RESEARCH METHODOLOGY
3.10 Data Analysis Approaches
The data derived from the field survey was categorised into two groups as follows:
3.10.1 Qualitative Data:
The analytical approach selected for this type of data was the typological analysis, which was the method for ordering and classifying the descriptive and non-numerical information derived from the observation and PE assessment. The urban and architectural contents of each condominium were classified based on the theoretical framework set at the beginning of the research.
3.10.2 Quantitative Data:
For the numerical information derived from the field survey, the statistical analysis was planned to perform the causal analysis between independent and dependent variables. Due to the variety of the units of measurement of each indicator, several statistical methods were applied to match the four different types of data -1) nominal data, 2) ordinal data, 3) interval data and 4) ratio data- as well as to validate and robust the findings of the field survey. The statistical approaches adopted in this study were described as follows.
3.10.2.1 Descriptive Statistic
The descriptive statistical analysis was applied to narrate the demographical features of the data as well as provide the general summary about the sampling group. The three types of univariate analysis:
1) distribution, 2) central tendency and 3) dispersion, were also implemented with the data set derived from sampling group. The categorical variables measured in nominal unit, for example, the personal attributes of high-rise inhabitants, such as age, gender, etc. were examined.
The frequency and distribution of the data then depicted in the form of a percentage. The quantitative variables measured in ordinal, interval, and ratio units, such as monthly income, the level of safety concern, etc. were examined to reveal their central tendency, namely, mean, median, and
76 mode along with the dispersion analysis of the standard deviation and variance, which were also applied to these types of data.
3.10.2.2 Inferential Statistic
The inferential statistical analyses were employed to test the research hypotheses and interpret the causality amongst variables. For analysing the significant influences of the independent variables on the dependent variables in this study, the multiple inferential statistical methods were practised regarding the multi-scales of measurement as mentioned earlier. The implemented inferential statistics are as follows:
To find the answers to the first research question (RQ1: How are the three fundamental psychological variables significantly affected by the physical environmental factors and the personal factors?), the following statistical methods were implemented.
• Independent Samples t-test
This type of statistical analysis was implemented for comparing the means between two unrelated groups on the same continuous dependent variables. Based on the dataset of this field survey, the independent sample t-test was performed to investigate the significant psychological differences between two different categorical groups of independent variables, for example, to compare the distinctive degree of mental status between gender (female and male), between access control (gated and non-gated territory), etc.
• One-way Analysis of Variance (ANOVA)
This method was applied for determining whether there were any statistically significant differences of means amongst three or more independent (unrelated) groups. To illustrate, in this study, the ANOVA was performed to compare the level of mental status of the respondents from six different zones of Bangkok, to compare the psychological differences amongst the respondents with different marital status (bachelor, married, and
divorced/widow), etc.
• Pearson’s Movement Correlation Coefficient (PMC)
This statistical method was used for examining the strength of association between the core dependent variables (safety concern, privacy satisfaction, and sense of community) and their sub-variables. The PMC was run to test multiple variables at a time. Primarily, the result of PMC was for scrutinising the multicollinearity conditions amongst the variables before furthering the process of causality analysis employing simple linear regression analysis, multiple linear regression analysis, and structural equation modelling. Therefore, the detail of PMC was not included as a part of the major results' interpretation.
• Linear Regression
By definition, this statistical approach was applied for modelling the relationship between a scalar dependent variable and one or more explanatory variables (independent variables).
In this study, the simple linear regression analysis (SLR) was to examine the influential analysis for the case of one independent variable (explanatory variable/ predictor) versus one dependent variable (response variable). For instance, the SLR was performed to examine the influence of age (measured in continuous unit) on the level of safety concern.
Meanwhile, the multiple linear regression analysis (MLR) was for the case of multiple independent variables versus one dependent variable. For instance, the MLR was conducted to investigate the influences of the three types of threats' experience 1) crime, 2) behavioural disorder, and 3) emergency (scored one to five) on the safety concern of the respondents (scored one to five).
Moreover, in this research, the MLR was also applied along with the structural equation modelling to lineally redefine and enhance the robustness of the predictive modelling at the last part of the analytical procedures.
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To find the answers to the second research question (RQ2: How do the safety concern, privacy satisfaction, and sense of community associate with each other?), the following statistical methods were implemented.
• Structural Equation Modelling
Mainly, this multivariate statistical analysis technique was carried out to investigate the structural relationships between the three psychological dependent variables -safety concern, privacy satisfaction, and sense of community- as the latent constructs. In this study, the SEM allowed the researcher to analyse the statistical association of all variables at the same time. It was the statistical method that was closest to the conceptual model hypothesised at the beginning of the study. As mentioned earlier, the MLR was also performed along with the SEM to re-confirm the reliability of the statistical associations amongst these three dependent variables and to finalise and propose all dominant factors influencing each dependent variable based on the linear perspective.
After defining the variable construct and the approach of data analysis, the operationalised diagram produced for the field survey is illustrated in Figure 3.4.
Figure 3.4 The diagram of operationalised variables construct and analytical approach DV1 Safety Concern