Slicing the Public Pie: A primer on data representations & issues surrounding their use @HealthCanada Main Menu Introduction coursework, concept of primer, my goal/purpose Data Ideology (AKA Service Standards) ITIL as a professional standard, lead-in to FPS issues The Canadian Federal Public Service intro to the federal public service in Canada Health Canada intro page to HC-related topics FPS 2.0 wrap-up of this scalar book List of Questions Presented questions presented throughout this primer according to subject area References a list of references for works mentioned in this scalar book (APA format)

Perception Bedrock

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So you have a pile of data.  Or maybe you only have two data points. Now you want to figure out how you should respond. Well, what are your goals?  What is your intent?  These are some of the first questions you need to ask yourself in data analysis (although this should have come much earlier in study design). Consider the following example.

Imagine you have the following two data points:How you look at these data points will depend entirely on your theoretical or ideological lens.  If you are concerned about the plight of the homeless, the first point may become invisible to you and the second, a big shock. If you are concerned with the status of the labor market in general, things may be looking pretty good based on the first point alone.

With substantially more data, the implications of your theoretical or ideological lens can become as nuanced as the data is complex. As a result, how data is represented or summarized for you will have an added effect on your response. Being aware of this in advance can better enable digital tools to aid you. But first, which sense would you like the data to be represented in? Click on your choice below.

Sound             Sight

For a general list of perception-based questions, click here.

If you are comfortable with perception issues, you can move on to data ideology considerations.

This page has paths:

  1. Introduction Richard Soulliere

Contents of this path:

  1. Sound Representations
  2. Data Visualizations