Structural Equation Modelling (SEM)

Structural Equation Modelling (SEM) is analysis done to answer the last question in the phase of profile evaluation which is to develop the model of effective school-based financial management in Malaysia. SEM was used as a tool for model testing in order to study on the relationship among few variables in the research data through a combination of factor analysis and regression or path analysis (Chua, 2006; Hox & Bechger, 1998). The researcher will come out with the hypothesis model which will be tested based on their fitness against the data collected from respondents. Therefore, SEM was primarily known for its confirmation procedures rather than exploratory procedures of the hypothesized model.

SEM has been applied to satisfy the second objective of this phase due to several important reasons. Based on its confirmatory feature, it was used as a tool to verify the empirical data gained in the survey with the hypothesized model of effective school- based financial management. Furthermore, SEM considers several equations simultaneously contrasted to ordinary regression analysis. The relationships among variables in the profile have to be tested so that to give better picture on the elements and financial functions included in the profile developed in the second phase of profile design. In addition, the analysis of SEM using AMOS SPSS also provides the

modification indices which could generate better fitness of the model through additional relationships between variables in the modified structural equation model. Thus, SEM is considered as a comprehensive statistical approach that could evaluate the profile and further develop the best-fit model.

As described by Zainuddin Awang (2012), SEM is a powerful statistical technique with ability to perform several main statistical tasks. The tasks are such running the Confirmatory Factor Analysis (CFA), analysing multiple regression models simultaneously, estimating the correlation and covariance in a model, modelling the inter-relationships among variables in a model, analysing the path analysis with multiple dependants and also analysing regressions with multicollinearity.

SEM would identify the fitness of the variance and covariance of the collected data with the suggested model through few indicators or goodness of fit indices. These indices were presented as the Chi-Square value, Baseline Comparison, and Root Mean Square Error of Approximation (RMSEA). Descriptions of analysis properties of SEM presented for this analysis were described as Table 3.27:

Table 3.27

Description of AMOS Analysis Properties

Analysis properties Descriptions

Maximum Likelihood Estimates

Regression Weights

The value of Critical Ratio (C.R.) shows the significant of standardized regression weight in measurement model for every latent variable and its indicator variable. C.R. not in the range +/- 1.96 is significant at p < 0.05 level

Standardized

Regression Weights

The high value of Standard Regression Weight in the column of Estimate shows that indicator variable could significantly represent its latent variable.

Squared Multiple

Correlations

Column Estimate in the Squared Multiple Correlations shows the value of explained variances for all endogenous variables in model

Modification Indices Modification Indices (M.I.) suggest for modification indices

if the results of Chi-square and RMSEA shows the hypothesized model significantly not fit with the research data. Nevertheless, any modification on the variable relationship should be backed by good theoretical consideration.

Table 3.27, continued

Description of AMOS Analysis Properties

Analysis properties Descriptions

Chi-square Goodness of Fit

Chi-square Goodness of Fit with probability level p < 0.05 indicates that the hypothesized model is significantly not fit with the collected data

CMIN CMIN (the likelihood ratio Chi-square) shows the value of

Chi-square Goodness of Fit together with Degree of Freedom (DF) and its probability (P). The value is considered to be significant with p < 0.05 and the hypothesized model was not fit with the data.

Baseline Comparisons Baseline Comparison consisted the value of NFI Delta1

(Normed Fix Index), RFI rho1 (Relative Fix Index), IFI Delta2 Incremental Fix Index), TLI rho2 Tucker-Lewis Fix Index) and CFI (Comparative Fix Index). Baseline Comparisons with the value of 0.90 indicated that the regression model suggested significantly fit with the collected data.

RMSEA Root Mean Square Error of Approximation (RMSEA) with

value of less than 0.06 indicates that the regression model was significantly fit with the collected data.

Basically, analysis of SEM is begins with specific theory or model that become the framework of the study. This theoretical or conceptual framework consist several variables that become foundation of analysis and is also incorporated with hypotheses in order to be tested. The variables of the study are measured using a set of items in a questionnaire and measured using either interval or ratio scale. Whereas, latent construct are the variables represent hypothetical concept of something which could not be measured directly.

