PLANIFICACIÓN DE LA RESPUESTA A LOS RIESGOS

Raw data itself is not of great use to a researcher. It needs to be analysed be- fore any meaningful results can be extracted from within its morass. Curi- ously enough, the approach for analysing collected data was not explained

by the creators of PADR. It can, however, be extrapolated based upon the work which was used to create PADR in the first place and upon related research approaches used by similar research projects under the HCI ban- ner.

The possible ways to analyse data are many and each has its own strengths and weaknesses as well as a multitude of variants and sub-variants upon the same base idea. One of the more common is a collection of techniques grouped together under the banner of Grounded Theory (Strauss and Corbin 1990). Grounded theory allows a researcher both to pare the raw data into amounts that can be more easily understood and to provide ways to derive useful insights from the newly summarised data. This approach has been seen as unnecessarily complex for HCI research (Paay et al. 2009). In this work however, the extra complication is considered to be worth the time.

The myriad methods encapsulated within grounded theory and its many variants (collectively called Grounded theory Methods or GTM) provides a strong explanatory narrative from the data, enables the researcher to see how the different components relate (or not relate, as the case may be), and also allows the researcher to extract multiple perspectives from the data (Braun and Clarke 2006, Boyatzis 1998). GTM usage in computing re- search, such as this work, will generally follow a standard approach. First, the domain and data types are identified and the relevant data is collected (Muller and Kogan 2010). The data will then be transcribed and the re- searchers will spend a significant amount of time reviewing the data, in order to become familiar with it. Themes, codes, and categories from the data are then iteratively analysed and identified. The different categories are then related to one another. From this the structure of the data can be found.

3.4. PARTICIPATORY ACTION DESIGN RESEARCH

Coding is the identification and extraction of key pieces of information from the data, and there are many different ways of coding data and each has its own strengths and weaknesses.

One of the more common, and often the first analytical step in many GTM, is open coding. Open coding allows the researcher to begin forming concepts within, and to grasp the different aspects of, the data (Strauss and Corbin 1990). The codes are developed and identified based upon their properties and then iterated upon; collections of codes are found and merged together based on their similar thematic elements. This process is repeated numerous times until the researcher has reduced the initial data to a level from which they are capable of deriving a structure.

It is worth stating that GTM is not an off-the-shelf methodology and that the individual tools and methods encapsulated within GTM each have their own effective uses outside of the banner of grounded theory. As said by Strauss and Corbin (1990, p.306), when discussing open coding within grounded theory:“Although if your purpose is just to pull out themes, then you could pretty much stop here.”

Due to the open nature of using GTM, it makes them ill-suited for ex- ploring hypotheses. Rather, they are by design intended to allow researchers to create hypotheses from data (Suddaby 2006). GTM gives researchers a means to approach data and understand their data; they do not tell re- searchers how that data can or cannot fit into an existing concept. This makes GTM inductive methods, and any research using them must there- fore also be inductive (Suddaby 2006)

The approach that this work takes follows a fairly common approach often used in HCI research fields. The data types and domain were known in advance, then data was collected and codes and themes were identified through an iterative approach. This does not align perfectly with the tradi-

tional grounded theory approach described by Glaser and Strauss (1967), but is still a good match for the goals of the research and allows for an in- depth exploration of the data. The remainder of this section will discuss how the data collected during the research was analysed using a GTM ap- proach, covering the three steps to be used in all phases of this research: data familiarisation, data coding, and finally grouping of the codes into themes. These three steps are based on the work of Strauss and Corbin (1990).

Data familiarisation

For a researcher to be able to extract any useful theories and information from raw data, they are required to be familiar with that data. To further that goal, researchers should spend a significant amount of time reading and then re-reading the data. This should continue until the researchers feel they are comfortable with, and have a proper understanding of, the data at hand.

The purpose of this familiarisation is not as an aid to memory; it is to give the researchers grounding which they can then use to begin coding the data. Without this grounding, coding the data would very likely be a much slower process as the intricacies of the data itself would not be as well understood. Additionally, without the grounding the coding would also likely result in different codes being identified, as without a solid un- derstanding of the data, reading the data whilst trying to code it would result in frequent back and forth shuffling in an attempt to comprehend how one word or phrase can be seen in the context of the others.

In the case of this work, the author extensively read and then re-read the results of the survey and the transcripts of the interviews multiple times over a period of a week following the conclusion of the data collection.

3.4. PARTICIPATORY ACTION DESIGN RESEARCH

During this process some simple notes were taken based on the author’s thoughts on the interview and survey data, however, no coding took place during this period. Once the author felt that the data was sufficiently fa- miliar, the next phase, coding, was undertaken.

Data coding

The many different approaches to grounded theory make use of many phases of abstraction from the data. As stated by (Strauss and Corbin 1990), open codes by themselves are useful in the early stages of explor- ing a subject. For this reason, open coding was used in this work and were then refined into the themes discussed later in Section 4.3. Theopen part of open coding refers to the codes being identified during the many passes through the data; there were no codes for the data in existence before the coding began. The process of creating the codes was iterative, where the data from both the survey and the interview were reviewed, with repeated and important terms being identified and marked up as a code. Figure 3.2

Me: You're from out of town then?

Participant: From Melbourne, the girlfriend had a week off, and it's a nice place that is close.

Me: Have you used your phone at all today?

Participant: Yeah, just to call each other to see where we are.

Me: You haven't used it for anything else?

Participant: No, we are not really into tech all that much. We do have phones for obvious reasons.

Me: Obvious reasons?

Participant: I use for research, if I'm going to buy something I will google it. Oh and for directions.

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shows an example of how the codes were created from the raw data.

Data themes

After the initial set of codes were identified, the individual codes are grouped together by their similarity with each other. These groups of codes became the themes (those created in this work are discussed in Sections 4.3.1, 4.3.2 and 5.7).

3.5 PADR andthiswork

At its core, urban informatics is a field still heavily tied to the comput- ing discipline and as such it has the more traditional desire to use tech- nological artefact to fixa problem. This of course assumes that there is a problem needing to be fixed. This research does not subscribe to this be- lief; it takes the approach that a place can exist without a problem. This work does, however, take the view that a space can still be positively en- hanced through the addition of technological artefact(s) and because of this a methodological approach attempting to understand a space and then fix the problems within it has still has validity for this research. PADR is also quite well suited to support the impact of technological artefacts in that it covers the gamut of insider and outsider views as means of inquiry (Ev- ered and Louis 1981, Brooks and Alam 2013), allowing support for both controlled experiments as well as deep ethnography in the one research project.

3.5.1 Diagnosing and Problem Formulation

As stated earlier, the first phase of the PADR approach is to try to both diagnose and understand the space and any issues that might exist within

3.5. PADR AND THIS WORK