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Survey datasets on women participation in green jobs in the construction industry

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      Abstract

      The unique qualities of women can make them bearers of solutions towards achieving sustainability and dealing with the dangers attributed to climate change. The attitudinal study utilized a questionnaire instrument to obtain perception of female construction professionals. By using a well-structured questionnaire, data was obtained on women participating in green jobs in the construction Industry. Descriptive statistics is performed on the collected data and presented in tables and mean scores (MS). In addition, inferential statistics of categorical regression was performed on the data to determine the level of influence (beta factor) the identified barriers had on the level of participation in green jobs. Barriers and the socio-economic benefits which can guide policies and actions on attracting, retaining and exploring the capabilities of women in green jobs can be obtained from the survey data when analyzed.

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      Random number datasets generated from statistical analysis of randomly sampled GSM recharge cards

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        Survey datasets on the externalizing behaviors of primary school pupils and secondary school students in some selected schools in Ogun State, Nigeria

        This data article contains the partial analysis (descriptive statistics) of data obtained from 1770 primary school pupils and secondary school students in three Local Government Areas of Ogun State, Nigeria. The schools are either privately owned or public (government owned) schools. The aim of the field survey is to measure the level and patterns of externalizing behavior of the respondents. The data was collected using a standardized questionnaire. The questionnaire is a modification of Achenbach manual for Child behavior checklist (Achenbach, 2001) [1] and manual for Youth self-report (Achenbach and Rescorla, 2001) [2]. The questionnaire was designed to suit the demographic and socio-cultural nature of the target population. Analysis of the data can provide useful insights to the patterns of externalizing behavior of primary school pupils and secondary school students.
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          Residential construction cost: An Italian survey

          This paper reports data describing development projects for new buildings according to construction costs in North-East Italy. A survey was carried out on local companies undertaking new residential development projects in two Italian regions (Veneto and Lombardy). The aim of this survey was to record new real estate construction projects, collecting both technical and socio-economic cost features. It is extremely difficult to collect such data for the Italian real estate construction sector, due to its lack of transparency, so that the novelty for the Italian scenario is the dataset itself. Another interest perspective of this survey is that socio-economic characteristics were also recorded; they are often studied in urban economics, but are usually related to property purchase prices and values, not to construction costs. The data come from an analysis of Canesi and Marella regarding the relationship between the trend of construction costs and the socio-economic conditions of the reference setting, such as the mean years of schooling of the workforce, housing market trends, and average per capita income.
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            Author and article information

            Affiliations
            Department of Building Technology, Covenant University, Nigeria
            Author notes
            Contributors
            Journal
            Data Brief
            Data Brief
            Data in Brief
            Elsevier
            2352-3409
            09 February 2018
            April 2018
            09 February 2018
            : 17
            : 856-862
            5834649
            S2352-3409(18)30125-2
            10.1016/j.dib.2018.02.009
            © 2018 The Authors

            This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

            Categories
            Environmental Sciences   

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