Dissertation•
Improving skilled workers’ performance in construction projects in Nigeria
01 Jan 2016-
TL;DR: In this article, the authors identified the causes of low skilled workers' low performance in construction projects and recommended ways to improve skilled workers performance in the construction projects in Nigeria through a structured qualitative method of questionnaire.
Abstract: Skilled workers’ performance is one of the crucial aspects of labour productivity that
requires proper attention for effective projects delivery in the construction industry. The
level of skilled workers’ low performance has been seen to be a major factor which
contributes toward inefficient construction projects productivity. Therefore, the objectives
of this research are to identify the causes of low skilled workers’ performance in
construction projects and to recommend ways to improve skilled workers’ performance
in construction projects in the Nigeria. The objectives were achieved through a structured
quantitative method of questionnaire distributed to 150 responents from project manager,
project engineer, site engineer and site supervision that are active in the Nigerian
construction and 111 of the response were collected which was 74% of the response rate.
The data were collected and analysed using Statistical Package for Social Science (SPSS)
version 22.0. Mean ranking and analysis of variance (ANOVA) were used as tools to
analyse the data. The findings shows that; low wages of skilled, lack of sufficient skill
acquisition centres and lack of incentive schemes for skilled workers were the most
significant causes of low skilled workers’ performance in the Nigerian construction
industry. Similarly, proper supervision, supply of quality plants and equipment and good
wages for skilled workers were the most significant ways to improve skilled workers’
performance in the Nigerian construction industry. The homogenous analysis indicates
that there are significant differences in perception of respondents on few variables whereas
majority of respondents have similarities in most of the variables. The research findings
confirmed that, stakeholders in the Nigerian construction industry should strategise on
motivation, training and retraining, conducive working condition, supply of quality
materials and equipment, and proper site management in order improve skilled workers’
performance in Nigerian construction industry.
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935 citations
01 Nov 2011
TL;DR: The authors proposed a new set of competitive grants from the federal government to states that would fund training partnerships between employers in key industries, education providers, workforce agencies, and intermediaries at the state level, plus a range of other supports and services.
Abstract: To improve the employment rates and earnings of Americans workers, we need to create more-coherent and more-effective education and workforce development systems, focusing primarily (though not exclusively) on disadvantaged youth and adults, and with education and training more clearly targeted towards firms and sectors that provide good-paying jobs. This paper proposes a new set of competitive grants from the federal government to states that would fund training partnerships between employers in key industries, education providers, workforce agencies, and intermediaries at the state level, plus a range of other supports and services. The grants would especially reward the expansion of programs that appear successful when evaluated with randomized controlled trial (RCT) techniques. The evidence suggests that these grants could generate benefits that are several times larger than their costs, including higher earnings and lower unemployment rates among the disadvantaged.
5 citations
References
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TL;DR: Krystek as discussed by the authors provides a comprehensive and self-contained overview of random data analysis, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind.
Abstract: This is a new edition of a book on random data analysis which has been on the market since 1966 and which was extensively revised in 1971. The book has been a bestseller since. It has been fully updated to cover new procedures developed in the last 15 years and extends the discussion to a broad range of applied fields, such as aerospace, automotive industries or biomedical research. The primary purpose of this book is to provide a practical reference and tool for working engineers and scientists investigating dynamic data or using statistical methods to solve engineering problems. It is comprehensive and self-contained and expands the coverage of the theory, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind. It could well be used as a teaching textbook for advanced courses on the analysis of random processes. The first four chapters present the background material on descriptions of data, properties of linear systems and statistical principles. They also include probability distribution formulas for one-, two- and higher-order changes of variables. Chapter five gives a comprehensive discussion of stationary random-process theory, including material on wave-number spectra, level crossings and peak values of normally distributed random data. Chapters six and seven develop mathematical relationships for the detailed analysis of single input/output and multiple input/output linear systems including algorithms. In chapters eight and nine important practical formulas to determine statistical errors in estimates of random data parameters and linear system properties from measured data are derived. Chapter ten deals with data aquisition and processing, including data qualification. Chapter eleven describes methods of data analysis such as data preparation, Fourier transforms, probability density functions, auto- and cross-correlation, spectral functions, joint record functions and multiple input/output functions. Chapter twelve shows how to handle nonstationary data analysis, classification of nonstationary data, probability structure of nonstationary data, calculation of nonstationary mean values or mean square values, correlation structures of nonstationary data and spectral structures of nonstationary data. The last chapter deals with the Hilbert transform including applications for both nondispersive and dispersive propagation problems. All chapters include many illustrations and references as well as examples and problem sets. This allows the reader to use the book for private study purposes. Altogether the book can be recommended for practical working engineers and scientists to support their daily work, as well as for university readers as a teaching textbook in advanced courses. M Krystek
3,390 citations
Book•
01 Jan 2007
3,220 citations
935 citations
TL;DR: Wang et al. as discussed by the authors analyzed the key risks in construction projects in China and developed strategies to manage them from a joint perspective of project stakeholders and life cycle and concluded that clients, designers and government bodies should take the responsibility to manage their relevant risks and work cooperatively from the feasibility phase onwards to address potential risks in time.
