Data Analysis and Interpretation

The process of data analysis for the qualitative and quantitative is discussed below.

6.3.7.1 Qualitative data

The approach to data analysis and interpretation was to integrate the findings from multiple-case data and map general models of the variables specifying connections between them. The standardisation of data through the use of matrices facilitated cross-case comparisons. Cross-case analysis was useful in enhancing the generalisability of results, and deepening understanding and explanation of findings (Miles and Huberman 1994).

The general process of mapping models was developed through the steps described by Miles and Huberman (1994) and Patton (1990). These steps were: (a) Making comparisons and contrasts – comparing cases to identify

similarities and differences in the way processes were defined. In doing this, the researcher was searching for those that had internal convergence and those with external divergence. Internal convergence refers to the extent to which the data belonging to a particular category hold together in a meaningful way. External divergence refers to the extent to which the differences among categories are bold and clear.

(b) Finding intervening variables responsible for presence and/or relationships between variables – identifying the internal and external environment factors that had an effect on process definition and outcomes.

(c) Considering which variables might reasonably be expected to have a direct impact on other variables both preceding them in time and having a plausible direct connection – determining the effect of each intervening

variable on processes, by checking respondents explanations for the causal linkages they perceive to exist;

(d) Testing emergent themes and suppositions by searching for rival explanations and negative patterns from respondents about causal connections.

(e) Searching for alternative explanations involved identifying alternative explanations to the emerging patterns, clarifying any assumptions made and demonstrating how the final explanations offered were the most plausible ones.

Working through these steps progressed the study from describing processes to determining the contribution of different process steps to process outcomes, and from identifying the intervening variables to determining the effect of each intervening variable on process outcomes. Through discussions and building up of logical chains of evidence, the result was a coherent conceptual presentation that demonstrated the relationships between variables (Miles & Huberman 1994).

To aid in the analysis process, various visual representation tools such as flow charts, organisational charts, context charts, matrices, data maps and causal models were used (Robson 1993; Miles & Huberman 1994). Context charts attempt to map out the relationships among variables that make up the context of organisational behaviour. Matrices included the case-ordered effects matrices that sort out the cases by degrees of the major variables being studied, and showing the effects for each case. Causal models show the connections between variables. All these display formats aimed to reduce the data and draw logical patterns and themes (Miles and Huberman 1994).

Given that the study was exploratory in nature, and the fact that the research project may be classified as small-scale, with eleven cases of qualitative data, a word processor with its features of easy coding, retrieval, searching, revision of data and graphic displays, was considered appropriate and sufficient for analysis (Fielding 1993; Miles and Huberman 1994).

Secondary data gathered from respondents in the form of annual reports, brochures, newsletters, bulletins and web site information was content analysed to identify themes that built on or opposed those obtained from the interviews. The content analysis was therefore restricted to issues that were relevant to the study, and contributed further in identifying explanations to emerging patterns and development of theory.

6.3.7.2 Quantitative data

Quantitative data were analysed using Statistical Package for Social Sciences (SPSS) to provide frequency distributions, percentages, cross-tabulations and correlations between variables. The frequency distributions and percentages were useful in summarising the data and describing observations. The cross- tabulations displayed the number of cases falling into each combination of the categories of two or more categorical variables, while the Pearson correlation was useful in measuring the directional relationship between two variables of linear association. One-tailed tests were used to measure the directional relationship, at both the 0.01 and 0.05 significance levels (Coakes & Steed 1999). The use of statistics provided for the necessary explicitness hence giving greater protection against bias in the interpretation of qualitative data (Robson 1993). Quantitative data analysis was therefore limited to the extent that it provided objectivity to the qualitative data.

With the help of SPSS, data were sorted, searched and recoded to allow for data exploration. Charts and tables were produced and emerging themes tested using the “what if” option that allowed for data to be arranged in different sets, such as by ‘years of operation of the organisation’, or ‘size of organisation’ (staffing, outreach and/or annual budget). The process of recoding and reorganising data allowed for comparisons between different sets of data leading to new themes being developed and continually tested (Leedy & Ormrod 2001).

The interpretation of qualitative and quantitative data was carried out simultaneously, generating meaning and drawing conclusions from the findings

and analysis. This was done by noting patterns and themes, seeing plausibility, clustering similar data, making comparisons and contrasts between cases, noting relationships between variables, and finding intervening variables. An understanding of the data included building a logical chain of evidence and making conceptual/theoretical coherence (Miles & Huberman 1994).