The present study conducted data analysis in three steps: data cleaning, descriptive analysis, and structural equation modeling (SEM) (Hair et al., 2010). Data cleaning addressed missing data, outliers, and assumptions (i.e. normality, homoscedasticity, linearity, and
correlated errors). Missing data refers to the absence of data from survey; the extent of missing data could affect generalizability of results. Missing data could be ignorable if they are results of research design, such as the population that is not covered in a sample; however, missing data need to be addressed if they are nonrandom and are the results of procedural factors, such as systematic errors in data entry or respondents’ collective tendency of avoiding answering a particular question. A rule of thumb is that random missing data under 10% can be ignored. In cases where data missing is nonrandom, potential remedies include imputation, case substitution, mean substitution, etc. (Hair et al., 2010). Second, outliers refer to the observations that are distinctively different from the rest of the observations. Outliers are not inherently beneficial or problematic, but need to be examined or adjusted within the research context. Methods of multivariate detection should be used to determine whether outliers are legitimate or warrant elimination (Hair et al., 2010).
107
Assumption testing involves testing the assumptions underlying multivariate analysis, which is indispensable for making strong statistical inferences. There are four important
assumptions to be tested. First, normality refers to the correspondence between data distribution and normal distribution, which is the benchmark of statistical analysis. Normality is a required assumption for the F and t statistics, and large deviation from normal distribution renders results invalid. Two possible patterns of nonnormal distribution are kurtosis (i.e. extent of sharpness or flatness) and skewness (i.e. extent of unbalance to the left or right). A rule of thumb for
normality is that the effect of violation is minimal in a sample of over 200 cases. Second, homoscedasticity is the assumption of dependent variables’ variance being equal across all predictor variables. This assumption is desirable because the variance of the dependent variable needs to be explained to equal strengths by all predictor variables instead of just a small range of them. A violation of homoscedasticity can be adjusted via data transformation. Third, linearity refers to the model’ predictability of related variables; specifically, linearity represents the linear relationship of predictors and outcome variables in terms of a constant unit change of the
dependent variable for that of a predictor variable. The assumption of linearity is the foundation of all regression techniques and factor analysis. Lastly, the absence of correlated errors is the assumption that prediction errors are unsystematic or uncorrelated with one another (Hair et al., 2010).
Descriptive analysis was conducted in two parts: the examination of respondents’
demographic profile, and their answers for the research constructs. Specifically, respondents’
demographic profile, including age, gender, education, ethnicity, travel purpose, length of stay, travel companion, size of travel group, and familiarity with Mexico were analyzed based on their
108
frequency, standard deviation, and percentage. Respondents’ answers to research constructs were reflected by mean and standard deviation.
Structural equation modeling (SEM) is a multivariate technique widely used in theory-testing. This method specifically applies to the situation where a variable is both a dependent and independent variable in the same theory, which happens in model-building that involves a series of dependence relationships. The analysis of dependence relationships when a variable is
simultaneously dependent and independent cannot be conducted with regular multivariate methods such as regression, hence the importance of SEM. There are two phases of SEM: a confirmatory factor analysis (CFA), and an analysis of the structural model (Anderson &
Gerbing, 1988). The purpose of conducting a CFA is to establish a measurement model where latent variables are properly represented by a summated scale; simply put, in this phase, researchers assess how each scale item individually and collectively measure a concept. Factor loadings, reliability, and validity of the measurement items will be tested for CFA.
Factor loadings refer to the correlation between measures and factors, which should be above the threshold of 0.7. Reliability indicates the extent to which items capture consistent results from respondents (Hair et al., 2010). Reliability is considered satisfactory if the item-to-item correlation, reflected by Chronbach’s alpha, exceeds the threshold of 0.7 (Hair et al., 2010).
Assessment of validity involves convergent validity and discriminant validity. Convergent validity evaluates the degree to which two measures of the same construct correlates (Hair et al., 2010), which is assessed via the average variance extracted (AVE), namely the percentage of variation explained by the items of a construct. AVE should be greater than 0.5 to be acceptable (Bagozzi & Yi, 1988).
109
In the second phase, Partial Least Square-Structural Equation Modeling (PLS-SEM) will be conducted using Smart-PLS. Results from PLS-SEM will be used to examine the relationship between dependent and independent variables. A key lesson is that the results of SEM cannot be stand-alone explanation of a phenomenon; rather, literature support plays a vital role in the ultimate explanation. The fit indices for PLS-SEM are SRMR ≤ .08 and NFI ≥.90 (Henseler, Hubona, & Ray, 2016), or R2 values. R2 values indicate the strength of paths, which is interpreted as substantial (R2=0.67), moderate (R2=0.33), or weak (R2=0.19) (Chin, 1998).
110
CHAPTER FOUR: FINDINGS
This chapter presents findings of data analysis. Detailed explanation is provided for the process of data collection and the results of statistical analysis.