3.9 DATA ANALYSIS
3.9.1 Mixed methods data analysis
In mixed methods research data analysis occurs both within and often between the quantitative and qualitative data, and data analysis relates to the type of design chosen for the procedures (Creswell & Plano Clark, 2007: 135; Creswell, 2009: 218; 2003: 220). In this study the concurrent triangulation design was used, which involved conducting a separate initial data analysis of both quantitative and qualitative data, and comparing the findings from both sets of data during the discussion of findings in the interpretation stage. Creswell (2009: 213) refers to this as “side-by-side integration”. Tashakkori and Teddlie (2003: 35) maintain that the main advantage of using mixed methods lies in the quality of inferences that are made at the end of the study. The term ‘inferences’ can be used by both qualitative and quantitative researchers as it refers to the conclusions that are derived inductively or deductively from the study. Inferences are based on the researcher’s interpretations of the results or outcomes of data collection and analysis.
3.9.1.1 Quantitative data analysis
The quantitative data analysis involved descriptive analysis (Creswell & Plano Clark, 2007: 129) of responses from the Likert-type questionnaire administered. This was effected according to a scale that reflected whether participants agreed, disagreed, or were uncertain with regard to each of the questions (in the form of statements), in order to check for trends and distributions. The data from the 157 questionnaires returned was captured on Microsoft Excel in rows and columns.
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Prior to capturing any data, the hard copies of questionnaires returned were numerically marked at the top right hand corner, and this number (for example, 1, 2, 3, 4, 5 etc.) corresponded with (and hence identified) the number of the participant. The number assigned to each questionnaire and the corresponding responses of participants to each of the questions on the questionnaire were captured in rows. The recording of the responses to the questions on the questionnaire was indicated in columns. Responses fell into Y (yes), N (no), U (uncertain) categories. Through a formula selected on Excel the number of responses in each category for each question was calculated.
Bar graphs indicating responses to each of the 25 questions were drawn up. These graphs were labelled according to the number of the question on the questionnaire and the corresponding question (statement) was inserted into the graph. The quantitative data on each graph were interpreted descriptively as the main purpose of the questionnaire was to gain background information into the life worlds of hearing parents raising deaf children, so that broad trends may be identified. Thus, the analysis of the questionnaire data was used for descriptive purposes, to complement the qualitative analysis in order to gain a better understanding of the meaning attached to participants’ verbal accounts of their parenting experiences. This made it possible to compare and integrate the two sets of data side by side.
3.9.1.2 Qualitative data analysis
Wilkinson and Birmingham (2003: 76) state that qualitative analysis is aimed at capturing the richness and describing the unique complexities of data. De Vos (2005: 334) states that qualitative data analysis “is a search for general statements about relationships among categories of data”. This entails transforming the data by reducing the amount of raw data, sifting out relevant information, identifying significant patterns and developing a framework for conveying the essence of what is revealed in the data (De Vos, 2005: 341;
Flick, 2004: 104; Creswell, 2003: 190; Wilkinson and Birmingham, 2003: 147).
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In this study the process of qualitative data analysis involved transcribing the recordings of the twenty one-to-one interviews which were audio-taped, and carefully scrutinising the transcripts for the purpose of coding, in order to generate categories and themes.
Transcription in itself is a phase of analysis which involves getting closer to the data.
Flick (2006: 288) suggests that if a technical device (such as the audio-tape recorder) has been used to record data then transcription is an essential step towards interpretation.
Several writers (Cohen et al., 2007: 267-268; Flick, 2006: 291; Henning et al., 2004: 162) provide guidelines for researchers with regard to the process of transcribing audio-taped data.
Coding helped to reduce the massive amount of raw data collected, and to focus on the meaning attached to participants’ experiences as hearing parents raising deaf children.
