3.4 Data Analysis
This study employs both qualitative and quantitative approaches. A qualitative approach emphasizes words rather than numbers, in attempt to
accurately describe and interpret the precise meaning of research focus. In contrast, a quantitative approach emphasizes numbers rather than words
Bryman 2001:20 in Nurrochmat 2005. The analysis and discussions are relied on qualitative analysis. The quantitative analysis is important to substantiate the
discussion, especially to quantify the qualitative data. Different methods of analysis are carried out. The prosess of data analysis is presented in Figure 11.
Questions:
Quantitative analysis of Multi-Criteria Analysis
How is the condition of fishery sustainability?
How is the condition of resource use and
destructive fishery? Quantitative analysis of
index of destructive fishery
Methods of analysis:
How bonding and bridging social capital
influence the way resource is used?
Questions:
How investment in social capital
contribute? Methods of analysis:
Quantitative analysis of index of bonding and
bridging social capital. Qualitative analysis of
building bonding, bridging and linking
social capital.
Questions: Methods of analysis:
How is the condition of coral reef?
Questions: Data on percent
coverage of live coral Methods of analysis:
Resource characteristics
How local rules are enforced?
To what extent social capital investment
shapes local rules and institutions?
Qualitative analysis of institutions and
collective action to: Overcome
destructive fishery Chapter 7.
Manage community marine sanctuary
Chapter 8.
Institution governing resource use
Characteristics of resource users
Chapter 5 Resource
use
Chapter 6
Chapter 7 - 8 Chapter 5
Figure 11 Research process of institutional analysis of coral reef management.
Calculating Index of Destructive Fishery
The outcome that intended to examine in this study is the resource use of the resident fishers. Compliance towards coral reef management is the minimal
use of fishing using bomb or poison, among other things. Therefore, it is important to measure the rate of bomb and poison fishing. This is one of the
challenges during research. Most of all, because the issue is taken in caution by fishers and local community, thus data is not readily and openly available for an
external researcher. Thus, the parameterization is done by using available data that collected in 2004 and 2005.
An index of destructive fishery is calculated from index of 2004 and of 2005. Index for 2004 is taken from the percentage of fishers using bomb or
poison fish. Index for 2005 is calculated from the percentage of responses saying ‘frequent’ use of bomb or poison fishing by resident fishers. The calculation and
result of this analysis is presented in Chapter 5.
Participatory Multi-Criteria Analysis MCA
To assess fishery sustainability, this study uses a formal methodology called multi-criteria analysis MCA. MCA is a general approach that can be used
to analyze complex problems involving multi-criteria Mendoza and Prabhu 2003, and have advantages when applied in a complex and stochastic system
like fisheries Adrianto et al. 2005. This method is suitable for three reasons. First, It can deal with mixed set of data, quantitative or qualitative, including
stakeholders’ opinion. Secondly, it is conveniently structured to enable a collaborative planning and decision-making environment. Finally, it is simple,
intuitive, and transparent, hile it has strong technical and theoretical support in its procedures.
Following Mendoza and Prabhu ibid, MCA is used as a decision-making tool to analyze and evaluate sustainability under a participatory group decision-
making environment. The use of this method can be used for generating criteria and indicators for sustainable resource management, estimating their relative
importance, estimating the performance of each indicator relative to its desired condition.
The analysis using MCA approach is done into two parts. The first part is to generate a set of sustainability indicators of fisheries. The methods used in this
part of analysis are varied, ranging from expert driven and top-down to bottom up, and locally defined Adrianto et al. 2004. This study is following a study done
by Adrianto et al. 2005 and using a mixed-method approach, in which it combines expert-driven fisheries sustainability indicators Pitcher 1999 and then
these indicators are confirmed to the local stakeholders in order to generate a “local accepted” fishery sustainability indicators.
The second part of analysis is evaluating the sustainability indicators in terms of their importance by ranking each indicators using a 5-point scale namely
1 – less important, 3 – moderately important, 5 – extremely important, and 2, 4 – intermediate value. A different scale is proposed by Mendoza and Prabhu 2003
which using 9-point of scale, and Adrianto et al. 2005 used 7-point of scale. However, for reason of simplicity during stakeholder meeting, this study uses 5-
point scale. Based on these rankings, relative weight of an indicator is then estimated using a formula as follows Mendoza and Prabhu 2003; Adrianto et al.
2004:
j j
j
a w
a =
∑
3 where
j
a
is the average weight of indicator j and
j
w
is the relative weight of indicator
j
. The next analysis examined each indicator by judging their current
condition relative to their perceived target or desired condition Mendoza and Prabhu 2004; Adrianto et al. 2005. The desired condition was to reflect or
represent a sustainable status of fishery sustainability indicators. In this respect, an MCA approach of 5-point scale is applied, following Adrianto et al. 2004, with
values 1: extremely weak performance, strongly favorable, 2: poor performance, unfavorable, 3: acceptable, 4: very favorable performance, and 5: state of the art
in the region. Then, the sustainability indicator score SIC is calculated using a formula:
j j
SIC s w
=
∑
4 where
SIC
is sustainability index of criteria
i
ecology, economy, social, and institution,
j
S
is the score of indicator jand
j
w
is the relative weight of indicator j Eq. 3.
