This guide gives you a quick overview to many issues you will encounter in finding, accessing, and using data and statistics. It primarily aims to help you find some of the best data sources that UBC provides access to, as well as some sources open to the public.The data sources are organized by geography at the top: Canada, U.S., and World, and then divided by subjects. Independent of the three geographical regions, historical data is listed separately.
Additionally, you will find guidance on some challenging tasks, such as how to cite data or how to harness the full potential of survey data sources by effectively downloading and analyzing them them.
Once you have determined whether your need falls under the category of statistics or data, you will also need to consider geography, time and detail.
Geography means which geographical region is the data of your interest about. A lot of survey data is conducted by government departments and so it is often at the country level, although there are exceptions. So if you want statistics about Canada, you would look at Statistics Canada and other sources listed under the tab Data/statistics Sources subpage Canada--Nationwide. In contrast, if you are looking to compare countries, you would be able to get a broader set of comparable statistics by looking at international statistical data sources listed under the tab Data/statistics Sources subpage World, such as World Bank data or OECD.Stat.
Time period refers to either a particular day, month or year, or a range of time that the statistics or survey data apply to. Some surveys, for example, have are run only once while others can be annual, quarterly or monthly.
Level of detail in survey data concerns the unit of analysis. What is the survey studying? For example, individuals or households? If information about individuals is required then a household level survey would not be appropriate.
Confidentiality concerns may limit access to data. Generally speaking, data sets will not allow the identification of survey respondents, This requirement will result in data suppression, random rounding or aggregation to a larger unit of analysis. This is particularly noticeable when studying smaller census areas such as dissemination areas.
This guide was adapted from the following libguides: