Exploratory Factor Analysis

The exploratory factor analysis is basically utilised to “examine the underlying patterns or relationships for a large number of variables and to determine whether the information can be condensed or summarised into a smaller set of factors or components” (Hair et al., 2009). The primary purpose of factor analysis is to define the underlying structure among the variables as well as the interrelationships shared among the variables in a study. It signifies that the factors deduced through this process have high correlations with few variables and the remaining correlation should be near to zero. Preferably, to carry out exploratory factor analysis the sample size should be more than 100 and as a general rule, the minimum is to have at least five times as many observations as the number of variables to be analysed.

The exploratory factor analysis usually begins with principal component analysis, which yields a set of uncorrelated components. The principal component analysis is primarily focused on data reduction, so as to get the minimum number of factors needed to account for the maximum portion of the total variance represented in the original set of variables as well as to have a relatively small proportion of error variance. The number of factors extracted through this process can be determined by examining the eigenvalues of the principal component analysis. The proportion of common variance presented in a variable is showcased through the table of communality. The factor loadings are the correlation of a

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variable with a factor that has been extracted through the principal component analysis. The variables with high loading on the factor are examined and are named as such that it summarises the content of these variables listed. The table 6.2 illustrates some relevant studies which have used exploratory factor analysis for the purpose of data interpretation.

Table 6.1: Relevant Studies Undertaking Descriptive Statistics

Table 6.2: Relevant Studies Undertaking Exploratory Factor Analysis

Sl. No. Author Year Dimensions

1 Dirks 1999 Examined the effects of interpersonal trust on work group performance.

2 Rajadhyaksha 2005

Tested a model of the techno-managerial competencies for executives belonging to vehicle manufacturing companies.

3 Ryan, et al. 2009

Demonstrates the impact of role demands and culture on the manifestation of managerial competencies for the most predictive performance index.

4 Camuffo, et al. 2012

Explores the extent the competency portfolio of entrepreneurs affects firm’s performance, controlling a set of individual and organisational variables.

5 Sutton and

Sutton 2013

Depicts the utility of an organisation-wide competency framework for developmental needs and job performance.

6 Kohont and

Brewster 2014

Examines the roles and required competencies of HR managers in Slovenian multinational companies when these companies enter the international arena.

Sl. No. Author Year Dimensions

1 Nikolaou 2003

Discusses the development, validation and psychometric properties of a measure of generic work competencies.

2 Dainty, et al. 2005

This study identifies the core competencies associated with the construction management role and further develops a predictive model to inform the human resource selection and development decisions within large construction organisations. 3 Raja and Swapna 2010

Evaluates the difference between managerial and executive level personal competencies, personal competencies in IT companies.

4 Chuttipattana and

Shamsudin 2011

Examine the moderating or contingent effect of organisational culture on the relationship between the personality and managerial competencies of primary care managers in Thailand.

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Analysis of variance (ANOVA) is a collection of statistical models used to analyse the differences between group means and their variations among and between groups which was developed by Ronald Aylmer Fisher in early 1920’s. In its simplest form, ANOVA is equivalent to the t-test in which only two variables are involved, whereas in ANOVA more than two exploratory variables are involved. The exploratory variables in ANOVA are categorical in nature; hence they are referred to as factors (Hinkelmann and Kempthorne, 2008). The fundamental technique of ANOVA is to partition the total sum of squares (SS) into components related to the effects involved in the model. There are various methods of applying ANOVA but is typically dependent on the number of factors and the number of dependent variables involved. The one-way ANOVA is the simplest form of application, as only one single factor is involved. It is commonly used to test the differences between independent variables and its effects that can be estimated for the population as a whole. Primarily, one-way ANOVA is used to test the differences among at least three groups of observations, as two groups of observation can be easily tested through a t - test or F-test (as F = t2). When there is a case of two or more factors, then two-way ANOVA and three-way ANOVA is a significant method of measurement.

ANOVA is a useful procedure to test for significant differences between means. However, three assumptions have to be achieved to conduct this test. First is the assumption of independence, which states that observations are random and independent samples from the population. Second is the assumption of normality, which states that distributions of the population from which the samples are selected are normal. Third is the assumption of homogeneity of variance, which states that, the variances of the distribution in a population

5 Rahimic, et al. 2012

The principal aim of this study is to determine the level of management competencies in the process of employee motivation within an organisation. 6 Khoshouei, et al. 2013

The main objective of this study was to identify the essential managerial competencies for Iranian managers.

7 Quintana, et al. 2014

The purpose of this paper was to analyse the three dimensions of leadership behaviour in a professional environment, by disclosing the specific competency profile developed by those who actually lead in work organisations.

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are equal. The table 6.3 illustrates some relevant studies which have used analysis of variance for the purpose of data interpretation.

Table 6.3: Relevant Studies Undertaking Analysis of Variance