Los conceptos de campo y habitus de Bourdieu

In QCA, the term “text” is often used as a broad term to mean all types of qualitative material. Hence, QCA is considered to be an effective approach for analyzing texts whose meaning is less discernable or uniformly agreed upon (Scherier, 2012, pp. 2-3), or in which there is an abundance of rich material with many conceptual layers. However, in order to analyze the meaning within these conceptual layers, the researcher first has to collect and organize the embedded data. Therefore, in QCA, data collection can involve a number of steps.

In Phase I of this study, data collection involved three parts. First, it involved turning raw material from the determined unit of analysis (Scherier, 2012, p.) into relevant data or units of

primary units of analysis were, (a) The body of documents found in the professional literature and, (b) The body of apps, as multi-modal texts. While, at times, a single unit of analysis can contain several units of coding- the nature of which can vary depending upon the subcategory considered by the researcher at the time (Scherier, 2012, p. 132)- the unit of coding did not vary within this study. The same unit was examined within each of two dimensions of my Coding Frame. Within both dimensions, the unit of coding was “curricular characteristics” embedded within the literature and the apps, respectively. Identifying instances of this unit of coding (i.e., Instances of curricular characteristics) within the literature and apps was accomplished by utilizing contextual units (Scherier, 2012, p.), found within the literature and apps.

Second, I organized these curricular characteristics into the distinct structure of the Coding Frame. This resulted in Version 1 of the Coding Frame (see 3.3 The Coding Frame, above, for the specific procedure of coding frame development). Third, I “pilot tested” the Frame by applying it to each app. The purpose of this was to adapt the frame to reflect any ideal

curricular characteristics that may have been present in the apps, but not within the literature (the data-driven content). I also drew upon the assistance of volunteers- namely, other early

childhood education researchers and mathematics education researchers, whose educational philosophies primarily match my own (i.e., A team of individual “frame generators”), in order to assist me in identifying any curricular characteristics I may have missed (both from the literature and from the apps). They also assisted me in editing the structure of the Coding Frame. Finally, I made changes to the content and structure of the Frame, as recommended, and evaluated its quality (see Evaluating the Coding Frame, below).

By utilizing volunteers to assist me generating and organizing categories of the coding frame, and through my own multiple iterations of Frame editing (as commenced over an

extended time period), increased the validity of the Frame categories and descriptors, through triangulation. Triangulation is the use of multiple forms of data collection, data sources, theories, and analysts in order to corroborate evidence for the validity of qualitative research findings (Dedrick, personal communication). Since the Coding Frame contents represented the “findings” from Phase I of this study, triangulation was an effective means for increasing validity.

In Phase II of this study, data collection involved three main parts- download, data collection, and “segment(ation)” (Scherier, 2012, p.). First, since the nature of apps and their subsequent curricular components can be altered at any moment [by their designer(s)], it was important to “preserve” this raw material by downloading all apps at a single point in time (for all coders). Next, there were two sets of curricular characteristics I needed to segment and collect. Since the primary unit of analysis is based on the kinds of categories generated by the researcher for the coding frame, my study aimed to compare the “ideal” curricular characteristics with those of real apps. Hence, I provided space for all four of these processes by creating an

App Observation and Classification Form, on which all data could be collected and segmented.

As such, one space on the Form is for “Observational Notes”. In this space, coders can record their observations of the app during play and participation. Specifically, coders were guided to: (a) Tour the environment and programmatic features of the app for approximately 20 minutes, and play the major individual learning activities within the app and, (b) Look for curricular characteristics of the app and record these observations in bulleted form within the “Observational Notes” section of the App Observation and Classification Form. After this, coders were asked to classify their observations according to the categories of the Coding Frame, which also were provided on the form. Any observations that were “leftover” were assigned to a

“Miscellaneous” category. In other words, the space for “Observational Notes” secured a place for coders to collect and segment data of the real app and classify it under the categories of the Coding Frame.

The second space on the form is called, “Guiding Questions”. I created these questions based directly on the categories of the Coding Frame. They outline the “ideal” curricular characteristics, as informed by the concepts of the Literature and data of the three apps, and essentially translate the content of the Coding Frame, into a form that is more “coder-friendly”. Using these two aspects of the Form, in tandem with one another, assisted me (and other coders) in, (a) Determining relevant and irrelevant material in the app, (b) Segmenting the relevant data from the app, into units of coding and, (c) Collecting and classifying the data from the app, according to the categories of the Coding Frame.

Since the modes of the app-texts are auditory, visual, kinesthetic, conceptual, and temporal (i.e., multi-modal), utilizing a template (i.e., The App Observation and Classification

Form) that asked the coding team to gather relevant data through four collection techniques

(participant observation, screen shots, a written description of key elements, and answering conceptual questions) increased the validity of the data through triangulation. As well, using multiple collection “instruments” (i.e., Individual members of the “coding team”), and multiple collection time periods (i.e., Two distinct times, 14 days apart), also increased the validity of the data collected.

Thus, to collect data for this study, coders and I abided by the following procedures: 1. I downloaded the selected apps onto one iOS mobile device. (Each coder

downloaded these apps on the same day as one another, on their respective devices.);

2. I participated in each learning experience offered by the app, and explored the program and virtual environment within each app, to develop a broad sense of the material;

3. I applied Version 1 of the coding frame (i.e., The concept-driven frame, based solely on literature) to determine the extent to which this frame reflected all relevant material across the three apps. This helped me identify all relevant material from each app, and ensure it became part of the structure and substance of the coding frame. I, then, made adjustments to the coding frame, so it reflected additional data-driven material, not originally included in Version 1 of the frame. This resulted in Version 2 of the coding frame. In this way, this process helped me “overcome the shortcomings of (my own) everyday understanding” (Scherier, 2012, p. 5) by generating a coding frame that was both data-driven and concept- driven (Schreier, 2012, p. 33);

4. I also amended my Literature Review to include these additional data-driven ideas;

5. I created the App Observation and Classification Form as a data collection tool; This form reflects the content from Version 2 of the Coding Frame, but organizes it in a format that makes it easier for coders to locate relevant data within the app, and classify the data under the subcategory he or she determines most fitting; 6. I utilized the App Observation and Classification Form for each of the three

identified apps. This form accomplishes multiple functions. First, it offers a uniform format for observing, participating in, and describing the app. This section of the form asks the coder to, (a) Tour the app and participate in the

learning experiences of the app for approximately 20 minutes, in order to become familiar with its curricular characteristics, (b) Capture digital screen shots during tour and participation and, (c) Take “observational notes” to describe the features of the app and its curricular characteristics, such as characters that are utilized, activity objective, and a summary of the activity. Second, this form offers “Decision Rules” (from the coding frame) in the form of “Guiding Questions” which, when answered by the coders, helps him or her determine the subcategory under which the data from the app should be classified. Third, the form offers a place for the coder to mark his or her decision about where data should be classified. This allowed me to catalogue my unit of coding (i.e., Curricular characteristics of the app) as occurrences of the categories of my coding frame (Scherier, 2012, p. 1);

7. I completed the App Observation and Classification Form twice for each app, with a 14 day separation between the first and second application, as

recommended as a minimum by Scherier (2012). After data was collected and classified, I analyzed it.