In this blog, we will detail the complete process of data analysis, including the four basic steps – analysis and discovery, model testing, and discussion. Each step is critical and together they form the cornerstone of data analysis.
The first step is the analysis and discovery session, which accounts for 20% of the total analysis process. Various aspects such as demographic profiling, reliability testing, and validity assessment are involved here. Descriptive statistics and correlation analyses are also required. Among them, demographic profiling is a structured outline of the survey respondents; reliability testing focuses on the stability and repeatability of the data; while validity assessment covers exploratory factor analysis and validation factor analysis to ensure the validity and accuracy of the questionnaire.
The second major block is model testing, which also accounts for 20 per cent of the total score. The main work includes the examination of mediated moderated effects (mediation and moderation effects), the use of Bootstrap method and the implementation of technical tools such as skewness analysis. The process involves the use of a variety of methods to accurately interpret the results and explore potential management insights.
Finally, there is the discussion section, which is also broker20%. This stage involves an in-depth interpretation and validation of the results based on existing research findings, clarifying whether the results can effectively answer the initial questions posed, examining whether the theoretical framework chosen can support the entire theoretical system, and reviewing the limitations that the research itself may imply. Through the above critical evaluation, the possibility and practical value of the research results in theory and practice will be determined.
We hope that the above will help you to improve your understanding and application of data analysis. As for the rest of the content, please look forward to the subsequent notes on the topic.
