The validity of the data

Validity is defined as the degree to which an instrument measures what it was intended to measure. In addition, validity is premised on the assumption that what is being studied can be measured or captured, and seeks to confirm the truth and accuracy of any findings or conclusions drawn from data which indicates that the conclusions drawn are trustworthy and that the methods warrant the conclusions (Hesse-Biber and Leavy, 2011). They further state that there are internal and external validity measures where internal validity is the measure of accurateness of the study considering the effects of unrelated variables. A threat to the validity of a design would cause a false positive or false negative outcome. One threat to the internal validity is the failure of the participants to answer the questions in the survey truthfully. On the other hand, external validity relates to the extent to which the outcome of the study generalises the results beyond the sample used.

According to Saunders et al. (2009), validity must truly represent a concept of measuring instrument. Therefore, validity must represent the results of the measuring instrument it is supposed to measure in order to provide accurate results. There are two types of validity namely: (1) external validity is about generalization and (2) internal validity ensures that a

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researcher’s research design closely follows the principle of cause and effect (Saunders et al., 2009).

According to Zikmund et al. (2010), validity is the extent to which a score truthfully represents a concept or the degree to which a test measures what it is designed to measure.

Validity refers to the extent to which measurements accurately reflect reality or the truth, and reliability is the extent to which the application of scale produces consistent results if repeated measures are taken. According to Anderson (2004), no one project is going to be able to produce findings that are 100 percent reliable and valid. Even so, it is necessary to address these issues in order to be able to determine an approach to data-gathering that indicates that an attempt was made to take an open-minded approach to gathering data, that steps were taken to minimise limitations of the study and to maximise its credibility.

Validity is considered to be the single most important attribute of a measuring instrument, (Zikmund et al. 2010). There are four types of validity that are considered to be important in psychological measurement and are discuss below as follows:

Content validity

According to Zikmund et al. (2010), content validity refers to the degree to which the instrument items represent the domain or universe of the trait or characteristic measured. It makes sure that a measure includes an adequate and representative set of items to cover a concept.

Criterion-related validity

Criterion-related validity is indicative of the ability of an instrument in predicting or estimating a criterion. This type of validity includes two types of validity, namely predictive and concurrent validity. Predictive validity refers to the ability of the measure to forecast the presence or absence of the trait in the future (Sekaran and Bougie, 2010).

Construct validity

It gives an indication of the degree of the relationship between the instrument and the construct being measured. This type of validity answers the question: What is the nature of the underlying variable or construct? Construct validity therefore indicates whether the scale measures what it was designed to measure (Bryman and Bell, 2008).

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Face validity

Face validity refers to the subjective content accepted by experts in the field of study on what apparently reflect the concept being measured. When the measuring instrument is established for the first time, the researcher must create the face validity (Zikmund et al., 2010).

Convergent validity

When two measuring instruments, measuring the same construct, are used and the results compared, the latter should converge (Sekaran and Bougie, 2010).

Discriminant validity

According to Zikmund et al. (2010), discriminant validity is the degree to which the operational definition is able to discriminate between the target construct and closely related variable which are conceptually distinct. They further state that, it represents how unique or distinct is a measure, a scale should not correlate too highly with a measure of a different construct. An exploratory factor analysis (EFA) is often conducted to assess the discriminant validity of data.

Saunders et al. (2009) state that exploratory factor analysis attempts to bring inter-correlated variables together under more general, underlying variables. Therefore the main objective of the factor analysis is to minimise the number of measurements to limited number explaining a particular phenomenon or variable. Table 3.7 shows the EFA results of the present study. In all the EFA analyses, the Principal Components method of extraction and Varimax Raw rotation were specified.

Table 3.7 shows that that seven RECN items loaded on factor 1. These items were all measures of recognition and this variable is therefore label as such.

Factor 2 attracted three job involvement items, namely JOB1, JOB2 and JOB4. This latent variable was therefore labelled as job involvement.

Four PYFB (fringe benefit) items loaded on Factor 3. This factor was consequently labelled fringe benefits.

Two job involvement items, JOB3 and JOB5, as well as one RECN item (RECN3) loaded on Factor 4. The content of these items describe aspects of jobs relating to job image or prestige. This factor was therefore labelled job prestige.

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Factor 5 attracted items PYFB1, PYFB2, PYFB3, PYFB4 and RECN1, which are all related to pay and rewards for skills.

TABLE 3.7 EXPLORATORY FACTOR ANALYSIS: INDEPENDENT VARIABLES: INDIVIDUAL VARIABLES

Table 3.8 reflects the EFA results for the organisational variables. All those values that are highlighted in bold and red indicate the significance of measuring instruments in the factor analyses calculated.

In Factor 1, there were four items loaded, namely COMM2, SKILL2, SKILL3 and SKILL7, and they represent perceptions about recognition of skills. This variable was therefore labelled as recognition of skills.

Factor 2 attracted four TEAM items: TEAM3, TEAM4, TEAM5 and TEAM6. These items were all measures of teamwork. This variable is labelled as such.

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Six items (COMM4, COMM7, SKILL1, SKILL4, SKILL5 and SKILL6) loaded on Factor 3.

These items captured perceptions about skills development and training. This variable is labelled as skills development.

TABLE 3.8 EXPLORATORY FACTOR ANALYSIS: INDEPENDENT VARIABLES: ORGANISATIONAL VARIABLES

Factor 4 attracted six items: COMM1, COMM3, COMM5, COMM6, TEAM1 and TEAM2 with significant loadings. This variable was therefore labelled as organisational communication.

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TABLE 3.9 EXPLORATORY FACTOR ANALYSIS: DEPENDENT VARIABLE

Table 3.9 indicates that nine PERF items loaded on Factor 1. These items are all measures of employee job performance and this variable is labelled, in short, job performance. In Tables 3.7 to 3.10 the Eigen values are greater than 1, which indicates that the variables are true constructs. According to Cooper and Schindler (2008), Eigen values must be greater than or equal to one when the loading of variables is complete and that value will be included as the factor. Therefore, from this explanation, the researcher can conclude that the Eigen values from the factor analysis tables are at an acceptable level because they are all above and greater than one.

TABLE 3.10 THE EMPIRICAL FACTOR STRUCTURE

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Table 3.10 shows the empirical factor structures as they emerged from the EFAs. All the Cronbach alpha values of the latent variables are greater than or equal to 0.70, except for job prestige (0.58). A Cronbach alpha of a minimum of 0.50 is however acceptable for exploratory research such as this study.

Due to the emergence of new variables from the EFAs, the following new hypotheses needed to be formulated:

H08: Fringe benefits exert no influence on employee job performance H8: Fringe benefits exert a positive influence on employee job performance H09: Job prestige exerts no influence on employee job performance

H9: Job prestige exerts a positive influence on employee job performance