CHAPTER 3 RESEARCH METHODOLOGY
3.5 Quantitative Research
3.5.5 Data Analysis
Step 9: Eliminate bugs in the pre-test
Pilot studies help better target areas of interest and refine initial ideas on the implementation process concept and analyze them. The pilot study process also serves as a preliminary test for the questionnaire and is designed to reduce response errors by dispelling ambiguity and keeping questions precise. A pilot version of the survey questionnaire was sent to conduct 90 food tourists who attend the local food festivals for a pre-test. Based on the pilot process’s responses, a revised and modified survey version was created. The questionnaire’s number was also increased from 69 questions to 72 questions. According to the calculations, the Kaiser-Meyer-Olkin Measure of the sampling adequacy (KMO) of questionnaires was .957, more than .60 and above, including Barlett’s Test of Sphericity, significant at .05 (Pallant, 2005).
information is investigated to meet the requirements of analysis by SPSS version 24.0 and AMOS version 22.0 were used in the data processing, analysis, and reaching the objectives that divided the analysis as follows;
1) The fundamental information analysis is the diagnosis for demonstrating the distribution of variables by using descriptive statistics such as mean and standard deviation (S.D.) to answer objectives 1 and 2 of this research.
2) Cluster analysis or traveler market segmentation led to marketing strategies for local food festivals in Phuket, Thailand, to answer objective 1 of this research.
3) Data analysis for testing the research hypothesis includes the analysis of the structural equation model (SEM) to answer objective 2 of this research.
The procedure of diagnosing the structural equation model by the AMOS program
The researcher will analyze the trials through the fundamental statistic data using descriptive statistics, including frequencies, means, percentages, and standard deviation. Moreover, the researcher will analyze the data with Multiple regression analysis, factor analysis, confirmatory factor analysis (CFA), and path analysis (SEM) according to the previous research objectives as follow:
First, the multiple regression analysis is an exploratory tool, this assists in the influence between an independent and dependent variable.
Second, the factor analysis is applied for a statistical method that will describe and decrease to a fewer number of variables from the large number’s factors. This technique can extract a maximum collective variance from the overall factors and places them into the standard scores. This method can assume various assumptions, including a linear relationship with no multi-collinearity, relevant variables in an analysis, and an actual correlation between factors (Bryant & Yarnold, 1995).
Third, confirmatory factor analysis (CFA) will assist the researcher in dealing with the measurement models involving the relationships between the experiential indicators, especially in behavioral observational assessments (Brown & Moore, 2012; Harrington, 2009).
Lastly, the technique is path analysis in the regression model. The path analysis presents the goodness of fit statistic that will be calculated in order to see the fitting model (Li, 1975); the data processing and analysis are as follows:
1) Cross-tabulation was used to determine if each respondent’s other characteristics, such as sex, education level, career, duration of work experience, etc.
2) The general data included the level of urban tourism development in Thailand by statistical data, i.e., frequency distribution, percentage, and elaborating the analysis in each category analyzed.
3) For the missing answers, the principle of “replacing missing value by series means” was applied to solve the problem. Replacing missing values is a way to approximate missing values to make complete information beneficial for analyzing all data and bringing about an actual result. This methodology is much better than leaving that missing data and doing an analysis (Kanlaya, 2007)
4) Hair et al. (2010) discuss the idea that structural equation modeling (SEM) is a multivariate technique combining dimensions of factor analysis and multiple regressions. SEM allows the researcher to simultaneously study a series of interrelated dependence relationships among the measures or observed variables and latent or construct variables and the relationships among many latent constructs. SEM has three main characteristics that are different from other multivariate techniques, as follows:
(1) SEM can simultaneously estimate multiple and interrelated dependent relationships.
(2) SEM can represent unobserved or latent concepts in the relationships and solve measurement errors in the estimation process.
(3) SEM is one model that can explain all relationships.
Sabherwal and Becerra (2003) have stated that “it (SEM) operates the constructs of interest with a measurement instrument, and tests the fit of the model to the obtained measurement data with a hypothesis as a model.”
5) SEM consists of the confirmatory measurement model or factor analysis and the confirmatory structural models or path analysis assessment. Factor analysis analyzes the latent construct’s practical measures, while path analysis tests the causal relations among the latent constructs (Anderson & Gerbing, 1988). In
assessing the model fit, four indices an employed: χ 2 (chi-square), GFI (goodness of fit index), CFI (comparative fit index), and RMSEA (root mean square error of approximation).
6) Chi-square measures the model’s overall fit with the data (Joreskog, 1993). Considering problems with the χ 2 test, although a p-value indicates no significant difference between the observed and estimated covariance matrices, this does not suddenly imply a good model fit; the researcher should always complement it with other GOF indices. The χ2 value and the model’s degrees of freedom should always be reported (Hair et al., 2010). Table 3.2 shows a guideline for using fit indices in different situations based primarily on simulation research (Hair et al., 2010).
7) The Goodness of Fit Index (GFI) measures the squared residuals from the empirical data’s prediction. Its value ranges from 0 to 1 (low to perfect fit). A GFI closer to .90 and 1 indicates a better fit. The Root Mean Square Error of Approximation (RMSEA) indicates the overall fit with the data. The acceptable values range from .05 to .08 (Hair et al., 2010).
Table 3.4 Characteristics of Different Fit Indices No. of Stat.
Vars.(m) M ≤ 12 12 < m < 30 M ≥ 30
χ 2 Insignificant
p-values expected
Significant
p-values even with good fit
Significant p-values expected CFI or TLI .97 or better .95 or better Above .92
RNI May not diagnose
misspecification well
.95 or better Above .92
SRMR Bias upward, use
other indices
.08 or less (with CFI of.95 or higher)
Less than .09 (with CFI above .92) RMSEA Values < .08
With CFI = .97 Or higher
Values < .08 With CFI = .95 Or higher
Value < .07 with CFI of .97 or higher Source: Hair et al. (2010).
Thus, SEM has been one of the most popular models for research analysis with many advantages and has outperformed traditional multiple regression analysis.
In addition, SEM provides a mechanism for accounting errors in measuring variables.
For this reason, SEM was considered appropriate for this study. In SEM, constructs or latent variables are shown in circles, and measured or observed variables are shown in rectangles.
In conclusion, the researcher will use the completed questionnaire for data analysis after accuracy. Then, the samples will be analyzed by using the statistic technique with descriptive data, including frequencies, means, percentages, and standard deviation.
Next, the relationships of variables will be analyzed by the Pearson correlation coefficient. Then, the Structural Equation Modeling (SEM) will be used for data analysis as a covariance Matrix method. Next, the researcher will apply Confirmatory Factor Analysis (CFA) to construct validity to examine the influence of direct and indirect variables in a local food festival in Thailand. Moreover, this stage will assist in reducing the latent variables from the observed variables by the statistic software program. Finally, the goodness to fit the measurement will be used for examining the empirical data.