* .105 .000 -1.18 -.66
2.003* .123 .000 1.70 2.30
.922* .105 .000 .66 1.18
(J) AGE GROUP OFRESPONDENT26 - 35 YEARS
36 YEARS AND ABOVE
25 YEARS AND BELOW
36 YEARS AND ABOVE
25 YEARS AND BELOW
26 - 35 YEARS
(I) AGE GROUP OFRESPONDENT25 YEARS AND BELOW
26 - 35 YEARS
36 YEARS AND ABOVE
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound
95% Confidence Interval
The mean difference is significant at the .05 level.*.
Scheffe Multiple Comparisons test shows that all the three group means are significantly different from one another, sig. (or p) ≤ 0.001
Lets look at two other examples
ANOVA
impgdevt
.376 2 .188 .370 .691
339.527 669 .508
339.902 671
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
ANOVA to test whether there is/are significant difference(s) in the means of “importance of growth and development” between employees of different age groups
F = 0.370, p = 0.691
p >0.05, so there is no significant difference between the means of the three age groups for the importance of “growth and development”
Example 1
Post Hoc Tests
Multiple Comparisons
Dependent Variable: impgdevt
Scheffe
-.01747 .06887 .968 -.1864 .1515
.03839 .07613 .881 -.1484 .2251
.01747 .06887 .968 -.1515 .1864
.05586 .06507 .692 -.1038 .2155
-.03839 .07613 .881 -.2251 .1484
-.05586 .06507 .692 -.2155 .1038
(J) AGE GROUP OFRESPONDENT26 - 35 YEARS
36 YEARS AND ABOVE
25 YEARS AND BELOW
36 YEARS AND ABOVE
25 YEARS AND BELOW
26 - 35 YEARS
(I) AGE GROUP OFRESPONDENT25 YEARS AND BELOW
26 - 35 YEARS
36 YEARS AND ABOVE
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound
95% Confidence Interval
All the significant levels are more than 0.05, so there is no difference in the means of the groups
Example 2
ANOVA
penvr
3.975 2 1.987 3.911 .020
339.927 669 .508
343.902 671
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
ANOVA to test whether there is/are significant difference(s) in the means of “importance of safe work environment (penvr)” between employees of different age groups
F = 3.911, p = 0.02
p = 0.02, (i.e. ≤ 0.05), so there is significant difference between the means
Multiple Comparisons
Dependent Variable: penvr
Scheffe
.08662 .06891 .454 -.0824 .2557
.20965* .07617 .023 .0228 .3965
-.08662 .06891 .454 -.2557 .0824
.12303 .06511 .169 -.0367 .2828
-.20965* .07617 .023 -.3965 -.0228
-.12303 .06511 .169 -.2828 .0367
(J) AGE GROUP OFRESPONDENT26 - 35 YEARS
36 YEARS AND ABOVE
25 YEARS AND BELOW
36 YEARS AND ABOVE
25 YEARS AND BELOW
26 - 35 YEARS
(I) AGE GROUP OFRESPONDENT25 YEARS AND BELOW
26 - 35 YEARS
36 YEARS AND ABOVE
MeanDifference
(I-J) Std. Error Sig. Lower Bound Upper Bound
95% Confidence Interval
The mean difference is significant at the .05 level.*.
Post Hoc Tests
Scheffe test shows that there is significant difference between a pair of means: “25 YEARS AND BELOW” and “36 YEARS AND ABOVE”, p = 0.023 (≤0.05)
Thank You
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