Analysis with SPSS/PSPP/JASP
Analysis with SPSS
Why use SPSS? SPSSSPSS statstical software SPSS is one of the most widespread statistical programs and provides automated processes with the help of a very detailed graphical interface and a large number of their configuration. In addition it provides the option of writing code with its help Syntax to speed up repetitive processes.
The following is a typical example of Statistical Data Analysis
Sample description
From the composite bar graph of percentages on the whole we observe that
1. Work is the most important stressor in all categories of marital status
2. The general concern for the evolution of life concerns mainly the single, those who got married for the first time and those who live with their parents.
3. The marital status that covers the widest range of sources of stress was presented to single people and married people for the first time.
Chi Square Test^{2}
With the help of the Chi Square test of independence between the variable of marital status and the variables of smoking habits and source of stress we observe that there is marital status and we expect to have different values in whether the research participants smoke as well as to their main source of stress.
ChiSquare Tests 

Value 
df 
AsymptoticSignificance (2sided) 

Pearson ChiSquare 
19,059^{a} 
7 
,008 
Likelihood Ratio 
18,338 
7 
,011 
LinearbyLinear Association 
,829 
1 
,363 
N of Valid Cases 
436 

a. 3 cells (18,8%) have expected count less than 5. The minimum expected count is 1,36. 
ChiSquare Tests 

Value 
df 
AsymptoticSignificance (2sided) 

Pearson ChiSquare 
112,174^{a} 
56 
,000 
Likelihood Ratio 
119,829 
56 
,000 
LinearbyLinear Association 
4,930 
1 
,026 
N of Valid Cases 
422 

a. 53 cells (73,6%) have expected count less than 5. The minimum expected count is ,20. 
Non parametric correlation
To control the degree of the relationship between the previous variables, a correlation was performed Spearman and the results showed that the main source of stress is to a small extent influenced by the marital status while the smoking habits are not related to the marital status of the respondent.
Correlations 

marital status 
smoker 

Spearman’s rho  marital status  Correlation Coefficient 
1,000 
,073 
Sig. (2tailed) 
. 
,128 

N 
439 
436 

smoker  Correlation Coefficient 
,073 
1,000 

Sig. (2tailed) 
,128 
. 

N 
436 
436 
Correlations 

marital status 
source of stress 

Spearman’s rho  marital status  Correlation Coefficient 
1,000 
,156^{**} 
Sig. (2tailed) 
. 
,001 

N 
439 
422 

source of stress  Correlation Coefficient 
,156^{**} 
1,000 

Sig. (2tailed) 
,001 
. 

N 
422 
422 

**. Correlation is significant at the 0.01 level (2tailed). 
A minimal effect of Gender on the causes of anxiety was also observed.
Correlations 

sex 
source of stress 

Spearman’s rho  sex  Correlation Coefficient 
1,000 
,099^{*} 
Sig. (2tailed) 
. 
,043 

N 
439 
422 

source of stress  Correlation Coefficient 
,099^{*} 
1,000 

Sig. (2tailed) 
,043 
. 

N 
422 
422 

*. Correlation is significant at the 0.05 level (2tailed). 
Two Way ANOVA
Results
Tests of BetweenSubjects Effects 

Dependent Variable: Total negative affect  
Source 
Type III Sum of Squares 
df 
Mean Square 
F 
Sig. 
Corrected Model 
1470,276^{a} 
15 
98,018 
2,019 
,013 
Intercept 
43267,124 
1 
43267,124 
891,107 
,000 
marital 
658,796 
7 
94,114 
1,938 
,062 
smoke 
32,762 
1 
32,762 
,675 
,412 
marital * smoke 
366,101 
7 
52,300 
0,03 
,000 
Error 
20198,610 
416 
48,554 

Total 
184109,000 
432 

Corrected Total 
21668,887 
431 

a. R Squared = ,068 (Adjusted R Squared = ,034) 
We observe that the interaction between marital status factors and smoking are statistically significant in 0.001 plevel
Further examination of the differences with its help Tukey’s post hoc analysis showed that among other things differences were observed between widows and couples who have a stable relationship.
Total negative affect 

Tukey HSD^{a,b,c}  
marital status 
N 
Subset 

1 
2 

WIDOWED 
7 
13,14 

MARRIED FIRST TIME 
186 
18,35 
18,35 
REMARRIED 
30 
19,10 
19,10 
DIVORCED 
23 
19,43 
19,43 
LIVING WITH PARTNER 
36 
19,56 
19,56 
SINGLE 
103 
20,20 

STEADY RELATIONSHIP 
37 
22,24 

SEPARATED 
10 
24,40 

Sig. 
,066 
,102 

Means for groups in homogeneous subsets are displayed.
Based on observed means. The error term is Mean Square(Error) = 48,554. 

a. Uses Harmonic Mean Sample Size = 20,536.  
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.  
c. Alpha = 0,05. 
Graph of Interactions
Smoking habits have been shown to affect the values of the rating of negative effects during the transition from an informal relationship to a more formal one (marriage).
3way ANOVA
Further examination of the Gender factor in the previous relationship showed that it exhibits statistically significant interactions.
Tests of BetweenSubjects Effects 

Dependent Variable: Total negative affect  
Source 
Type III Sum of Squares 
df 
Mean Square 
F 
Sig. 
Corrected Model 
2246,559^{a} 
28 
80,234 
1,665 
,020 
Intercept 
44247,737 
1 
44247,737 
918,110 
,000 
marital 
728,713 
7 
104,102 
2,160 
,037 
smoke 
21,755 
1 
21,755 
,451 
,502 
sex 
35,130 
1 
35,130 
,729 
,394 
marital * smoke 
449,449 
7 
64,207 
1,332 
,233 
marital * sex 
347,783 
6 
57,964 
1,203 
,304 
smoke * sex 
79,565 
1 
79,565 
1,651 
,200 
marital * smoke * sex 
75,414 
5 
15,083 
,002 
,000 
Error 
19422,328 
403 
48,194 

Total 
184109,000 
432 

Corrected Total 
21668,887 
431 

a. R Squared = ,104 (Adjusted R Squared = ,041) 
Further graphical examination of our factor showed that no interactions occurred in men while the previous transition we described held true in women.
Conclusion
The difference in the scores of the negative effects during the transition from the free state or the state of informal cohabitation applies only to women.