RESEARCH METHODOLOGY
2.13 Method of Data Analysis
A data analysis sheet was prepared using data extracted from the returned questionnaires A,B and C. The scale of data obtained was nominal, ordinal, ratio, and interval. The data was analyzed with Epi-info statistical software version 3.5.1.and SPSS-15.The Variables were presented by frequency tables, pie charts, bar charts, and cross-tabulations. Odd ratio with 95%
confidence interval and Fisher’s exact test were used to test the associations between the essential elements of Housing Delivery System (HDS) and the possible determinants as well as the desirable factors.
The use of Fisher’s exact test was against the backdrop of similar studies already sighted in this research, which were empirical and theoretical in the test association for significance among variables.
Table 2.7 List of independent variables
Serial
Nos. Code Variables Serial
nos code Variables
1 A1 Tenure 47 C6 Perception of PPP
2 A2 Age 48 C7 PPP preferences
3 A3 Gender 49 C8 Building regulation and standards
4 A4 Marital status 50 C9 Perception of single family housing
5 A5 Dependants’ categories 51 C10 Current Role of professionals
6 A6 Household size 52 C11 Development criteria
7 A7 Level of education 53 C12 Impact of design/layout typologies on HDS
8 A8 Monthly income level 54 C13
9 A9 Source of income 55 C14
10 A10 Employment type
11 A11 Work related stay in Lagos
12 A12 Religion
13 A13 State of origin
14 A14 Dwelling layout
arrangements
15 A15 Dwelling design typology
16 A16 House acquisition
17 A17 Acquisition costs
18 A18 Process function
applicability
19 A19 Impact of building
regulation
20 A20 Effect of Process cost 21 A21 Life style-size assessment 22 A22 Perception of Housing
environment
23 A23 Estate size
24 A24 Material quality assessment 25 A25 Design Quality assessment 26 A26 Organizational
arrangements
27 B1 Name and location of the
estate
28 B2 Estate typology
29 B3 Age of the estate (periods are representative of policy epochs)
30 B4 Age of the firm
31 B5 Tenure of estate
32 B6 Infrastructure provision by
public sector
33 B7 Access to land
34 B8 Location
35 B9 Cost of foreign exchange
36 B10 Source of funding
37 B11 Actors profit motive
38 B12 Actors/partners
commitment
39 B13 Construction cost
40 B14 Level of technology
41 B15 Government policy
requirements
42 C1 Social Sector of partners
arrangement
43 C2 Partners profession
44 C3 Professional experience
45 C4 academic qualification
46 C5 Involvement
(Source, Author)
The data in relation to each objective were then analyzed as follows:
Objective - 1
The results was tested for determinants of the housing delivery systems(HDS) with the Fisher’s exact test; in terms of Housing Design preference/taste, provision of infrastructure, Household income, levels of building activity regulations, tenure, process costs, and typology (layout/design).
Objective-2
The determinants of actors/partners arrangements in the housing delivery systems of Lagos in each of the four-estate typologies were tested with the Fisher’s exact test: Then, reduced to two for computation of the odd ratio. This was in order to find out if there are any association between partners’ arrangements and the housing delivery system of choice among housing development actors/partners between the public and the private: And to find out if there is any association between levels of partners’ commitment.
Objective-3
The data obtained was investigated to see if there was any significant difference in the perception of housing development experts, housing development actors/partners, and household. In terms of major preferences, tastes, and lifestyle that influences the housing environment; this was achieved using the Fisher’s exact test; and if there are major differences in the objectives of actors/partners, their housing delivery systems and architectural typology.
Objective-4
The data obtained analyzed to find out the main determinants of public-private partnerships in housing delivery:
Objective-4 was further investigated through the development of predictive models for evaluating future levels of adequate housing delivery among actors/partners. Significance levels of P=0.05 was the datum and variables which failed to meet this level were removed from model. The remaining variables were used in developing Adequacy evaluation techniques (AET) as a predictor model for PPP and adequate Housing Delivery(AHD).The overall significance of the model was tested using the Fishers exact p-value: By using the standard error of estimate, residual analysis and exponential smoothing with trend adjustments, the reliability and accuracy of the models were improved.
2.13.1 The Basic of GST Linear Equations 1. The Equations
This research generally based its form of GST Linear equation as follows;
Y=a+b1,x1+b2x2+…bk xk
Where,Y is the dependent variable to be estimated,x1 x2…..xk are the (k) independent variables; ‘a’ is the intercept value that reveals the Y predicted value when x=0
The corresponding coefficients for the K independent variables are b1,b2,…bk
It is the partial change in b1, which signifies a change in the Y value, and this corresponds to a change of a unit in x1 where x2…xk remain constant.
In essence, when applied to HDS, Consider equation (1)
H=P+T+A=X Or
H= ἰ………..(2)
Where X1=P, X2=T and X3=A and H is HDS, P is PPP, T is Housing layout/design typology and A is AHD.
Therefore the sum of the variables determining HDS, is equal to the sum of the variables determining,PPP,AHD which includes the housing layout/design typology.
This relationship exists and can be measured using simple to complex GST Linear equations, which creates the platform for evaluating the entire HDS yet capturing its integral elements.
2.13.2 Predictive Linear Models (of HDS, PPP AHD)
Based on the works of past research in the field of general systems theory (GST), a linear model that describes the relationships between HDS, PPP, and AHD is proposed for a better understanding of this study. The linear model takes its origins from the structure of a system as proposed by Bertalanffy,(Bello,1985).
Linear equation of a single relationship;
1
= F
1(
1,
2…
n)
2
= F
2(
1,
2…
n)
n
= F
n(
1,
2…
n)
Where ἰ
= 1,2,…n,
is the first derivative ἰ .Then, Let be defined as PPP an element of HDS, then P is a function of Pἰ ,ἰ=1,2,3,…12 i.e,
This research provides fifteen variables as leading indicators of the level of partnering among housing development actor/partners namely: location of estate, estate typology, tenure, provision of infrastructure, access to land, age of estate and firm, cost of construction, cost of foreign exchange, source of funding, profit motive, level of technology-in use, government policy. Assuming a linear relationship between levels of partnership and development actor/partners variable, then the model can be stated as follows;
If VP1 is the estimated level of partnership, C0 is the constant term
While, C1,C2,C3 are regression coefficients to be estimated
Then, VP2,VP3,VP4,VP5, etc are estimated indicators of level of partnership from resource optimization and the process optimization elements of PPP.
i.e,
VP1=C0+C1VP2+ C2 VP3 + C3 VP4 + C4 VP5 + C5 VP6 + C6 VP7 + C7 VP8 + C8 VP9 + C9 VP10 + C10 VP11 + C11 VP12+ C12 VP13
2.13.3 Validation of the Model
This research uses a validation technique, which allows for the testing of predictions obtained from fresh data. This is preferred to two other options in the open literature often used, namely, the analysis of model coefficients and data splitting. In order to achieve correctness, the new data must indicate that the Fisher’s exact p-values are proximal to chi-square values for variables in association, which in turn are validated by the prior plausibility of the supportive literature: By so the data obtained is conclusive on the qualitative and quantitative terms.
2.14 Characteristics/nature of Variables in the Study