Estimación de las Ineficiencias Operacionales

In order to accurately process, the data obtained on a research it is necessary to use appropriate methods of data analysis. However, Fellows and Liu (2008:187) opine that data analysis could comprise the use of multiple analytical techniques to facilitate the ease of communicating the research results while at the same time improving its validity. Drawing from this suggestion thus, three methods of data analysis were employed for this study. Descriptive statistical techniques were employed to analyse the background information of the respondents and to analyse the severity of the variable contributing to the constructs identified under each of the study objectives.

Further to this, Principal Component Analysis (PCA) was employed to establish the underlying factors contributing to the constructs.

3.4.5.1 Descriptive Statistics

One of the most important initial analysis is to describe the participant of study and the finding must present the characteristics of the sample (Russel & Purcell, 2009:141).

Descriptive statistics are used for describing the basic feature of the data set and to summarise the key variables. The basic summary of each variable is presented by showing a proportionate breakdown of categories for each variable. The purpose of this statistical tool is to provide the characteristics of respondents, check the variables for any violation of the assumptions underlying the statistical techniques that will be used to address specific research questions; to have an overall and straightforward picture of a large amount of data (Henn, Weinstein & Foard, 2006: 206; Struwig & Stead,

2007:158; Pallant, 2011:53). Henn, Weinstein and Foard (2006) identified the three measures of central tendency as; mean median and mode.

3.4.5.2 Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a data reduction technique that takes a large set of variables and looks for a way the data may be reduced or summarised using a smaller set of factors or components, which can then be used for further analysis (Pallant, 2011:181). There are two main approaches to factor analysis as described in the literature: exploratory and confirmatory. Exploratory factor analysis is often used in the early stages of research to gather information about (explore) the interrelationships among a set of variables. Confirmatory factor analysis, on the other hand, is a more complex and sophisticated set of techniques used in this research to test (confirm) specific hypotheses or theories concerning the structure underlying a set of variables.

Factor analysis encompasses a variety of different, although related, techniques. One of the main distinctions is between what is termed principal components analysis (PCA) and factor analysis (FA). These two sets of techniques are similar in many ways and are often used interchangeably by researchers. However, both attempts to produce a smaller number of linear combinations of the original variables in a way that captures (or accounts for) most of the variability in the pattern of correlations. They do differ in a number of ways, however. In principal components analysis the original variables are transformed into a smaller set of linear combinations, with all of the variance in the variables being used. In factor analysis, factors are estimated using a mathematical model, whereby only the shared variance is analysed (Pallant, 2011:182). Consequent upon the distinction between Factor Analysis (FA) and Principal Component Analysis (PCA) Tabachnick and Fidell (2007:635) posits that; “If you are interested in a theoretical solution uncontaminated with unique and error variability… FA is your choice. If on the other hand, you simply want an empirical summary of the data set, PCA is the better choice”.

Therefore, the focus of this research is to establish an operational framework to evaluate delay related to contractual claims, hence the need for empirical analysis of the data set with a view to use the findings in the development of the framework.

To use this technique, the data will first undergo a suitability test that involves an examination of the determinants of the correlation matrix for multicollinearity and

singularity. Other necessary tests according to Kaming et al., (1997:88) and Pallant (2011:182) are:

(i) Kaiser–Meyer–Olkin (KMO) =

---3.1

Where rij is the simple correlation coefficient between variables and , and is the partial correlation coefficient between variables and .

KMO is a measure of sampling accuracy, which ranges from 0 to 1, and a value below 0.50 is unacceptable. Bartlett Sphericity test is a test of the identity matrix, which is particularly useful when relatively small samples of data are involved (e.g. m <100).

(ii) Bartlett’s chi-square test statistic ---3.2

Where n denotes the number of matrix variables in R; m denotes the sample size, and Ln /R/ denotes the natural logarithm of the determinant of the sample correlation matrix in R. The computed value is compared to the tabular for a selected & risk with 0.5 (n² -n) degrees of freedom.

Principal Components Regression was used in this study to extract the smallest number of variables that account for interrelationships among the variable sets considered in this study. The total number of principal component factors that can be extracted from any factor analysis is equal or less than the number of variables involved. The important factors are those whose eigenvalues are more than the average of the eigenvalues, , where p is the number of principal components extracted from the data, and λ is the eigenvalues of component of factors identified indicates the number of sub-models to be accommodated by the main model. Factor loadings and the commonalities (h²) of the determinants of the variables are then evaluated. Factor loadings are the correlation coefficient between an original variable and an extracted factor while commonality is the variance in the variables that have been accounted for by the factors extracted. For instance, the higher the absolute value of the factor loading, the more the variable contributes to the component extracted. In order to minimise the number of components in which the determinants have a high factor loading, a varimax rotation is carried out and the factor scores, results obtained using the principal component extraction method (Pallant, 2011:182). In conclusion, the principal component factor analysis was carried out using SPSS 22.0 software package.