Calculating Index of Bonding and Bridging Social Capital
Measuring social capital of the island community can be done by distinguishing their networks into bonding and bridging social capital. Bonding
social capital is strong bonds of social relationships which are endorsed among family members or among members of an ethnic group. Bridging social capital is
weaker but more cross-cutting ties of social relationships, which can be found in relationships from different ethnic groups or acquaintances Grootaert et al. 2003;
Aldridge et al. 1999.
Table 13 Variables of bonding and bridging social capital
Criteria No Variable Operational
definition Attribute Source
of data
Network 1
Source of the island’s
production capital
From where production capital
i.e. financial of fishing or household
enterprise come from
Oneself parents; credit union
ROSCA; island’s patron; patron in
Makassar; outside patron
Household survey
Q1
2 Marketing
of island’s
product Where or to whom
products of fish or household enterprise
fuel, food, etc are sold
Island resident i.e. patron, trader, or
household; Makassar; outsider
3 Membership
in fishing patron-client
network Membership in
fishing patron-client network
Either yes or no Resource
user survey
Q2
4 Visit
out Travel outside island
within the last month for household or
family reason Either yes or no
Group 5 Membership
in local group Membership of
respondent in local groups: credit
union, rotating saving credit
associations ROSCA or sport
Either yes or no
6 Membership
of local group Membership of the
local group Island resident;
resident of neighboring
islands; outsider
Note: Q1 was obtained from Household survey April-June 2004, total N = 3,990. Q2 was obtained from Resource user survey July-October 2005, total N = 102.
The measurement is done by developing a composite measure or an index. It is carried out by specifying different variables related to the attributes of groups
and networks Table 13. Data is derived from the household and the resource user interviews based on structured questionnaires. Groups and networks are
maintained by island residents and fishers. These are networks for obtaining production capital for fishing and household enterprise such as kiosk, fuel
selling, food stall, house or ship builder, as well as for marketing their products. Each variable of networks and groups is then distinguished to each type of
social capital, either bonding or bridging social capital. For example, when the source of production capital is coming from community members in the island or
when the market of the products is sold to island residents, then this network is regarded as bonding. On the other hand, when the capital is coming from outside
the island or the products are sold outside the island, then it is regarded as bridging social capital. The analysis and results are presented and discussed in
Chapter 6.
Statistical Analysis
Statistical analysis is used to assess the relationships between various dimensions of social capital and reef fishery resource use. The model is
proposed as follows:
, ,
Y f SI SO X
=
5 where
Y
is indicator of resource use;
SI
is indicator of input dimension of social capital,
SO
is indicator of output dimension of social capital; and
X
is other indicator. Each of indicator consists of several variables Table 14. Data for
analysis is based on population survey and resource user interview that held in 2004 and 2005 respectively.
The partial results for statistical analysis are presented in Chapter 6 on sections: community trust; vertical bonding social sapital and Chapter 7 on
sections: conservation group; tolerance and fairness; and discount rate. They present some significant correlations between variables that are appropriate for
discussion in the respective chapter. The complete result of statistical analysis is discussed in Prasetiamartati et al. 2006a.
Most of data collected are nominal or ordinal variables, therefore the analysis is using nonparametric statistical analysis. The methods utilized are the
Kruskal-Wallis test, Spearman’s correlation coefficient, and logistic regression. The SPSS Release 11.5.0 software is used to assist in data analysis.
Table 14 Variables for each indicator
Source of data Population survey 2004
Resource user interview 2005 Independent
variables Indicators of
resource use Bombposion fishing
Bomb fishing Poison fishing
Coral taking
Independent variables
Indicators of output social capital
collective action Prohibition of destructive fishing
Prohibition of taking coral Existence of conservation group
Active conservation group
Indicators of input social capital
Network of source of capital
Network of marketing Group membership
Community trust Mutual help
Community engagement Participation in community meetng
Tolerance towards destructive
fishing Fairness
Agree on sanctioning destructive fishing
Other indicators: 1 Government’s
functioning Law enforcement
Benefit of external assistance 2 Socio-economic
condition Education
Age Ethnic
Alternative livelihood 3 Resource
perception Crisis perception on fish
Crisis perception on coral reef Perceived benefit on fish
Perceived benefit on coral reef Perception on changes on fish size
The Kruskal-Wallis H test, an extension of the Mann-Whitney U test, is the nonparametric analog of one-way analysis of variance and detects differences in
distribution location. It is used in order to determine the influence of independent variables on dependent variable. The Kruskal-Wallis H test is used for data with
several independent samples:
2
12 3
1 1
i i
i
R H
n n n
n =
− +
+
∑
6 where
H
is Kruskal-Wallis coefficient,
i
n
is number of observation on sample
i
, while
i
is number of data on each sample, and
i
R
is rank on sample
i
. The Spearman’s correlation coefficient rho is a measure of association
between rank orders, which is used to measure the association between dependent variable
i
Y
and independent variables
i
X
.The p-value shown is for
testing the null hypothesis that no significant trend exists. If the p-value is 0,05 then the Spearman’s correlation coefficient r
s
is significant. The Spearman’s correlation coefficient is calculated using a formula:
2 1
3
6 1
n i
s
di r
n n
=
= − −
∑
7 where r
s
is Spearman’s correlation coefficient,
i
d
is the difference between two ranks
xi
r
-
yi
r
,
n
is number of samples. Finally, in order to develop a model of fishery resource use that
incorporated social capital, an analysis using logistic regression is carried out. Logistic regression is useful for situations in which you want to be able to predict
the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to
models where the dependent variable is dichotomous SPSS Inc. 2002.
Chapter 4 General Conditions of Study Sites