Data Analysis and Testing of the Hypothesis - Statistics Project Example

Download file to see previous pages The paper "Data Analysis and Testing of the Hypothesis" is a perfect example of a statistics project on statistics. Data analysis chapters divided into four main chapters Data Analysis I, Data Analysis II, Data analysis III, and Data analysis IV. Firstly, Data Analysis I presents some descriptive statistics and explores the demographic characteristics of the respondents, and data is checked to see whether it meets the pre-requirements for multivariate data analysis. Once the data is considered fit from the preliminary analysis, the next step involves applying the multivariate statistics. In Data Analysis II, the multivariate data analysis applied to start with exploratory factor analysis, development of the measurement model with confirmatory factor analysis. In the Data Analysis III, development of the structural models, Model I- Simple structural model and Model II- Revised Structural model and testing of the hypothesis using Structural Equation Modelling. In the last chapter, Data Analysis IV, the researcher aims to examine the new framework for public confidence in police in Abu Dhabi using moderation and multi-group analysis techniques to see whether the parameter estimates explaining the relationships in the model vary across sample groups with varying geographical areas, gender impact ethnicity impact, and age impact.

Data Analysis I chapter discussing the data analysis was carried out through successive phases and presents the results leading to the testing of the hypothesis. The first phase is concerned with questionnaire response statistics and with some descriptive analysis which included the respondents’ characteristics and some central tendency measures; variability (dispersion) measures. The second phase involves the evaluation of the quality of data (screening the data prior to analysis). This phase is carried out to ensure data usability and avoid some issues such as missing data, outliers, normality, multicollinearity, linearity, and homoscedasticity. This phase also included sample size and sample bias to measure the differences between groups or variables (e.g. T-test) are discussed in more detail as follows.

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