Assignment:
Data analysis exercise
It's not enough for a researcher to ask great question; you also need to answer them.
The purpose of the data analysis exercise is to help you choose your research topic, sharpen your research questions and to confirm that some empirical evidence does exist to help you answer your research questions.
Instructions
In approximately one page (400-500 words), email the following components
* State your main research question (don't worry if your topic or question changes during the semester). What relationships are you trying to uncover? For example, what explains different transit ridership rates across cities? ...or poverty rates? Look at the range of possible topics from the syllabus.
* State hypotheses. What do you think are potential answers to your question? For instance, if your topic is different transit ridership rates, do you think it owes to city size, city density, gas prices, etc.
* Specify variable(s) you will use to test the relationship, including the units in which they are measured. Which are the dependent variables (the issue that needs explaining) and which are the independent variables (causal variables) that may explain? Taking the transit topic again, you may say the dependent variable is the variance in transit ridership rates (% of workers using transit to commute), while the independent variables are the potential causal factors, including city size (population), density (population per sq/km), gas prices ($/L unleaded gasoline).
* Identify the source(s) from which you will be accessing the data. It is not enough to state from which website or other source, but specify the name of the dataset, years. You can search from various sources, and I can help you once you have specified research questions. For instance, from https://www.statcan.gc.ca you can choose CANSIM and then select a topic and see if they have CMA-level data. There are many other sources of data, including https://hdr.undp.org/en/statistics/
* In a separate page, produce a table that shows data for each of the variables you have specified, including the year. For instance, you may choose to present transit ridership rates for 5 Canadian cities, and then add additional columns showing independent variables suggested by your hypotheses (population, density, gas prices).
* Discuss the data you have presented. How do the data speak to your research question? At first glance are they suggestive as evidence for your hypotheses? What limitations might there be - ex. you may want transit ridership rates for the entire population, but you have to settle for transit rates for workers.
Criteria that may help you choose variables.
* Relevancy. Indicators measure meaningful and important features
* Accuracy. Indicators approximate/proxy what is intended to be measured
* Accessibility. Data are easy to find and inexpensive to acquire
* Periodicity. Data are updated regularly.
* Comparability. Data are comparable across jurisdictions
* Scalability. Data are available for neighborhood through national levels