Data exploration on the factors associated with cost overrun on social housing projects in Trinidad and Tobago

This data article explores the factors that contribute to cost overrun on public sector projects within Trinidad and Tobago. The data was obtained through literature research, and structured questionnaires, designed using open-ended questions and the Likert scale. The responses were gathered from project actors and decision-makers within the public and private construction industry, mainly, project managers, contractors, engineers, architects, and consultants. The dataset was analysed using frequency, simple percentage, mean, risk impact, and fuzzy logic via the fuzzy synthetic evaluation method (FSE). The significance of the analysed data is to determine the critical root causes of cost overrun which affect public sector infrastructure development projects (PSIDPs), from being completed on time and within budget. The dataset is most useful to project and construction management professionals and academia, to provide additional insight into the understanding of the leading factors associated with cost overrun and the critical group in which they occur (political factors). Such understanding can encourage greater decisions under uncertainty and complexity, thus accounting for and reducing cost overrun on public sector projects.


a b s t r a c t
This data article explores the factors that contribute to cost overrun on public sector projects within Trinidad and Tobago.The data was obtained through literature research, and structured questionnaires, designed using open-ended questions and the Likert scale.The responses were gathered from project actors and decision-makers within the public and private construction industry, mainly, project managers, contractors, engineers, architects, and consultants.The dataset was analysed using frequency, simple percentage, mean, risk impact, and fuzzy logic via the fuzzy synthetic evaluation method (FSE).The significance of the analysed data is to determine the critical root causes of cost overrun which affect public sector infrastructure development projects (PSIDPs), from being completed on time and within budget.The dataset is most useful to project and construction management professionals and academia, to provide additional insight into the understanding of the leading factors associated with cost overrun and the critical group in which they occur (political factors).Such understanding can encour-age greater decisions under uncertainty and complexity, thus accounting for and reducing cost overrun on public sector projects. Crown

Value of the Data
• The data set is the first to provide a methodological classification of the leading root causes of cost overruns in public sector social development housing projects.This is useful in acquiring a deeper understanding of these leading root causes and validated against their theoretical ontologies.• The data can be used in decision making research to show the uncertainty, imprecision and complexity of perceptions and heuristics used in the construction industry and their major influences on the economic viability of social developmental projects.The data set shifts the current research agenda in cost overrun studies, exposing the lack of attention to the true leading root causes of cost overruns and adds to contemporary academic debate by encouraging project and construction practitioners to reflect, refocus, reframe, and reset the research agenda to uncover key tacit knowledge areas.• The data can be applied to develop forecasting models to demonstrate the misalignment in the construction housing industry and highlight the gaps in contemporary project practices leading to unsustainable delivery and practices of social housing.The data can be used as a basis of comparison with that of other Small Island Developing States and/or on a worldwide scale, in the field of construction project management.It further updates project management practices by uncovering and prioritising theoretical constructs critical to public sector projects.• The provided data can be utilized by academia and construction project practitioners to develop a multitude of risk assessment processes, models and pragmatic tools based on these critical risk factors for further testing to optimize cost performances and sustainability on this value driven socially dependent infrastructure projects.• The data can be used by policy makers and governmental bodies to analyse the latent effects of critical risk factors grouped under various root causes can have on overall developmental policies, and their emulation on the overall social housing value.These latent effects can be studied to develop strategies to mitigate wicked problems associated with social housing such as crime, unemployment, and income inequalities.

