Step Five: Data analysis stage

According to Whittemore & Knafl (2005: 550), data analysis in research reviews requires that data from primary sources are ordered, coded, categorised and summarised into a unified and integrated conclusion about the research problem; a thorough and unbiased interpretation of primary sources, along with an innovative synthesis of the evidence are the goals of the data analysis stage.

In the integrative review method, the approach to data analysis is compatible with the use of varied data from diverse methodologies. Webb & Roe (2007: 152), and Whittemore & Knafl (2005: 550), suggested that primary research methods of analysis, developed for mixed-method and qualitative designs, are particularly applicable to the integrative review method because they allow for reproducible comparisons across primary data sources.

The process of data analysis in this review included using a constant comparison method as recommended by Whittemore & Knafl, (2005:550). Extracted data was compared item by item to categorise and group similar data through the identification of patterns, themes, variations and relationships. The coded data was compared, analysed further and synthesised using the method recommended by Miles & Huberman (1994:11), which consists of four phases: Data reduction, Data display, Data comparison, Conclusion drawing and verification phase.

Each of the data analysis phases is described below.

Data reduction phase

The first phase of data reduction involved the determination of an overall classification system for managing the data from diverse methodologies.

Data was divided into subcategories according to the nursing process’ assessment, diagnosis, interventions and evaluation steps in answering the secondary review questions. Studies were grouped into a hierarchy of evidence as per DiCenso, et al., (2005:33). The primary sources included in the integrative review were divided into subgroups according to a logical system to facilitate analysis. The initial subgroup classification was based on the type of evidence and analysed sequentially (that is, examining all qualitative or descriptive studies on the topic, then the correlation or comparative designs and lastly any intervention or experimental designs) and analysed by topic.

68 Subsequently data reduction involved techniques of extracting and coding data from primary sources to simplify, abstract, focus and organize data into a manageable framework. Reliable and valid coding procedures are essential to ensure methodological rigour (Brown, Upchurch & Acton, 2003: 206). Predetermined and relevant data of each subgroup classification were extracted from all primary data sources and compiled into a spread sheet (Whittemore & Knafl, 2005: 550; Miles & Huberman 1994:11). Thus, each primary source is reduced to a single page with similar data extracted from individual sources (of each subgroup classification). This approach provided concise organisation of the literature, which facilitated the ability to systematically compare primary sources on specific issues, variables, or sample characteristics. Categories that were extracted using the worksheet included the definition of wound management, aspects of wound management such as infection, healing and pain. An appraisal worksheet designed for systematic reviews was used to extract data from the studies (Refer to Appendix P).

Data display phase

The next step in data analysis was data display, which involved converting the extracted data from individual sources into a display that assembled the data from multiple primary sources around particular variables or subgroups. Data were displayed in the form of a table (Appendix R) and set the stage for comparison across all primary sources.

The display enhanced the visualisation of patterns and relationships within and across primary data sources and served as a starting point for interpretation (Whittemore & Knafl, 2005: 551). Different data displays were used for each subgroup classification of the integrative review.

Data comparison phase

The next step in data analysis was data comparison; which involved a reproducible process of examining data displays of primary source data in order to identify patterns, themes, or relationships. From the emerged patterns a conceptual map was drawn that included a majority of the variables or identified themes (Whittemore & Knafl, 2005:551). Similar variables were grouped close to one another and a systematic order was displayed (if appropriate).

Relationships were depicted between the variables or themes. This process of data visualisation and comparison provided some clarity to the empirical and theoretical support emerging from early interpretive efforts.

69 Creativity and critical analysis of data and data displays are key elements in data comparison and the identification of important and accurate patterns and themes according to Whittemore & Knafl (2005:551).

Elements of data analysis were based on Miles & Huberman (1994:11) which included noting patterns and themes; seeing plausibility; followed by clustering, counting, making contrast and comparisons. The researcher also discerned common and unusual patterns, considered particulars in general and noted relations between variables. Finally the researcher sought to find intervening factors to build a logical chain of evidence (Miles & Huberman, 1994:11).

Conclusion drawing and verification phase

Conclusion drawing and verification was the final phase of data analysis that moved the interpretive effort from the description of patterns and relationships to higher levels of formulating comprehensive concepts by extracting common qualities from specific examples and incorporating the particulars into the generalised (Whittemore & Knafl, 2005:551).

Patterns and processes were isolated, commonalities and differences were identified with a gradual elaboration of a small set of generalisations that encompassed each subgroup database of the integrative review in its entirety. Miles & Huberman (1994:12), advocate the continual revision of the guidelines (conclusions or conceptual models) in order to be inclusive of as much data as possible. The authors propose that all discernment of patterns, themes, relationships, or conclusions require verification with primary source data for accuracy and validity.

Whittemore & Knafl (2005:551), stated that explicit care needs to be undertaken during this process to avoid premature analytic closure (being locked into a particular pattern) or exclusion of pertinent evidence. Addressing conflicting evidence is a considerable challenge, particularly when results are equally compelling and from high quality reports. Vote counting, is proposed as one strategy to categorise and analyse conflicting results, comparing the frequency of significant positive findings against the frequency of significant negative ones. Exploration of confusing influences contributing to variability in findings (that is, sample characteristics) can also be considered. However, conflicting evidence in general, demonstrates the need for further research with the subsequent research question and design aimed at resolving the conflict. The need for this had been identified in the conflicting information on the safety of silver dressings. This subsequently led to a further review question for future research: Are silver

70 On completion of each subgroup analysis, a final step of the data analysis in an integrative review is the synthesis of important elements or conclusions of each subgroup into an integrated summation of the topic or phenomenon (Whittemore & Knafl, 2005:551). A new conceptualisation of the primary sources integrates all subgroups into a comprehensive portrayal of the topic of concern, thus completing the review process.

A record was kept during the entire process of data analysis which documented data analysis decisions, analytical hunches, thoughts, puzzles, alternate hypotheses, or any idea that may be directly related to the interpretation of data (Miles & Huberman 1994:12). Analytical honesty was a priority; the data analysis process is made transparent with rival explanations and false relationships thoughtfully explored.

Whittemore & Knafl (2005:550), suggested that in a review that encompasses theoretical and empirical sources, two quality criteria instruments could be developed for each type of source and scores could be used as criteria for inclusion/exclusion or as a variable in the data analysis stage.

Conclusion drawing and verification moves the interpretive effort from the description of patterns and relationships to higher levels of abstraction, subsuming the particulars into the general. Patterns and processes were isolated and commonalities and differences were identified, with a gradual elaboration of a small set of generalisations that encompassed each subgroup database of the integrative review in its entirety. Conclusions or conceptual models that are developed are continually revised in order to be inclusive of as much data as possible (Miles & Huberman, 1994:11).