In order to facilitate the analysis of SEM, AMOS software or Analysis of Moments Structure software has been used. AMOS software was selected due to its user friendly interface as compared to other similar software in its class. As supported by Zainuddin Awang (2012), the advantage of AMOS is its graphic representation of the model which requires researcher to only draw the schematic diagram without writing specific instructions through computer program. Furthermore, researcher has performed the analysis by using the bootstrap procedure. Bootstrap is identified to be one way of

dealing with small samples and non-normal data particularly in Structural Equation Modelling.

3.7 Chapter Summary

The development of profile of effective school-based financial management has been designed based on the framework of developmental study which consisted of three main phases that were the phase of need analysis, phase of profile design and phase of profile evaluation. The first phase of need analysis was conducted through interview sessions with 10 principals of schools with guided financial autonomy in the Klang Valley. Then, the second phase of this research was conducted through three rounds Delphi method consisting of the semi structured interview and the other two rounds of surveys with the panel of experts. Fifteen (15) individuals were participants based on specific criteria as an expert in school financial management in Malaysia. The result of this phase has become the constituent of the profile which then evaluated in the third phase of profile evaluations.

The profile evaluation was done through survey conducted with the cluster schools in Malaysia. The total of 119 school leaders from 170 cluster schools in Malaysia have become the sample of the study. The data had then been analyzed with Structural Equation Modelling (SEM) for developing a model of effective school-based financial management in Malaysia. The summary of research design for all phases in this study is described by the following research matrix (Figure 3.6).

Research Questions Methodology and sample

Data analysis

Phase 1: Need analysis

a. What is the importance of having the profile of effective school-based financial management in Malaysia? b. Who are the people responsible for

implementing effective school-based financial management in Malaysia? c. What are the needs required for

implementing effective school-based financial management in Malaysia?

Semi structured interview with 10 principals of schools with guided financial autonomy

Thematic qualitative data analysis

Phase 2: Profile design

a. What are the main elements of effective school-based financial management in Malaysia agreed with the highest consensus among the panel of experts?

b. What are the practices of effective

school-based financial management in Malaysia agreed with the highest consensus among the panel of experts? Delphi method Round 1: Semi structured interview Round 2 and 3: Structured questionnaires Sample for round 1, 2 and 3 are 15 experts as follows:

- 2 lecturers from public higher learning

institutions

- 2 lecturers from IAB - 3 auditors from school audit division

- 5 excellent principals and

- 2 excellent head teachers from schools with excellent financial performance - 1 experienced school inspectorate a) Analysis of descriptive statistics: Mod, median, inter quartile range by Words 2010. Analysis of Wilcoxon matched-pairs signed-test using SPSS version 19

Phase 3: Profile evaluation

a. What is the level of practices of effective school-based financial management by cluster schools in Malaysia?

b. What is the model of effective school- based financial management based on the Malaysian context?

Survey through distributions of questionnaires to all cluster schools in Malaysia. Analysis of descriptive statistics: Frequency and percentage, mean, and standard deviation. Analysis of structural equation modelling (SEM) using AMOS SPSS version 20.

CHAPTER 4

FINDINGS

4.1 Overview

This chapter presents findings on the study done to answer the research questions and the purpose of study to develop the profile of effective school-based financial management in Malaysia. Basically, this study has been designed to cover three important phases that are phase of need analysis, phase of profile design and phase of profile evaluation. Every phase was designed with specific research instruments which covered both the qualitative and quantitative aspects of research design. The analysis were done and the finding were reported accordingly together with their explanation and related illustration. This chapter is divided into three parts: findings for phase of needs analysis, findings for phase of profile design and findings for phase of profile evaluation.

The objectives of the needs analysis phase are to find the importance of having the profile of effective school-based financial management in Malaysia and the relevant needs and people responsible in the implementation of effective school-based financial management in Malaysia. Then, the phase of profile design will design the lists of elements and practices of effective school-based financial management in Malaysia agreed with the highest consensus among the panel of experts. Finally, the objectives for phase of profile evaluation are to measure the level of practices of effective school- based financial management by cluster schools in Malaysia and to develop the model of effective school-based financial management based on Malaysian context.

4.2 Phase of Needs Analysis

The first phase of need analysis has been carried out through qualitative study which involved semi-structured interview with 10 participants. The study was conducted to answer the following research questions that are:

a) What is the importance of having the profile of effective school-based financial