Data Description
The data was obtained through literature research, and structured questionnaires.A total of 150 questionnaires were distributed to Project Managers, Contractors, Engineers, Architects, and Consultants within the construction industry who have been involved in social housing projects [ 1 ].The data received from the participants were presented as follows: The data on the highest level of education attained by the respondents is presented in Table 1 which illustrates that more than 70% of respondents have a minimum qualification of a Bachelor of Science degree, data on the professional role ( Table 2 ) which highlighted that respondents represent mainly five professional roles, sector of employment in which they are employed ( Table 3 ) either in the public sector or the private sector, types of projects mainly carried out by the organisations to which the participants belong ( Table 4 ) under main eight categories, the number of employees ( Table 5 ) where that most of the respondents are belonging to the organisations which are having more than 200 employees, number of projects participated in ( Table 6 ), annual estimated turn over ( Table 7 ), expected duration of projects ( Table 8 ), and the actual time spent ( Table 9 ).Table 10 presents data on the number of years of experience of each respondent in the field of project management, consultancy, contracting, engineering, and architecture.Data on the Risk Impact associated with cost overrun on construction projects compared between the private sector and public is presented in Table 11 .The data clearly show that the impact of the factors that contribute to the cost overruns is different between the public and private sectors.Furthermore, Table 12 presented factors contributing to cost overrun on public sector projects which were extracted through the existing literature.The analysis of the raw data (factors presented in Table 12 ), provides the 22 critical factors associated with a cost overrun on public sector projects ( Table 13 ) based on the severity and the probability of each risk whistle analysing the risk impact factor.The data in  18 (Data on the overall risk level) presents the levels to the fuzzy logic analysis approach implemented to rank the principal risk groups (Political, Socio-economical, technical and psychological) according to the risk index.ranked according to the normalised values obtained so that factors having values greater than 0.5 were deemed critical ( Table 13 ).
Through the application of fuzzy logic, namely fuzzy synthetic evaluation, the 22 critical risk factors (CRFs) were classified under four critical risk groups (CRGs), namely, political, socioeconomical, technical, and psychological, and ranked overall according to their category, based on the risk impact ( Table 14 ) [ 1 ].The weighing function, of the CRFs, (second-level) and CRGs (first level) are calculated from the mean values, obtained through SPSS for both its probability and severity ( Table 15 ).Next, the membership functions of the CRFs & CRGs (level 1) along with the risk level of each CRG (MF level 2) were determined and presented in Table 16 .The obtained fuzzy evaluation matrixes, D i (i = u 1 , u 2 , u 3 , u 4 ) of the CR Gs (level 2) were further normalized by considering their weighing functions to generate the final fuzzy evaluation matrix of overall risk level (ORL) of cost overrun of social housing development (i.e. level 1).The probability and severity matrixes of the PRFs are represented in column 3 of Table 17 .The overall risk level of cost overrun on public sector projects in developing countries is presented in Table 18 which illustrates that the political category has more risk compared to the others.
The outcome of this study indicates that further studies could be conducted to evaluate the cost controlling and monitoring strategies for the identified risk factors of cost overrun on social housing projects and a study on cost planning and estimating mechanisms to mitigate the factors of cost overrun on social housing projects could also be carried out.Furthermore, similar types of studies can be conducted for the other types of building and infrastructure construction projects which will contribute greatly to the existing knowledge and the betterment of the industry.

Limitations
None.
Copyright © 2023 Published by Elsevier Inc.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) (41) factors related to cost overrun on construction projects were extracted from 37 journals, through literature research.Structured questionnaires designed with both open-ended questions and the 7-point Likert scale captured the demographic data and the views of the respondents.Survey questionnaires were distributed to construction professionals such as

Table 1
Data highest level of education attained in the field of Civil engineering/ project management of the respondents.

Table 4
Data on the nature of the organization's projects to which the respondents belong.

Table 7
Data on the annual estimated turnover of the company in which the respondent is employed.

Table 8
Data of the expected duration estimated for the last executed project by the respondents.

Table 9
Data of the actual time spent to execute the last project by the respondents.

Table 10
Data on the number of years of experience of the respondents.

Table 14 (
Data on the classification and ranking of critical risk factors), Table 15 (Data on the weightings for the 22 CRFs and 4 PRFs for Social Housing Program), Table 16 (Data on the membership function of all CRFs and PRFs for Risk Probability and Severity), Table 17 (Data on the membership function of the overall risk level), Table

Table 11
Data on the level of risk associated with a cost overrun on construction projects.

Table 12
Data on the 41 factors linked to cost overrun on public sector projects, were extracted and grouped through literature research.Data on the risk probability, severity, and risk impact along with the normalised values obtained for the risk factors associated with cost overrun.

Table 14
Data on the classification and ranking of critical risk factors.

Table 15
Data on the weightings for the 22 CRFs and 4 CRGs for social housing program.

Table 16
Data on the membership function of all CRFs and CRGs for risk probability and severity.

Table 16 (
continued ) Membership functions for all CRFs and PRFs for cost overrun on Social Housing Programs (Risk Probability) Membership functions for all CRFs and PRFs for cost overrun on Social Housing Programs (Risk Severity)

Table 16 (
continued ) Membership functions for all CRFs and PRFs for cost overrun on Social Housing Programs (Risk Probability) Membership functions for all CRFs and PRFs for cost overrun on Social Housing Programs (Risk Severity) CRF = Critical Risk Factor, CRG = Critical Risk Group.