A Local-State Government Spatial Data Sharing Partnership

Chapter 6 – Results of Partnership Case Studies at Local Government Level
satisfaction and improvements in data quality. In other instances variations appear to be
more related to the size or composition of the jurisdiction. The relationship between
variables such as pricing and access policies, size, existing collaborations, organisational
change, training and the outcome variables therefore deserve further attention. In order to
investigate these relationships, the next section of the chapter examines the use of factor
and regression analysis to explain these relationships.
6.4 Factor and Regression Analysis
6.4.1 Overview of Process
The purpose of the factor and regression analysis was to identify which variables or groups
of variables were contributing towards the success of the data sharing partnerships and
hence SDI development. Figure 6.23 illustrates the analysis process.
Figure 6.23 Factor and regression analysis process
The analysis consisted of a three stage process which commenced by using exploratory
factor analysis to reduce the identified 36 variables from Parts 1-7 of the questionnaire to
13 factors. These factors reflect the capacity of the LGAs in each of the extended SDI
framework components. Variables from Part 8 were also reduced down from six outcome
variables to a single outcome factor.
The second stage of the process was then to examine the relationships between the 13
factors to identify any important trends. The process of grouping factors often creates
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