Structural Equation Modeling Analysis Using AMOS

8/10/2019 Structural Equation Modeling Analysis Using AMOS

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Megat Shariffudin Zulkifli, DrUniversiti Putra [email protected]

Structural Equation Modeling (SEM)

Structural Equation Modeling (SEM) technique is used to test the relationships among

the variables in the model. The method involves multiple regression analysis of factors

among a single measured dependent variable and a group of predictors (Ullman 2007).

Hair et al., (2006) argued that SEM is able to examine two types of models, i.e. the

measurement model that represents the theory and the model which represent the

latent factors. A structural equation modeling (SEM) employs multivariate analyses and

will show relationships between constructs and the causal dependencies between

endogenous and exogenous variables (Hair et al., 2006). The SEM also allows two

groups comparisons which makes it suitable for testing the hypotheses of the research.

Therefore, SEM is selected in this study to maintain parsimony in the proposed model

while benefiting from its strength in testing the research hypotheses.

The study uses AMOS version 6 to examine the relationships between the independent

variables (PE, EE, SI, FC, SQ, IQ, SVQ), moderator variables (PIIT and experience)

and one mediator (satisfaction) and behavioural actual use of eBidding service. There

are two main reasons behind the decision to adopt this software. First, the software is

available in the graduate school resource centre, as such it is accessible for the

analysis of data. Second, AMOS is rarely employed in previous empirical and

conceptual research of user acceptance (Tong, 2007).


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Structural Equation Modeling (SEM) in AMOS involves drawing a circle and arrow path

diagram. AMOS is easy to learn and use and it is capable of analysing many goodness-

of-fit measures. AMOS also offers flexibility and ability to analyse numerous linear

models (Garson, 2009). The data will be analysed with descriptive and inferential

statistics tests. The following statistical methods are used in this study to organize the

data:

a) Data are keyed-in and sorted in the AMOS 6 software ;

b) An exploratory data analysis was conducted to check central tendency and

data dispersion to calculate the mean, range, standard deviation and the

variance in the data.;

c) Two test procedures were undertaken to maximize the instruments validity

and reliability;

d) Cronbachs alpha was used to analyse the reliability of the measures and the

correlations strength between each items. The internal consistency of

reliability is higher when the value is closer to 1. Reliability analysis test was

performed prior to testing the proposed model. Cronbachs Alpha was

employed for evaluating the consistency between multiple research variables


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measurements, where the generally acceptable value for reliability is 0.7

(Creswell and Miller, 2000);

e) Validity test ensures that the instruments are reliable, however an instrument

can be reliable without being valid. Confirmatory factor analysis (CFA) was

used to evaluate and establish evidence of sufficient construct validity

(Creswell, 2003). The CFA also tests whether measures of research

constructs are consistent or fit the validity of the research model. For the

model to have goodness-of-fit, assumptions of multi-collinearity,

homoscedasticity and normality distribution must be met.

4.7.4 Testing Process Using Structural Equation Modeling (SEM)

Structural Equation Modelling (SEM) was used to examine the hypotheses. SEM with

AMOS combines both regression analysis and factor analysis. SEM is a multi-variate

statistical method used to test direct and indirect associations between a predictor

variable with one or more outcome variables. SEM consists of seven major stages, from

developing the model, constructing path diagram causal relationship, building and

modifying measurement model and finally model fit (Hair et al., 2006).

a) Stage 1 : Developing a Model

The present study aims to understand the factors influencing procuring officials

behaviour in eBidding adoption in government setting. The proposed model in

the study must fulfil the established criteria for making causal relationships. Hair


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et al., (2006) argued there must be sufficient relationships between variables,

existence of cause versus effect and theoretical justifications for the

relationships. Appendix II shows the list of the indicators of the present study.


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b) Stage 2: Path Diagram Relationship

A Structural Equation Modeling model consists of an observed variable (OV)

and latent variable (LV). The relationships of observed and latent variables in

SEM are shown using path diagrams (Gefen, 2003; Tong, 2007).

i. Actual Use/Adoption This variable is an endogenous variable, which

consists of four (4) observed factor variables/indicators, namely Use1,

Use2, Use3 and Use4;

ii. Performance Expectancy (PE) -This variable is an exogenous variable,

which consists of seven (7) observed factor variables, namely PE1, PE2,

PE3, PE4, PE5, PE6, PE7;

iii. Effort Expectancy (EE) -This variable is an exogenous variable, which

consists of seven (7) observed factor variables, namely EE1,

EE2,EE3,EE4,EE5,EE6 and EE7;

iv. Social Influence (SI) - This variable is an exogenous variable, which

consists of seven (7) observed factor variables, namely SI1, SI2,SI3,SI4,

SI5,SI6 and SI7;

v. Facilitating Conditions (FC)- This variable is an exogenous variable

which consists of seven (7) observed factor variables, namely FC1,

FC2,FC3, FC4,FC5,FC6 and FC7;


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vi. System Quality (SQ) - This variable is an exogenous variable, which

consists of seven (7) observed factor variables, namely SQ1, SQ2, SQ3,

SQ4, SQ5, SQ6 and SQ7;

vii. Information Quality (IQ)-This variable is an exogenous variable, which

consists of seven (7) observed factor variables, namely IQ1, IQ2, IQ3,

IQ4, IQ5, IQ6 and IQ7;

viii. Service Quality (SVQ) - This variable is an exogenous variable, which

consists of seven (7) observed factor variables, namely SVQ1, SVQ2,

SVQ3, SVQ4, SVQ5, SVQ6, and SVQ7;

ix. Satisfaction- This variable is an exogenous variable, which consists of

seven (7) observed factor variables, namely Satis1,

Satis2,Satis3,Satis4,Satis5, Satis6 and Satis7;

x. Experience (Exp) - This variable is an exogenous variable, which

consists of five (5) observed factor variables, namely Exp1, Exp2, Exp3,

Exp4 and Exp5;

xi. Personal Innovativeness in IT (PIIT)- This variable is an exogenous

variable, which consists of five (5) observed factor variables, namely

PIIT1, PIIT2, PIIT3, PIIT4 and PIIT5.