Abstract: The aim of this paper is to understand the key risks in construction projects in China and to develop strategies to manage them. Risks were prioritized according to their significance of influences on typical project objectives in terms of cost, time, quality, safety and environmental sustainability, and then scrutinized from a joint perspective of project stakeholders and life cycle. Postal questionnaire surveys were used to collect data, based on which a total of 25 key risks were ascertained. These risks were compared with the findings of a parallel survey in the Australian construction industry context to highlight the unique risks associated with construction projects in China. Strategies to manage the risks were sought from the perspectives of project stakeholders and life cycle and in light of the Chinese construction culture. It is concluded that clients, designers and government bodies should take the responsibility to manage their relevant risks and work cooperatively from the feasibility phase onwards to address potential risks in time; contractors and subcontractors with robust construction and management knowledge should be employed to minimize construction risks and carry out safe, efficient and quality construction activities.
714 citations
01 Jan 2002
TL;DR: In this paper, the authors provide a simplified methodology for calculating Cohen's d effect sizes from published experiments that use t-tests and F-tests of significance, and a Microsoft Excel Spreadsheet that can be used to compute Cohen's D from published data.
Abstract: Overview This article provides a simplified methodology for calculating Cohen's d effect sizes from published experiments that use t-tests and F-tests. Accompanying this article is a Microsoft Excel Spreadsheet to speed your calculations. Both the spreadsheet and this article are available as free downloads at www.work-learning.com/effect_sizes.htm. Why we use effect sizes Whereas statistical tests of significance tell us the likelihood that experimental results differ from chance expectations, effect-size measurements tell us the relative magnitude of the experimental treatment. They tell us the size of the experimental effect. Effect sizes are especially important because they allow us to compare the magnitude of experimental treatments from one experiment to another. Although percent improvements can be used to compare experimental treatments to control treatments, such calculations are often difficult to interpret and are almost always impossible to use in fair comparisons across experimental paradigms. Although extensive articles have been written detailing methods for calculating effect sizes from published research articles (e. at least some of us—the first author included—require a simpler approach. This article provides a method to calculate Cohen's d from both t-tests and some F-tests of significance. Accompanying this article is a Microsoft Excel Spreadsheet that can be used to compute Cohen's d from published data. Cohen's d has two advantages over other effect-size measurements. First, its burgeoning popularity is making it the standard. Thus, its calculation enables immediate comparison Effect Sizes Work-Learning Research 3 www.work-learning.com to increasingly larger numbers of published studies. Second, Cohen's (1992) suggestion that effect sizes of .20 are small, .50 are medium, and .80 are large enables us to compare an experiment's effect-size results to known benchmarks. The simple methodology offered below is not new but is drawn from previously published articles, most notably Rosnow and Rosenthal (1996) and Rosnow, Rosenthal, and Rubin (2000). We have simplified the methodology not by changing the formulas and calculations but by discarding as much as possible the jargon and computational rationales typically included in articles written for research audiences. This article is an attempt to provide a practical methodology to enable the calculation of effect sizes. What is an effect size? In essence, an effect size is the difference between two means (e.g., treatment minus control) divided by the standard deviation of the two conditions. It is the division by the standard deviation that enables us to compare effect sizes across experiments. Because t-tests and F-tests utilize different measures …
682 citations