Panantza San Carlos

Three different instruments were used, i.e. one for measuring integrity, one for measuring counterproductive work behaviour, and one for measuring the specific personality characteristics. The instruments for measuring counterproductive behaviour and personality (two dimensions of the HEXACO) were used in their original form while the instrument for measuring integrity was developed to fit the purpose of this study.

3.5.1 Integrity

A new Ethical Integrity Test (EIT) was developed for the purpose of this study. After dealing with missing values, the remaining sample (N = 318) was used to develop, refine, and validate the new EIT. The Ethical Integrity Test was systematically developed through different stages based on specific guidelines in the literature (Babbie & Mouton, 2004; Kline 2011). Each of these stages is discussed below.

Phase 1: Aim of the instrument and construct domain

The Ethical Integrity Test is designed to be utilised for personnel selection in order to predict counterproductive work behaviour. This test is composed of items that query job applicants about their attitudes toward work related counterproductive behaviour and inquire about any past counterproductive behaviour. Integrity testing has greatly increased and has become a mainstream selection practice for a wide variety of jobs in which employees have access to cash or merchandise or perform security functions (Camara & Schneider, 1994).

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For the purpose of this study ethical integrity was defined as acting in accordance with universally accepted ethical principles, values and norms. The components of ethical integrity are behavioural consistency, righteousness, frankness, credibility and fairness.

Phase 2: Item Generation

The purpose of this phase was to create a large, inclusive pool of items, so that they together would reflect the domain of behaviours that meet the definition of ethical integrity. On the basis of previously published theoretical and empirical investigations of behaviours revealing integrity in the workplace (see section 2.3.2, Chapter 2), an initial pool of 70 items was generated.

Thirty-five (35) items were adapted from a variety of measures, which measure different aspects of (moral) integrity: Brown, Trevino and Harrison (2005) (3 items), Butler (1991) (11 items), Kalshoven, Den Hartog and De Hoogh (2011) (4 items), Mayer, Aquino, Greenbaum and Kuenzi (2012) (5 items), Neider and Schriesheim (2011) (3 items), Simons, Friedman, Liu and Parks (2007) (3 items) and Yukl, Mahsud, Hassan and Prussia (2011) (6 items). Another thirty-five (35) items were developed particularly for the purpose of the new integrity scale.

Respondents were asked to indicate the extent to which they agreed with a specific item on a 5-point Likert scale. The scale anchors varied from 1 (disagree strongly) to 5 (agree strongly).

Phase 3: Item Review

The next phase in the development process was to subject the experimental instrument to expert scrutiny (Huysamen, 1980). The initial pool of 70 items and five dimensions were reviewed by 15 judges who have acquired expertise in the field of Industrial Psychology in South Africa. All the judges have at least attained Master’s degrees in Industrial Psychology and have gained experience in management consultation.

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The judges reviewed the items on the basis of several criteria. Firstly, the judges evaluated each dimension in terms of whether it was consistent with the definition of integrity used in this study. Secondly, the judges selected the most representative dimension per item. The experts were further requested to make recommendations in order to improve the measure. Based on the feedback from the panel of experts, four (4) items were deleted due to too much overlap with other items, eight items were moved to more appropriate dimensions, and five items were rewritten to improve their level of comprehension. The number of items per dimension after the item review phase, as well as example items is illustrated in Table 3.2.

Table 3.2

Number of items per subscale of the EIT after the item review phase

Subscale No of items Example of item

Behavioural consistency 10 Item 5: I consistently behave in an ethical way

Item 19: I practice what I preach

Righteousness 14 Item 20: I use my moral beliefs to make

decisions

Item 35: My behaviour is guided by sound principles

Frankness 14 Item 7: I shall tell the truth, even under

pressure from others

Item 16: People can believe what I say

Credibility 15 Item 22: People can depend on me

Item 37: I keep promises that I make to others

Fairness 13 Item 23: My major concern is always what is

best for the other person

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Total 66

Phase 4: Item analysis

The 66 items generated from the item generation and item review phases were subjected to item analysis to determine their internal consistency (see section 3.6). The results of the item analysis are presented in Chapter 4.

Phase 5: Exploratory Factor Analysis (EFA)

Exploratory factor analysis (EFA) is used to determine whether the dimensionality of each scale contributes to an internally consistent description of the relevant measuring model. Exploratory factor analysis can further be used as a process to refine and reduce items by identifying and removing items with inadequate factor loadings (Pallant, 2007). Nunnally (1978) refers to factor analysis as a “broad category of approaches to conceptualizing groupings (or clusterings) of variables and an even broader collection of mathematical procedures for determining which variables belong to which group” (p. 327).

