Collecting and Displaying Research Data

After choosing a topic to research the next step is simply to collect data. Depending on the topic, data can mean different things (e.g. stories, numbers, people). For my topic of choice (Most Expensive Cities in 2016) I will be handling numbers for the most part.

Collecting research data can be broken down into three steps:

  1. Determine what type of data you want to collect
  2. Find and record relevant collected data
  3. Display collected data for review

The first step only requires you to think about your topic and consider what’s relevant to that topic. For my example, ‘most expensive cities in 2016’ can be broken down into four important areas that I should consider collecting data in: rankings, costs, city names and time frame.

With any research project, the initially collected data does not necessarily need to extremely detailed, but rather to provide a good baseline to base your project on.

The second step is probably simplest step, as all you need to do is follow the guidelines you set in step one to look up and record relevant data. However, there are a few important research aspects that should always be considered:

  • Credibility of source
  • Consistency of detail
  • Relevance
  • Objectiveness

Researched data is most beneficial to a research project when it is credibly sourced, is consistent with level of detail, is relevant to your guidelines and shows minimal bias. All of these aspects work together to form great data to be presented.

The third and final step during this phase is to display the recorded data for yourself and or others. This can be done in whatever preferred way, so long as it encapsulates the effort invested in the research phase.

Below is an example Google Sheets doc I put together on my topic, which can also be viewed online here. Here are some screenshots that document my collected research data.


Screen Shot 2017-01-30 at 11.32.05 AM.png


Screen Shot 2017-01-30 at 11.32.48 AM.png


Screen Shot 2017-01-30 at 11.31.54 AM.png

The difficulty in my example involves the fluctuations in costs over 2016 and the difference in ranking styles from my sources. My solution was to put together avg. ranking lists based on the lists my sources published for two unique categories (real estate and cost of living). The result of my solution can be seen in the last image, which is a top 10 list of most expensive cities in 2016 – based on the findings from credible economic organizations.

More detailed research will have to be done to determine actual dollar value averages in these two groupings as my project develops.



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