Skip to content

Latest commit

 

History

History
66 lines (42 loc) · 4.46 KB

07-more-stages-of-data-analyzed.md

File metadata and controls

66 lines (42 loc) · 4.46 KB
Raw

← More Stages of Data: Cleaned   |   More Stages of Data: Visualized →

7. More Stages of Data: Analyzed

Analysis can take many forms (just like the rest of this stuff!), but many techniques fall within a couple of categories:

Descriptive Analysis

Techniques geared towards summarizing a data set, such as:

  • Mean
  • Median
  • Mode
  • Average
  • Standard deviation

Inferential Analysis

Techniques geared towards testing a hypothesis about a population, based on your data set, such as:

  • Extrapolation
  • P-Value calculation

Qualitative Analysis

Techniques geared towards understanding a phenomenon, rather than predicting and testing hypotheses, such as:

As we have discussed thus far, data are not neutral or objective. They are guided by and produced through our interests and assumptions, often shaped by our socio-political contexts. Hence, we must also understand that the forms of analyses we take to our data further shapes how we are choosing to tell the story. We are crafting a narrative through each of the stages of data that helps us communicate our projects to a wider audience. This is not to say that our analyses are not "empirical" or "scientific" but a suggestion to make transparent the theoretical foundations and perspectives that are guiding our interpretations. For a more nuanced perspective, consider The Numbers Don't Speak for Themselves in Data Feminism.

Evaluation

Descriptive analysis help us summarize a data set.

  • True*
  • False

Challenge: Analysis

  1. As we consider the types of analysis that we choose to apply onto our data set, what are we representing and leaving out?
  2. How do we guide our decisions of interpretation with our choices of analyses?
  3. Are we comfortable with the intended use of our research? Are we comfortable with the unintended use of our research? What are potential misuses of our outputs?
  4. What can happen when we are trying to just go for the next big thing (tool/methods/algorithms) or just ran out of time and/or budget for our project?

Solution:

  1. I may choose to leave out data that are perceived to be outliers, especially if they differ to much from the "normal" curve. I end up representing only those who fall within the "normal" curve which may not actually be an equitable representation.
  2. The interpretation of the results should align itself with the type of analyses that I ran. In addition, it should be guided in some capacity by previous work in this area to inform my understanding.
  3. Potential misuse that I am always concern with is the weaponziation of marginalized participants' words and thoughts. I think I remain somewhat uncomfortable with the unintended use of my research because I don't think I can ever consider every circumstances that the analysis can be misused or misquoted. When I was working on an oral history project, I have set up some layers of boundaries to prevent too easy of an access to audio files as an attempt at negotiating access and protection of my narrators.
  4. In chasing the next big thing, the original intentions for beginning the project might be lost. For me, making sure that my work is meaningful to my communities is important and the excitement of exploring a new tool can sometimes distract me from this intention. Running out of time and/or budget can also mean that the project may end abruptly, and relationships built could be strained in a haphazard wrap up. This brings me back to making sure that before the project begins to spend a significant amount of time on project planning to reduce the chances of this happening.

Keywords

Do you remember the glossary terms from this section?

← More Stages of Data: Cleaned   |   More Stages of Data: Visualized →