Modelling building construction durations in Nigeria - Revision history

Designing Buildings at 11:18, 15 December 2021

2021-12-15T11:18:20Z

← Older revision		Revision as of 11:18, 15 December 2021
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−	This dissertation won the Merit Award, Masters Dissertation Award, ~~[http://iandrawards.ciob.org/winners/2014~~ CIOB International Innovation & Research Award, 2014].	+	This dissertation won the Merit Award, Masters Dissertation Award, CIOB International Innovation & Research Award, 2014.

	The judges said, “A well-written dissertation which is tightly written and nicely argued. It draws from a broad and authoritative literature review to frame the research question in an exemplary way. It has a diligent and statistically-rigorous analysis of quantitative data gleaned from a well-designed questionnaire survey. It is an excellent piece of work and is well deserving of international recognition.”		The judges said, “A well-written dissertation which is tightly written and nicely argued. It draws from a broad and authoritative literature review to frame the research question in an exemplary way. It has a diligent and statistically-rigorous analysis of quantitative data gleaned from a well-designed questionnaire survey. It is an excellent piece of work and is well deserving of international recognition.”

Designing Buildings at 06:42, 14 January 2021

2021-01-14T06:42:51Z

← Older revision		Revision as of 06:42, 14 January 2021
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	--[[User:CIOB\|CIOB]]		--[[User:CIOB\|CIOB]]

−	[[Category:~~International~~]] [[Category:~~Research_/_Innovation~~]] [[Category:DCN_Research,_Development_and_Innovation]]	+	[[Category:DCN_Commentary]] [[Category:DCN_Guidance]] [[Category:DCN_Research,_Development_and_Innovation]] [[Category:International]] [[Category:Research_/_Innovation]]

CIOB at 15:54, 9 September 2016

2016-09-09T15:54:11Z

← Older revision		Revision as of 15:54, 9 September 2016
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−		+	Hassan Ahmadu, Ahmadu Bello University, Nigeria.
−	Hassan Ahmadu, Ahmadu Bello University, Nigeria.~~<br/>~~	+

	There is now widespread acceptance in the research community that time prediction models for construction projects should take time-influencing qualitative and managerial factors into consideration. Consequently, several multivariate models combining project scope with these factors have been developed. However, the literature also points to the limitations of these models: their application is only effective in the regions and countries where they were developed. This study aims to develop a multivariate time prediction model that is suitable for the Nigerian construction industry. A self-administered questionnaire was used to source information on the project scope factors and qualitative and managerial factors that should be considered in the study.		There is now widespread acceptance in the research community that time prediction models for construction projects should take time-influencing qualitative and managerial factors into consideration. Consequently, several multivariate models combining project scope with these factors have been developed. However, the literature also points to the limitations of these models: their application is only effective in the regions and countries where they were developed. This study aims to develop a multivariate time prediction model that is suitable for the Nigerian construction industry. A self-administered questionnaire was used to source information on the project scope factors and qualitative and managerial factors that should be considered in the study.
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	Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows. Three models were developed; two of them - for the public and private sector - had high R2 values. Through testing and validation they were found to be suitable for predicting construction time. By contrast, the third model, which covered all projects, had a low R2 value and was found to be an unsuitable prediction tool. The models with high R2 values serve as a useful tool to help project managers and contractors predict construction time, thereby facilitating effective planning.		Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows. Three models were developed; two of them - for the public and private sector - had high R2 values. Through testing and validation they were found to be suitable for predicting construction time. By contrast, the third model, which covered all projects, had a low R2 value and was found to be an unsuitable prediction tool. The models with high R2 values serve as a useful tool to help project managers and contractors predict construction time, thereby facilitating effective planning.

−	----	+	-----
		+	This dissertation won the Merit Award, Masters Dissertation Award, [http://iandrawards.ciob.org/winners/2014 CIOB International Innovation & Research Award, 2014].

−	~~This~~ dissertation ~~won~~ the ~~Merit Award, Masters Dissertation Award, [http://iandrawards~~.~~ciob~~.~~org/winners/2014 CIOB International Innovation & Research Award, 2014]~~.	+	The judges said, “A well-written dissertation which is tightly written and nicely argued. It draws from a broad and authoritative literature review to frame the research question in an exemplary way. It has a diligent and statistically-rigorous analysis of quantitative data gleaned from a well-designed questionnaire survey. It is an excellent piece of work and is well deserving of international recognition.”

