| dc.description.abstract | Validation of results is an important phase in the organization of a researcher’s work. Libraries and the internet offer a number of sources for guidance with respect to conducting validation in a variety of fields. However, construction engineering and management (CEM) is an area for which such information is unavailable. CEM is an interdisciplinary field, comprised of a variety of subjects: human resources management, project planning, social sciences, etc. This broad range means that the choice of appropriate validation methodologies is critical for ensuring a high level of confidence in research outcomes. In other words, the selection of appropriate validation methodologies represents a significant challenge for CEM researchers. To assist civil engineering researchers as well as students undertaking master’s or doctoral CEM studies, this thesis therefore presents a comprehensive review of validation methodologies in this area. The validation methodologies commonly applied include experimental studies, observational studies, empirical studies, case studies, surveys, functional demonstration, and archival data analysis. The author randomly selected 365 papers based on three main perspectives: industry best practices in construction productivity, factors that affect labour productivity, and technologies for improving construction productivity. The validation methodologies that were applied in each category of studies were examined and recorded in analysis tables. Based on the analysis and discussion of the findings, the author summarized the final results, indicating such items as the highest percentage of a particular methodology employed in each category and the top categories in which that methodology was applied. The research also demonstrates a significant increasing trend in the use of functional demonstration over the past 34 years. As well, a comparison of the period from 1980 to 2009 with the period from 2010 to the present revealed a decrease in the number of papers that reported validation methodology that was unclear. These results were validated through analysis of variation (ANOVA) and least significant difference (LSD) analysis. Furthermore, the relationship between the degree of validation and the number of citations is explored. The study showed that the number of citations is positively related to the degree of validations in a specific category, based on the data acquired from the examination of articles in Constructability and Factors categories. However, based on the data acquired from the examination of articles in the year 2010, we failed to conclude that there existed significant difference between clear-validation group and unclear validation group at the 95 % confidence level. | en |
