Community Resilience to Climate Change: Theory, Research and Practice

140 equally distributed. Most respondents followed Hoa Hao Buddhism, while very few respondents belonged to the Cao Dai or Catholic religion. Poor households account for 39.4% of the sample, followed by well-off households (31.8%) and medium-income households (28.8%). Average household income was $2,918.0 per year ($1 or approx. VND 20,830.0 in 2010). However, the average income of poor households was $763 per year. For medium-income households it was $2,553.0 per year, while better-off households had an average income of $5,909.0 per year. The per capita income of each person was an average of $600.0 per year. Per capita income in poor households was $168.0 per year. In medium-income households, per capita income was $576.0, and it was $1,161.0 in better-off households. Table 2. Statements for measuring household resilience to floods in the MRD A factor analysis was used for combining related variables into “composite” variables for conceptualizing components of household resilience to floods. Factor analysis helps us to identify patterns in responses to a set of questions (de Vaus 2002). The purpose of this technique is to reduce the large amount of variables to a smaller set of underlying variables by creating factors (Kim and Mueller 1978). The principal component factor method is used in this analysis. There are a number of methods involving rotation variables including the quartimax method, the equamax method, and the varimax method (Kim and Mueller 1978). One of the most frequently used methods is the varimax method, which aims to minimize the number of variables that have a high loading on a factor. This approach was widely used when identifying the factors of the vulnerability analysis (Cutter et al. 2003, Fekete 2009). Because binary variables were not suitable for a factor analysis, each item response was standardized (z-score) before conducting a factor analysis using SPSS software. This method was used by identifying underlying factors in measuring social vulnerability to natural hazards (Cutter et al. 2003). Factors will be selected if they have an Eigenvalue greater than one. The results of factor analysis from SPSS were also triangulated by using MPLUS software with the original nonstandardized data.