The first step is to perform an Exploratory Factor Analysis (EFA) on all the items comprising the sub-scales. Exploratory Factor Analysis (EFA) was done to examine the uni-dimensionality of the sub-scales and identify items contributing to the lack of coherency. The purpose was to confirm the uni-dimensionality of each scale and subscale and to remove items with inadequate factor loadings (Theron et al., 2004). SPSS (Version 20) was used to perform the uni-dimensionality test.

Principal axis factor analysis was used as the extraction technique. This technique was utilized rather than the principal components analysis because the statistical calculation of the Principal factor analysis allows for the presence of measurement error. The extracted solution was then subjected to oblique rotation. Although oblique rotation is

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slightly more difficult than orthogonal rotation, it allows the underlying factors to be correlated (Pallant, 2007).

As soon as the number of significant factors had been established, the factor loadings on the rotated matrix were investigated. Poor items should be identified and subjected to elimination according to the EFA decision criteria. A factor loading was considered acceptable if λij > 0.30 (Tabachnick & Fidell, 2001).

The decision rules that were followed to determine the number of factors to be extracted, and the items to be included in each factor when conducting exploratory factor analyses were as follows:

 The number of factors to be extracted had to have eigenvalues > 1.00, according to Kaiser’s (1961) criterion.

 An item not loading > 0.30 on any factor would be excluded (Pallant, 2007; Tabachnick & Fidell, 2001).

 An item loading > 0.30 on more than one factor would be excluded if the difference between the higher and the lower loading was < 0.25 (Tabachnick & Fidell, 2001).

A Kaiser-Meyer-Olkin measure of sampling adequacy (KMO index) is used to compare the magnitudes of the observed correlation coefficients in relation to the magnitudes of the partial correlation coefficients. Large values are good because correlations between pairs of variables (i.e. potential factors) can be explained by the other variables. A value close to 1 indicates that patterns of correlations are relatively compact and factor analysis should therefore present distinct and reliable factors (Field, 2009). Kaiser (as cited in Field, 2009) recommends accepting values greater than 0.5 as acceptable; values between 0.5 and 0.7 as mediocre; and values between 0.7 and 0.8 as good, while values between 0.8 and 0.9 are great and values above 0.9 are superb.

Bartlett’s test of sphericity is used to test the hypothesis that the correlation matrix is an identity matrix (all diagonal terms are one and all off-diagonal terms are zero). Significance values less than .05 are acceptable (Field, 2009). The scree plot can be

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used to provide further information when deciding on the factors which should be reserved. The use of the scree plot entails inspecting the point at which the shape of the curve changes direction and becomes horizontal (Pallant, 2007). According to Catell (1966), all factors above the elbow, or break, in the plot should be retained, as these factors contribute the most to the explanation of the variance in the data set.

Phase 6: Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis (CFA) is a technique whereby hypotheses or theories relating to the structure underlying a set of variables are tested (Pallant, 2007) (see Section 3.6.2). LISREL 8.80 (Jöreskog & Sörbom, 1996) was used to perform separate confirmatory factor analyses (CFA) on the different sub-scales of the Ethical Integrity Test. The results of CFA are discussed per sub-scale in terms of important fit indices (see Chapter 4).

3.5.2 Conscientiousness and Honesty

Lee and Ashton’s (2004) HEXACO Personality Inventory-Revised (HEXACO-PI-R) was administered to participants to measure Conscientiousness and Honesty. The Honesty- Humility (16 items) and Conscientiousness (16 items) subscales were utilised for the purpose of the study. Responses were given on a 5-point rating scale; from 1 (strongly disagree) to 5 (strongly agree) where higher scores indicate a higher degree of the personality dimension. HEXACO-PI-R factor scales show high internal consistency reliabilities and show adequate convergent validities with external variables (Ashton & Lee, 2009). Table 3.2 depicts the internal consistency reliabilities of the Honesty- Humility and Conscientiousness subscales.

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Table 3.3

Internal Consistency Reliabilities of the HEXACO-PI Scales (Coefficient Alpha)

Scale Reliability Scale Reliability

Honesty-Humility Sincerity Fairness Greed Avoidance Modesty .92 .79 .85 .87 .83 Conscientiousness Organization Diligence Perfectionism Prudence .89 .85 .79 .79 .78 Note. N= 409. Each facet-level scale has 8 items, and each of the factor-level scales has 32 items.

Adapted from Lee and Ashton (2004)

3.5.3 Counterproductive Work Behaviour (CWB).

Counterproductive work behaviour was measured by means of the Interpersonal and Organisational Deviance Scale developed by Bennett and Robinson (2000). The scale contains items tapping into various kinds of CWB, including theft, absenteeism, bullying, vandalism and alcohol abuse. According to Bennett and Robinson (2000), the internal reliabilities for the Organisational Deviance Scale and Interpersonal Deviance Scale are 0.81 and 0.78 respectively.