CHAPTER 3: OVERVIEW OF THE STUDIES AND THESIS STRUCTURE
3.4 Qualitative analysis software
The coding process can be performed by hand, or the coder can use qualitative data analysis software such as NVivo (www.qsrinternational.com) to digitally categorise sections of text. This type of software enables the coder to identify and highlight meanings within the text according to theme and intent, building a database of keywords and ideas that is categorised for easy review. Using this method, the
researcher is able to track nuances throughout the text which allows themes to emerge.
Once the themes are identified, the researcher is able to export a report which shows the number of occurrences for each theme, as well as how much of the text is related to each theme. This delivers a quantifiable view of the tone and meaning of the text. Due to the ability of this software to develop complex and nuanced framework of terms, it was used to perform the analysis of the qualitative data collected in Study 2 - Exploring Parental Use of Game Classification.
3.5 Validity
When conducting a content analysis, the validity of the analysis is crux to the reliability of the outcome. Perception of this validity is dependent on whether the analysis is quantitative or qualitative in nature. Validating a quantitative analysis is straight-forward, in that it lies in the realm of confirming that numbers are correct, repeatable and
reliable, involving processes such as comparison, correlation, and significance. Riffe et al. (2014) discuss four measures of validity of quantitative content analysis as identified by Holsti (as cited in Riffe et al., 2014). The following is an overview of these measures:
Face validity
With face validity, the researcher presents an argument that a measure makes sense when taken at face value. This can have risks, in that when presented in a different light, the same measure may be construed differently. This can be seen with summative content analysis, where the researcher interprets textual content and may derive a measurement based on their interpretation.
137 Concurrent validity
Concurrent validity occurs when previous research suggests a particular outcome, and the quantified results from the current study correspond with this outcome. Concurrent validity can support face validity, adding trustworthiness to the analysis.
Predictive validity
Predictive validity occurs when outcomes correspond with a predicted outcome, lending confidence to the results thus strengthening validity. Once again, this type of validity can support face validity as well as being linked to concurrent validity.
Construct validity
Construct validity measures whether outcomes of the analysis match theory, and whether changing an underlying measure results in expected changes to the outcome.
If change occurs and there is no other cause found for the change than that predicted by theory, then construct validity is supported.
When it comes to qualitative content analysis, validity can be a contentious and subjective term. As discussed in Creswell and Miller (2000), there are many perspectives that have been used in research exploring qualitative validity such as authenticity, adequacy, plausibility, trustworthiness, credibility and verisimilitude. These authors stated that there is such a broad array of terms that there may be confusion surrounding this issue. They then proceeded to discuss that validity of qualitative data can be confirmed using multiple perspectives: that of the researcher, the participants, and reviewers and readers. When looking at it from a researcher’s perspective, Creswell and Miller discuss how validity involves triangulation, disconfirming evidence and researcher reflexivity. An overview of their discussion about these procedures is presented below.
Data triangulation
The process of theming the data exposes recurring themes. The stronger the evidence for the theme, for example a large number of occurrences, the more valid the theme
138 can be considered. Triangulation occurs when the resultant information is
cross-checked with two other distinct sources, such as verifying that identified themes correspond with those identified in previous research.
Disconfirming evidence
In an effort to apply rigour, after triangulation the researcher attempts to disprove the themes identified within the data. This occurs by examining all of the perspectives of a theme to discover information that might discount it, thus rendering the theme invalid.
Researcher reflexivity
The stance of the researcher can influence the context within which they analyse the data. By informing the reader of their beliefs, values and any potential bias they may have, the reader is able to view the results within this context, allowing them to form opinions based on their perception of the researcher’s stance.
Each of these methods offers confidence to the outcomes by applying rigour to the analysis. As the two types of analysis presented in this research employed use slightly different approaches, each will use a combination of these methods in order to
demonstrate validity.
3.6 Summary
In sum, the approach to content analysis depends on the expected outcomes of the research, as well as the type of data being analysed. The method employed to ensure validity is dependent on whether the analysis is quantitative or qualitative, but methods for each type serve the same purpose: to ensure that outcomes of the analysis are valid. The following three chapters present the results of both quantitative and qualitative analyses for both Study 1 and Study 2, with each chapter discussing the approach and validity of the presented analysis.
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