Phase 1: Validity and reliability

Phase 1 consisted of the calculation of exploratory factor analysis (EFA), CFA and Cronbach’s alpha coefficients.

6.7.1.1 Validity

According to Babbie (2014), Leedy and Ormrod (2013) and Neuman (2011), validity is concerned with the extent to which an instrument measures what it is supposed to measure in a consistent and accurate manner. The current study calculated both EFAs and CFAs to examine the psychometric structure of a proposed SWB construct (Chapter 4) and to test the hypothesis that SWB is a latent variable comprising Happiness, Optimism, Self-esteem, and Engagement.

(a) Exploratory factor analysis

One of the aims of this study was to conceptualise the psychological construct of SWB. Previous academic research suggests that SWB at work is comprised of well-being- related psychological constructs (e.g. Happiness, Optimism and Self-esteem) and Engagement (Diener, 2000b; Eid & Larsen, 2008; Frey & Stutzer, 2012; Rothmann, 2014; Seligman, 2011). The researcher proposed a model that these four constructs could be used to measure SWB (see Section 5.4).

According to Cooper and Schindler (2014), factor analysis is used to reduce the number of variances, to detect structure in the relationship between variables, as well as to discover the underlying construct that explains the variance. EFAs were conducted on the four proposed psychological constructs of SWB, namely Happiness, Optimism and Self-esteem measured by the TEIQue, and Engagement measured by the WEQ28. The first proposed a single-factor solution, and the second a two-factor solution.

Shapiro–Wilk tests of normality were used for this research. Shapiro–Wilk tests are used to assess whether a variable can be considered normally distributed (Kaplan, 2000). This is important as understanding the distribution of the data has an influence on the appropriate statistical tests to use in the inferential statistics section. The null hypothesis for this test was that the data are normally distributed. If the p-value is

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greater than 0.05, then the null hypothesis is not rejected (Kaplan, 2000; Oztuna, Elhan, & Tuccar, 2006).

To minimise error, principal axis factoring (PAF) was deemed the appropriate method of factor estimation because several of the variables were found to be non-normally distributed (Costello & Osborne, 2005; Williams, Onsman, & Brown, 2010). The principal axis factor analysis was used for the purpose of understanding the covariation among variables (Costello & Osborne, 2005). Oblimin rotation was used (Williams et al., 2010) as factor interdependence was assumed. Direct oblimin rotation is an oblique rotation that is used to maximise the variance of the loadings of a factor on all the variables in a factor matrix. It minimises the variables that have high loadings on any one specific factor (Costello & Osborne, 2005).

Factor loadings greater than ± .30 are considered to meet the minimal level. Loadings of ± .40 are considered more important, and if the loadings are ± .50 or greater, they are considered practically significant (Hair, Black, Babin, Anderson, & Tatham, 2010; Schumacker & Lomax, 2010). Factor loadings of .30 or less magnitudes should be discarded, as they do not meet the minimum level of practical significance (Costello & Osborne, 2005).

(b) Confirmatory factor analysis

Confirmatory factor analysis (CFA) is used for psychometric evaluation of measures, construct validation and relationships between constructs (Hurley et al., 1997).

CFA was used for this study to test the proposed psychometric structure of SWB further. SWB was entered as a latent variable made up of four observed variables: Happiness, Optimism and Self-esteem measured by the TEIQue, and Engagement measured by the WEQ28. A second CFA was calculated to assess whether SWB was better represented as a second-order latent variable.

The Satorra–Bentler scaling correction factor was used for this research (Satorra & Bentler, 2001). As the data was non-normally distributed, a robust ML estimator was used to assess the fit of the model to the data to measure the goodness-of-fit. As there is no consensus within the literature as to which measure of goodness-of-fit is best,

119 researchers advise using multiple tests (Kline, 2012). For this study, the main indices used were:

• the χ2/df ratio, where an excellent fit is indicated when χ2/df < 3.00 (Bryant & Satorra, 2012);

• comparative fit index (CFI), where a value of 0 reflects no fit, while a value of 1 is a perfect fit (Hooper, Coughlan, & Mulen, 2008) and values close to 0.90 reflect an acceptable fit (Byrne, 2010);

• the Tucker–Lewis Index (TLI), where values over .90 are considered an excellent fit of the data (Schumacker & Lomax, 2010); and

• the standardised root mean residual (SRMR), where values > .08 are considered a good fit (Kline, 2005).

6.7.1.2 Reliability

‘Reliability’ refers to how consistently a measuring instrument derives the same results when measured between different groups of the same population, and the consistency with which it measures what it is supposed to measure (Bryman, 2010; Foxcroft & Roodt 2005; Leedy & Ormrod, 2013; Neuman, 2011). It is the most important psychometric indicator used to determine the usefulness and the accuracy of instruments (Von der Ohe, 2014) and whether the results are repeatable (Bryman, 2010). Tests of this nature are conducted to ascertain whether the instrument can be relied upon to provide reliable information if the survey is administered repeatedly to different groups under similar conditions (test–retest).

For this study, the reliability of the measuring instruments, the TEIQue, the Work-life Balance Scale and the WEQ28 was determined by calculating the internal consistency reliability, where each item on a scale correlates with another item, ensuring that a test measuring the same thing more than once will produce the same outcomes or results (Terre Blanche & Durrheim, 2006). The Cronbach alpha coefficient (α) is a widely used estimate of internal consistency and was used in the current research to assess the scales and subscales and to confirm the reliability of the measuring instruments. The Cronbach’s alpha coefficient is the estimate of consistency of responses to different items of the measuring instruments, and ranges from 0 (no internal consistency) to 1 (the maximum internal consistency score) (Tredoux & Durrheim, 2013).

120 Furthermore, according to Tredoux and Durrheim (2013), a reliability coefficient of .70 is adequate for research instruments. This means that, in this research, a Cronbach’s alpha coefficient of .70 was used in the data analysis to determine the acceptable reliability coefficient of the TEIQue, the Work-life Balance Scale and the WEQ28.