Coding is a process of organising the data into small units or segments of text in order to attach meaning to each segment (Creswell & Plano Clark, 2007: 131; Creswell, 2009:
186; 2003: 192). It entails taking the text data, segmenting sentences and paragraphs, identifying units of meaning, categorizing them and labelling these categories with an appropriate term, and finally putting together similar categories into themes (Neuman, 2006: 460; De Vos, 2005: 338; McIntyre, 2005: 294; Corbin & Holt, 2005: 50; Henning et al., 2004: 105). The aim of open coding is “to express data and phenomena in the form of concepts” (Flick, 2006: 297). Open coding is that part of the data analysis process which entails close examination of the data collected, and assigning of codes which are used for the naming and categorising of phenomena (Neuman, 2006: 461; De Vos, 2005:
340; Corbin & Holt, 2005: 50; Henning et al., 2004: 105). It is the initial basic analytic step to condense the mass of information into categories, which will then inform the rest of the analysis process. The open coding process was used to generate a description of the participants’ experiences, according to categories and themes which are discussed in detail in the next chapter.
In this study the coding process suggested by Tesch (1990: 142-145) was followed in the analysis of the qualitative data. All the transcripts were read carefully to get a sense of the life worlds of hearing parents raising deaf children. Thereafter, one interview transcript
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was selected for closer scrutiny, to obtain an impression of the underlying meaning of the participant’s parenting experience, and points were written in the margin to flag certain thoughts. This procedure was repeated for other interview transcripts, and a list of all the topics was made. Similar topics were clustered together. The topics were abbreviated as codes which were written next to the relevant section in the text. Descriptive words were assigned to topics, which were turned into categories. The list of categories was reduced by grouping together related topics, and labels were assigned to the categories. The different categories were also colour-coded on the transcripts for easy identification. The categories were organised into themes which reflect the major findings. These themes and categories, which appear in table form in the next chapter, enabled the researcher to make sense of the data and attach meaning to participants’ experiences.
3.9.1.3 Concurrent data analysis for the triangulation design
The following general guidelines applied to the concurrent data analysis for the triangulation design (Creswell & Plano Clark, 2007: 136-137):
• Stage 1 involved conducting separate data analysis initially for the quantitative and qualitative sets of data (this was discussed in 3.9.2.1 and 3.9.2.2)
• Stage 2 involved merging the quantitative dataset into the qualitative dataset for the sake of comparison
• The merging of databases enabled the researcher to answer the mixed methods research questions related to the concurrent triangulation design and find answers to the following questions: To what extent do similar types of data confirm each other?
To what extent do the survey results complement the themes emanating from the qualitative interview data? What similarities and differences exist across levels of analysis?
In this study the quantitative data are first presented (c.f. 4.2) followed by the qualitative data (c.f. 4.3). The technique used for merging the quantitative and qualitative data was through a discussion of the findings, and the answers to questions relating to the mixed
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methods concurrent triangulation design are found in the next chapter, under the section dealing with findings and discussion of findings (c.f. 4.3.3). Specific quotations or information about a theme will be followed up by a descriptive statistical result for comparison to either confirm or disconfirm results. Creswell and Plano Clark (2007: 140) state that this approach is used frequently by mixed methods researchers. Two adapted visual models of the triangulation mixed methods design are provided below. The plus sign denotes the concurrent nature of the design. Uppercase letters indicate the dominant method, while lowercase denotes the less dominant.
Figure 3.2 Visual model of Triangulation mixed methods design procedures (Adapted from a 2005 study by Cherlin, Fried, Prigerson, Schulman-Green,
Johnson-Hurzerler & Bradley, in Ivankova et al., 2007: 272)
Figure 3.3 Visual model of Concurrent Data Analysis Procedures in Triangulation Design (Adapted from Creswell & Plano Clark, 2007: 137)
Survey
(N = 157) Individual in-depth
interviews (N = 20)
Descriptive
statistical analysis Coding and theme development
Interpretation based on comparison of quan + QUAL results
Interpretation
Quan QUAL
Stage 1: Separate Quan analyses QUAL and
Quan data analysis:
• Prepare the data
• Explore the data
• Analyse the data
• Represent the results
QUAL data analysis:
• Prepare the data
• Explore the data
• Analyse the data
• Represent the results
Stage 2:
Merge the two datasets
• Merge the quan into the QUAL (Quan + QUAL) to compare results (discussion)
•
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