Selecting a Data Analysis Method and Justification

In research the data analysis involves several stages starting from conducting basic statistics (e.g. Descriptive Statistics, Multivariate Normality Test, Outlier Test, Co-linearity and Linearity Test) to advanced statistics such as measurement model analysis (CFA or EFA) and Model analysis (hypothesis testing). There are numerous techniques available to perform model analysis. However, selecting a technique requires a careful consideration as each method has its own advantages and disadvantages. Data analysis approaches can be divided in-to two main categories:

first generation (e.g. simple linear regressions, multiple regression, ANOVA, and MANOVA) and second generation (e.g. SEM). Review of the literature indicates that the second generation approaches have many advantages over the first generation approaches. For the past two decades second generation techniques have gained popularity among IS researchers. For example, a study by Gerow et al. (2010) shows that just over 70% of articles from early 1990 to 2008 published in the IS leading journals including Management Information Systems Quarterly, Information Systems Research, and Journal of Management Information Systems utilised a second generation approach. This is because the researcher can achieve better results using second generation approaches compared to using first generation approaches (Gerow et al., 2010). One of the major drawbacks of first generation approaches is that the researcher can only test one layer of relationships between independent variables (IVs) and a dependent variable (DV) in a single analysis (Hair et al., 2010). This signifies that first generation approaches can limit the ability of researchers to perform more than one layer of relationships between IVs and DVs at a time. And therefore, researchers are unable to test a model with more than one DV in a single analysis. However, this can be easily achieved through a second generation approach like SEM. Therefore, this has been seen as a major advantage of the second generation approaches over the first generation approaches (Hair et al., 2010).

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Moreover, using a second generation approach allows a researcher simultaneously to check for mediating factors whilst testing the relationships between IVs and DVs (Hair et al., 2010). However, this cannot be achieved using first generation approaches. In addition, having the ability to estimate direct and indirect effects during model estimation can be seen as another advantage of the second generation techniques over the first generation methods (Hair et al., 2010). Furthermore, other advantages of second generation techniques include estimating the error variance parameters (Hair et al., 2010). First generation techniques ignore measurement error, and therefore using first generation methods can lead to inaccurate results, in the case when measurement error occurs. Nevertheless, this problem can be solved by employing a second generation approach like SEM. In addition, through a second generation technique researchers can assess both the measurement model and the structural model in a single test (Hair et al., 2010). This allows researchers to conduct the analysis in fewer steps compared to first generation techniques. Table 5-6 summarises the advantages of second generation approaches over first generation approaches.

Table 5-6: Second Generation Approaches Vs. First Generation Approaches

Features 1^st

Generation

2^nd Generation Testing more than one layer of relationship in a single analysis No Yes

Suitable for models with more than one dependent variable No Yes Allow for testing direct and indirect effects in a single analysis No Yes Allow a variable to work as both IV and DV in a single model No Yes Suitable for both recursive model and non-recursive model No Yes

Estimate the error variance parameters No Yes

Testing the measurement model and the structural model in a single analysis

No Yes

The above evaluation indicates that a second generation approach is seen as the more rational choice for this study, since the proposed framework includes several DVs and this requires conducting a series of regression analyses in a single test.

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Accordingly, SEM was chosen as the data analysis approach. Several techniques and programs such as LISERL, AMOS, EQS and PLS-Graph are used to perform SEM (Hair et al., 2010). Based on statistical algorithms these are also divided into two categories such as covariance-based SEM approach (e.g. LISERL and AMOS) and partial-least-squares-based SEM approach (e.g. EQS and PLS-Graph) (Hair et al., 2010, Gerow et al., 2010). Covariance-based can be used in research aiming to develop and test theory as it enables researchers to find the overall model fit through examining the generated set of fit indices (e.g. Chi-square (X²), Normed (X/df²), and Standardised Root Mean Square (SRMR). This way a researcher can determine the best fitted model to the collected data in comparison to the proposed model. Whereas, PLS is more suitable in exploratory studies because it tests model fit through examining the paths and square roots (R²) (Petter et al., 2007). Taking into consideration the primary aim of this study (model development) and the confirmatory (theory testing) nature of this study, then SEM is again a rational choice.

Further, IS scholars have suggested that the degree of knowledge and time are the two important factors that researchers should consider in reaching a better decision when selecting an analysis technique (Gerow et al., 2010). Considering these two factors, it was decided to use AMOS as the main data analysis technique.

5.8 SEM

In the earlier section SEM was selected to analysis the data in this study. This is because it is the most widely used data analysis technique which provides many advantages over other analysis techniques. One characteristic of SEM is that it enables researchers to estimate the measurement model and the structural model (causal relationships) simultaneously; this known as a ‘one step approach’ (Hair et al., 2010). However researchers have advised using a two-step approach (Hair et al., 2010), where the first step involves testing the constructs’ reliability and validity and the second step focuses on testing the theoretical framework or the structural model.

Conducting SEM requires several considerations and these are discussed in the

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subsequent sections. For the purpose of this study, SPSS version 21 was used to conduct the preliminary analysis and AMOS version 19 was used to conduct the SEM analysis.