−	~~The judges said, “A well~~-written dissertation which is tightly written and nicely argued. It draws from a broad and authoritative literature review to frame the research question in an exemplary way. It has a diligent and statistically-~~rigorous analysis of quantitative data gleaned from a well-designed questionnaire survey. It is an excellent piece of work and is well deserving of international recognition.”~~	+	--[[User:CIOB\|CIOB]]

−	[[Category:International]]	+	[[Category:International]] [[Category:Research_/_Innovation]] [[Category:DCN_Research,_Development_and_Innovation]]
−	[[Category:Research_/_Innovation]]	+

Designing Buildings at 09:32, 20 May 2015

2015-05-20T09:32:46Z

← Older revision		Revision as of 09:32, 20 May 2015
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−	Hassan Ahmadu, Ahmadu Bello University, Nigeria. <br/>	+	Hassan Ahmadu, Ahmadu Bello University, Nigeria.<br/>

−	There is now widespread acceptance in the research community that time prediction models for construction projects should take time-influencing qualitative and managerial factors into consideration. Consequently, several multivariate models combining project scope with these factors have been developed. However, the literature also points to the limitations of these models: their application is only effective in the regions and countries where they were developed. This study aims to develop a multivariate time prediction model that is suitable for the Nigerian construction industry. A self administered questionnaire was used to source information on the project scope factors and qualitative and managerial factors that should be considered in the study.	+	There is now widespread acceptance in the research community that time prediction models for construction projects should take time-influencing qualitative and managerial factors into consideration. Consequently, several multivariate models combining project scope with these factors have been developed. However, the literature also points to the limitations of these models: their application is only effective in the regions and countries where they were developed. This study aims to develop a multivariate time prediction model that is suitable for the Nigerian construction industry. A self-administered questionnaire was used to source information on the project scope factors and qualitative and managerial factors that should be considered in the study.

	Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows. Three models were developed; two of them - for the public and private sector - had high R2 values. Through testing and validation they were found to be suitable for predicting construction time. By contrast, the third model, which covered all projects, had a low R2 value and was found to be an unsuitable prediction tool. The models with high R2 values serve as a useful tool to help project managers and contractors predict construction time, thereby facilitating effective planning.		Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows. Three models were developed; two of them - for the public and private sector - had high R2 values. Through testing and validation they were found to be suitable for predicting construction time. By contrast, the third model, which covered all projects, had a low R2 value and was found to be an unsuitable prediction tool. The models with high R2 values serve as a useful tool to help project managers and contractors predict construction time, thereby facilitating effective planning.

Designing Buildings: Created page with " Hassan Ahmadu, Ahmadu Bello University, Nigeria.
There is now widespread acceptance in the research community that time prediction models for construction projects should..."

2015-05-20T09:29:34Z

Created page with " Hassan Ahmadu, Ahmadu Bello University, Nigeria. <br/> There is now widespread acceptance in the research community that time prediction models for construction projects should..."

New page

Hassan Ahmadu, Ahmadu Bello University, Nigeria. <br/>

There is now widespread acceptance in the research community that time prediction models for construction projects should take time-influencing qualitative and managerial factors into consideration. Consequently, several multivariate models combining project scope with these factors have been developed. However, the literature also points to the limitations of these models: their application is only effective in the regions and countries where they were developed. This study aims to develop a multivariate time prediction model that is suitable for the Nigerian construction industry. A self administered questionnaire was used to source information on the project scope factors and qualitative and managerial factors that should be considered in the study.

Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows. Three models were developed; two of them - for the public and private sector - had high R2 values. Through testing and validation they were found to be suitable for predicting construction time. By contrast, the third model, which covered all projects, had a low R2 value and was found to be an unsuitable prediction tool. The models with high R2 values serve as a useful tool to help project managers and contractors predict construction time, thereby facilitating effective planning.

----

This dissertation won the Merit Award, Masters Dissertation Award, [http://iandrawards.ciob.org/winners/2014 CIOB International Innovation & Research Award, 2014].

The judges said, “A well-written dissertation which is tightly written and nicely argued. It draws from a broad and authoritative literature review to frame the research question in an exemplary way. It has a diligent and statistically-rigorous analysis of quantitative data gleaned from a well-designed questionnaire survey. It is an excellent piece of work and is well deserving of international recognition.”

[[Category:International]]
[[Category:Research_/_Innovation]]

Modelling building construction durations in Nigeria - Revision history

Designing Buildings at 11:18, 15 December 2021

Designing Buildings at 06:42, 14 January 2021

CIOB at 15:54, 9 September 2016

Designing Buildings at 09:32, 20 May 2015

Designing Buildings: Created page with " Hassan Ahmadu, Ahmadu Bello University, Nigeria. There is now widespread acceptance in the research community that time prediction models for construction projects should..."

Designing Buildings: Created page with " Hassan Ahmadu, Ahmadu Bello University, Nigeria.
There is now widespread acceptance in the research community that time prediction models for construction projects should..."