Q3. Recoding the variable was done on the variable that asked whether people in the neighborhood get along. The question was previously asking whether people are not getting along with each other well. The values of this variable were altered and inverted, that is, five which stood for strongly agree was recoded to 1 standing for strongly agree. 4 was replaced with 2 and 3 remained in its position since it was a neutral point. The new values obtained were then assigned to new labels. One for strongly agree, 2 agree, three neither agree nor disagree, four disagree, and five strongly disagree.
| Descriptive Statistics | Minimum | Maximum | Mean | Std. Dev |
| close-knit neighborhood | 1 | 5 | 2.64 | 0.95 |
| people willing to help their neighbors | 1 | 5 | 2.28 | 0.816 |
| people in this neighborhood can be trusted | 1 | 5 | 2.36 | 0.82 |
| people in this neighborhood get along with each other | 1 | 5 | 2.28 | 0.766 |
The table above shows how people believe their neighborhood is. The statistics from this table first row, show people think about their neighborhood; this analysis shows that most people believe that their neighborhood is close-knit. And is shown by the lowest figure being one standing for strongly agree, and 5 was the highest standing for strongly disagree. Therefore, we see that; the mean is 2.64, which is closer to one (1), indicating the majority of the people strongly agreed or agreed.
The recoded parameter showed how people in the neighborhood get along with each other. After being recorded, the mean became 2.28, showing clearly that people firmly believe that the people in this neighborhood get along well with each other. It is on the importance to also note that the averages for the other variables are even closer to one (strongly agree), which indicates that people believe that the neighborhood people can be trusted, and they also assist each other.
| close-knit neighborhood | close-knit neighborhood | people willing to help their neighbors | people in this neighborhood can be trusted | people in this neighborhood get along with each other |
| Pearson Correlation | 1 | .560** | .448** | .308** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | |
| Sum of Squares and Cross-products | 41230.945 | 19777.699 | 15809.379 | 10196.052 |
| Covariance | 0.902 | 0.434 | 0.348 | 0.224 |
| N | 45711 | 45582 | 45368 | 45493 |
Q4. The table below shows a bivariate Pearson correlation table. From the table, it can be stated that variables, close-knit neighborhoods, people willing to help their neighbors, people in this neighborhood can be trusted, and people in this neighborhood get along with each other all have a statistically linear relationship with p<0.001.
The direction of the relationship is positive. Meaning that the variables increase together, increasing one factor leads to an increase in the other. The strength of the relationships is moderate for all the three factors lying between 0.3 and 0.5.
Q5. The histogram shows how people believe the closeness of their neighborhood is. In this case, we that few people strongly agree the community is close-knit, and the majority of the people agree that the neighborhood is tightly knit. A large percentage of the people neither agree nor disagree with both sides, whereas a few numbers of people disagree and strongly disagreeing with people who are very few.
Table 1a histogram of a close-knit neighborhood
A significant number of people agree that they are willing to help their neighbors and believe that society is composed of people who are willing to help each other. This can be interpreted to mean that people’s willingness to improve their neighbors is a clear indication that people in that neighborhood are tightly knit.
Table 2 histogram of people willing to help their neighbors.
Q6. The analysis of variance table is presented for multiple linear regression analysis done using SPSS statistical software.
Multiple linear regression analysis is done; it can be stated that the predictor variables taken for this analysis have a significant influence on the dependent variable. The independent variable taken for this analysis was close-knit neighborhoods; the predictor variables used are people willing to help their neighbors, whether people in the neighborhood can be trusted. People in the neighborhood get along with each other (decoded) and worry about being affected by crime.
The results of the analysis show multiple regression coefficients to be 0.581; the R square is 0.337. This figure 0.58 shows that the variables chosen as the independent variables can predict the dependent variable’s outcome to a good quality level. The coefficient of determination, R square, shows that the variables used in multiple linear regression can explain the independent variable by 33.7%. The coefficients of the variables show they all positively affect the close-knotless of the society, except one, the worry about being affected by crime. The results show this factor negatively affects the close-knit of the neighborhood by a factor of 0.072. The significant value of these variables is l <0.05, which indicates their relationship, and the variables indeed influence the neighborhood closeness. It can be shown, therefore, that the multiple regression was done to predict the close-knit neighborhood using the variable, the predictor variables used are people willing to help their neighbors, whether people in the neighborhood can be trusted, people in the neighborhood get along with each other (decoded) and worry about being affected by crime, are all statistically significant to the analysis. The value of F (4, 45323) =5770.016, p<0.05 and R2 = 0.337. Confirming that all four variables are statistically significant to the prediction of the dependent variable.
Q7. The conclusion attained in the analysis of these variables is that closeness of the societal neighborhood is affected by how people in that neighborhood relate to each other, starting from the individual person. People’s ability to help each other in society is the highest factor contributing to the closeness of society. It was more evident that other factors selected for this study including, whether people in the community can be trusted, whether people get along well with each other, the last element was worrying about being affected by crime.
The results showed that the variables are closely associated with the dependent variable, affecting the members’ closeness positively except the worrying of being affected by the crime, which was negative. The negative nature of this factor could indicate that in people worrying about being affected by the crime, they tend to lose trust in the neighborhood. More studies and research could be needed in this area to unravel more about this issue. The factors studied and analyzed here that affect the social cohesion in the society discussed in question one are the ability of people in the neighborhood to help each other, trust each other, get along with each other and avoid the fear of crime. Showing that if crime persists in a community, the society tends to be fabricated, and the members loose closeness, and hence cohesiveness would not be achieved. In the future, the analysis could include other factors to ascertain other factors that affect social cohesion in society. The study should consist of a lot of predictor variables.