CHAPTER 5: RESEARCH DESIGN AND METHODOLOGY
5.16 Data processing and analysis
150 5.15.7 Collection of questionnaires
Questionnaires were returned to the researcher in order to enable the researcher to capture the responses of the respondents. In this study, respondents were given fifteen days to complete the questionnaires and place the completed questionnaires in a box that had been prepared and placed by the liaison persons at these provincial legislatures. Indeed, the respondents completed the questionnaires and place them in the box provided to them. Later the liaison person collected the questionnaires.
After the questionnaires were completed, they were put inside the provided box. After thirty days, the contact persons in the provincial legislatures (Limpopo and Mpumalanga) collected the questionnaires and subsequently sent them back to the researcher via registered post.
The researcher, confirmed receipt of questionnaires through an e-mail to the liaison persons.
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The purpose of using AMOS 18 was to evaluate the goodness-of-fit indices: Goodness of Fit, Chi-square (CMIN), Degrees of Freedom (Df), the Goodness-of-fit Index (GFI and AGFI), Normed Fit Index (NFI), Relative Fit Index (RFI), Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Root Mean Square Residual (RMR) (Boomsma, 2000; Hoyle, 1995).
The data collected from the respondents through a structured questionnaire was analysed using the descriptive method. Babbie (2013a) states that descriptive statistics are concerned with the description or summary of the data obtained from a group of individuals. The aligning of the questionnaire was in line with the hypotheses and research questions of the study.
There were two stages to analyse data. First, reliability analysis was through the application of the SPSS version 22 in order to determine and evaluate the reliability and consistency of the items measured. Secondly, the use of AMOS 18 was to evaluate the goodness-of-fit indices of the research proposed structural equation model and hypotheses.
In the second stage of data analysis, data was presented using the structural model, covariances, model, model fit measurement and both standardised and unstandardised scores to support or not to support the null hypothesis, and lastly, squared multiple coefficients (R2) was performed.
The spread of distribution of data, for example, standard deviation, is the extent to which data measures tend to cluster close together, or are widely spread over the range of values (average distance of scores from the mean).
There are commonly used statistics such as mean and standard deviation and they represent how respondents responded to different items on the scale (Babbie, 2010). In most instances, there is aggregating of responses to represent the mean average of the responses. In this study, there was aggregation of items of the constructs, as outlined below:
Items were measured on a 4-point Likert scale as outlined in the next paragraph. The scale was used to interpret the construct range from the mean score averages of between 0.0-2.0 (0%-50%) and 2.1-4.0 (51%-100%). This meant that any items rating of below the threshold of 65% in respect of the construct was not acceptable.
In reporting on employee satisfaction, all items were measured on a 4-point Likert scale. The mean score averages of between 0.0-2.0 (0%-50%) and 2.1-4.0 (51%-100%) represent
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levels of satisfaction with the item. The mean score averages of between 0.0-2.0 (0%-50%) represent levels of dissatisfaction. Lastly, the mean score averages of between 2.0-4.0 (51%-100%) represent levels of satisfaction with the item, and that any items rating of below the threshold of 65% in respect of the construct was not acceptable.
Furthermore, for levels of different constructs, the aggregated mean of each construct was divided by the number of levels of agreement in the scale multiplied by 100, in order to derive a percentage level of each variable. For example aggregated mean (2.5) divided by number of agreement levels (4) and multiply by hundred (100) equals (=) 62.5%. The adequate or minimum accepted percentage for each variable is 65%.
In order to determine the relationships between different variables a Structural Equation Model generated from AMOS 18 was used. Twelve (12) hypothesised paths formed part of the study.
The descriptions of categories of variables were through SEM that informed the Model.
There were a number of categories from the SEM, such as:
(12 variables),
7=observed variables, 5=unobserved variables, 7=exogenous and 5=endogenous.
The different shapes in the SEM represent the path variables. These shapes are in squares and circles. The circles in the SEM represent the latent variables and the squares represent measured variables. It is important to note that the generation of the SEM is in line with the hypotheses of the study. Briefly, the lines in the path denote the level of a relationship between variables.
The arrows between variables in the structure represent hypothesised relationships. These arrows represent the direction of the relationships between variables. In the SEM paths there are also bi-directional relationship represented by two arrows indicated by (e) referred to as a residual and it stands for error.
The results of the SEM are reported with the aid of both the standardised and unstandardised coefficients output, and are recorded at the significant (p-value > 0.05). This means that hypotheses results with a (p-value < 0.05) is not significant and as such, Null
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hypothesis will be accepted and hypotheses results with a (p-value > 0.05) will be considered significant, and Null hypotheses will be rejected.
Furthermore, model fit measurement and both standardised and unstandardised scores to support or not to support the Null hypothesis, and lastly, squared multiple coefficients (R2) was performed. Finally, the study drew a conclusion from the findings in a short paragraph, and a discussion and interpretation of the findings follows.