Statistical Study
and the other way is through giving out of information regarding the crimes and the opportunities that lead to this crime.
The other thing to consider after we have specified our model as Y = α + b1 X1 + b2 X2 + u, is the signs expected, we expect that as the level of control increases then the rate of these crimes will reduce, in the same case as the level of information increases then the rate will decrease. Therefore there exist an inverse relationship between the independent variable and the dependent variable.
Therefore the parameters of X1 and X2 will be negative in such a way that when the value of X1 increase then Y decreases, when X2 on the other hand increases then Y decreases. For the autonomous value it must take a positive value meaning that if there existed zero levels of control measure and also information then the level of crime will be at a positive level, therefore the autonomous value is positive.
After getting our data from either a secondary or primary source then there will be a need to estimate the model using the classical linear regression model, this will involve determining the parameters and the autonomous value. After regressing we will first check the signs whether they are consistent with our expected signs. After these we will test hypothesis regarding these parameters regarding their statistically significance.
If the parameters are statistically significant and our correlation levels are high then it is clear that our model is appropriate and explains the effects of the existence of the independent variables in crime prevention.
Null and alternative hypotheses
Hypothesis testing involves testing the estimates of a model to check whether the estimates are statistically significant. The steps of a statistical hypothesis test are as follows:
State the null and alternative hypothesis;
We will consider in our case the significance of
Α, b1 and b2
For α
We state the null hypothesis as
Ho: α = 0
And the alternative hypothesis will be as follows
Ha: α ≠ 0
For b1 and b2 we do the same as follows
null hypothesis
Ho: b1 = 0
alternative hypothesis
Ha: b1≠ 0
Null hypothesis
Ho: b2 = 0
Alternative hypothesis
Ha: b2≠ 0
After stating the null and alternative hypothesis we have to make a decision regarding their significance by finding the t calculated value and the t critical value
The T critical value;
The T critical value is retrieved from a z table whereby we make a decision on the level of test we would like to test, this include such levels as 95%, 98% and 99% levels.
When the T Critical is retrieved from the table taking into consideration the degrees of freedom we compare them with the t calculated value.
The T calculated;
The T calculated value will be calculated by dividing our estimated parameters with their standard errors, this gives us the t critical value, the decision therefore will be made as follows, if the T calculated value is greater than the T critical value then we reject the null hypothesis, when the t calculated value is less than the t critical value then we accept the null hypothesis. It is important to note that when we reject the null hypothesis we at the same time mean that we have accepted the alternative hypothesis. By rejecting the null hypothesis then this means that our parameter in question is statistically significance. This is because our test shows us that the value is not equal to zero at the given test level.
Data sources
Primary sources:
These are the sources of data that include the use of interviews and questionnaires to gain data, it is termed as secondary because the person undertaking the research has first hand data collection and does not rely on the use of what others have gathered.
Interviews:
Interviews are reliable source of statistical data, interviews can either be face to face interviews or phone interviews, all these interviews however require the preparation of questions that aid in the collection of data. However interviews are sometimes very difficult to conduct due to the fact that they are time consuming and they are not cost effective. In our case interviews could be conducted on victims of these crimes which will help us get data for testing our hypothesis.
Questionnaires
Data can be collected by the use of questionnaires. This involves setting up questions that will aid in the collection of data regarding these crimes. Questionnaires are designed in a way that they give us both quantitive data and at the same time qualitative data. When these questions are set up then they are first tested to check if there exist any biasness in the questions and then they are mailed to the respondents, when