Assignment 2: Understanding And Interpreting Quantitative Political Research

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Assignment 2: Understanding and Interpreting Quantitative Political Research

Part A)

Poverty
Poverty
Levels of democracy
Levels of democracy
Propensity to take part in riots
Propensity to take part in riots

Distrust in politicians
Distrust in politicians

This political phenomenon of rioting: is often considered to be an area where “too little is known about these mass phenomena” (Michael Gross, 2011). Hence, the aim of this section shall be to present some ideas that shall seek to provide a rationale for what effects “people’s propensity to take part in riots”. The arrow diagram above highlights three casual links that shall be developed into hypotheses throughout this section. The rationale behind each hypothesis shall be explained before moving on to suggest potential ways to test if any statistical association can be found between the dependent and independent variables.

The first hypothesis is that of poverty, where levels of poverty increase we would expect to see an increase in the propensity of an individual to take part in riots. If we examine areas where poverty exists, individuals and families are likely to have lower levels of income. Hence they may be more likely to riot during times of economic depression, as socio –economic conditions may result in the feeling of desperation and the need for direct action. Equally those residing in areas where poverty is rife are likely be more susceptible to take part in criminal activity due to the above average crime rate locally, so in turn this results in more people having a greater propensity to take part in riots. In direct contrast, those in areas of prosperity are less likely to riot as they will not suffer from relative deprivation, meaning they have less reason to riot.

The independent variable that shall be used for this hypothesis will be “household income”. If the hypothesis is true then we would expect to see a negative relationship between levels of income and propensity to riot. This refers to when an individual’s level of household income decreases. When this occurs we would expect to see an increase in the propensity of the individual to take part in riots. National statistics should be used to highlight differing incomes. To then examine this relationship we need to develop empirical indicators for both the dependent and independent variables. For our dependent variable we shall use a scale from 1-5; 5 meaning an individual will be very likely to take part in riots in the future, while 1 means it would be very unlikely. Income can be grouped into different categories, hence making it an ordinal variable. Then by using bivariate analysis, we can test for a causal statistical association between the two variables using linear regression.
The second hypothesis in my arrow diagram is the effect of distrust towards politicians which can often have wide ranging ramifications. Disillusionment and distrust of politicians could also increase the propensity to riot as the aforementioned feelings often aid the feeling of discontent towards the legislature in their country. In times of poor socio-economic conditions this could lead to an increased propensity to take part in riots as a sign of protest against the government and politicians. The recent London riots and student protests in the United Kingdom highlight how distrust of politicians could manifest into a desire to take part in riots. To measure this interdependent variable, we could use qualitative data, such as surveys and questionnaires to gauge public opinions towards politicians. A survey could be used to measure public opinion; a scale from 1-5 could be used, where 5 refers to a very strong distrust in politicians and where 1 refers to a strong trust. In turn questionnaires can be used to collect nominal data and question the public of their opinions with regards to the performance of politicians and the government as a whole. Therefore we can then see if
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