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c) Stage 3: Structural and Measurement Models

SEM integrates both path and factor analyses including multiple observed

variables or indicators for every unobserved variable as in factor analysis and

specific paths connecting the unobserved variables in path analysis (Tong,

2007). Confirmatory factor analysis (CFA) establishes a model fit to the data.

Unobserved variable is tested to see if they are measured by the observed

constructs where each unobserved variable is assumed to be related with a set

of observed variables (Tong, 2007). The measurement model is then evaluated

using the Goodness-of-Fit (GoF) test. By analysing the results from

measurement and structural models testing, a more precise hypothesis testing

can be undertaken (Garson, 2009).

d) Stage 4: Matrix Type and Proposed Model Estimation

Data screening was done to check the raw data in order to identify any missing

data, which involve testing data normality and skewness. The key assumption of

SEM software is that endogenous and exogenous variables must be normally

distributed. A distribution is considered as normal if it has skewness indices of

less than 3 and kurtosis value being less than 10 (Tong, 2007). In addition,

multi-collinearity problem will occur if and when the observed variables are

closely associated. Pearson coefficient test detects multi-collinearity which is

not present if the coefficient value is less than 0.8 (Sekaran, 2000).


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e) Stage 5: Assessing the Model Identification

The first type of model identification is a simple model, which has the same

number of variables as the co-variance elements. In this study, an over-

identified model was obtained because it will significantly enhance the reliability

of the estimates. An over-identified model has more co-variance elements

compared to variables (Garson, 2009).

f) Stage 6 : Evaluating Goodness-of-Fit Criteria (GoF)

Goodness-of-fit is employed to determine the model rejection or acceptance for

further analysis (Tong, 2007). Although AMOS offers 25 different goodness-of-fit

measures, however Garson (2009) advised researchers not to report all the 25

goodness-of-fit measures. He argued that by reporting all goodness-of-fit

measures, it implies that the researcher is on a fishing expedition. However,

Kline (2005) recommended the four most commonly reported goodness-of-fit

tests are chi-square; Goodness-of-Fit Index (GFI), Normed Fit Index (NFI),

Comparative Fit Index (CFI), and Root Mean Square Error of Approximation

(RMSEA).

Chi-square (2) is the most popular goodness-of-fit test used for SEM (Garson,

2009). AMOS outputs list chi-square as chi=\CMIN. The model chi-square

values tend to decrease (better fit) when more paths are inserted or created in a


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model. On the other hand, significant 2 value shows poor model fit (Garson,

2009; Kassim, 2001; Tong, 2007). Relative chi-square (2/df) is used in SEM so

that the 2 value will be less influenced by the size of the sample (Ullman,

2007). Relative X2 is listed in AMOS as RATIO = Cmin/df, and a relative X

2

value of 3 or less is considered as acceptable (Tong, 2007).

Comparative Fit Index (CFI) is another measurement for Goodness-of-Fit test.

CFI is used to measure the model fit improvement compared to a null model

(Garson, 2009; Tong, 2007). AMOS lists CFI as CFI=\cfi (Tong, 2007). CFI value

which is close to 1 indicates an excellent model fit (Garson, 2009; Tong, 2007).

Normally, CFI has to be .90 for a model to be accepted. CFI has to be .90 for

a model to be accepted (Tong, 2007).

Normed Fit Index (NFI) is an alternative index of CFI (Tong, 2007). NFI ranges

from 0 (bad fit) to 1 (good fit) (Ullman, 2007). NFI value of 0.50 means the SEM

model improves by fifty per cent in comparison to null model (Tong, 2007). NFI

values above .95 are considered outstanding, NFI values from 0.90 to 0.95 are

desirable, but the researcher has to re-specify the model if NFI value is below

0.90 (Garson, 2009; Tong, 2007).

Tucker Lewis Fit Index (TLI) is another incremental fit index similar to NFI

(Garson, 2009). TLI is relatively independent of sample size. TLI is expressed

as the fit per degree of freedom (Tong, 2007). AMOS lists TLI as TLI =\TLI


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(Tong, 2007). TLI value close to 1 or TLI value > 0.90 shows a good model fit,

while TLI value below 0.90 shows the need to re-specify the model (Tong,

2007).

Another important indicator is Goodness of Fit Index (GFI) which analyses the

per cent in the model co-variances (Garson, 2009). AMOS lists GFI as GFI

=\gfi (Tong, 2007). GFI should be equal to 0.9 or higher for a parsimonious

model (Garson, 2009) whereas, Schumacker and Lomax (2004) recommended

a GFI value of 0.95 or higher.

Another criterion for Goodness-of-Fit test is Root Mean Square Error of

Approximation (RMSEA), which is the difference per degree of freedom (Tong,

2007). RMSEA is an effective measure of model fit as there is no need to

compare a SEM model with a null model (Tong, 2007). AMOS lists RMSEA as

RESEA =\rmsea. RMSEA value of 0.8 as good model fit (Tong, 2007).

g) Stage 7: Modifying the Model

The model was examined for potential model modifications after an initial SEM

model was established. A re-specification process was done by trimming the

model by adding path arrows and removing parameters to achieve a model with

good fit (Garson, 2009).

Structural Equation Modeling Analysis Using AMOS

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