Statistical analysis, presentation on Data Analysis in Research.
- 1. Statistical Analysis Guided By: Arunesh Gupta Presented By: Leena Gauraha M.COM, Ist Semester Vivekanand Mahavidyalaya Data Analysis in Research
- 2. Topics Discussed: Meaning of Data analysis Objectives & Steps Types Benefits to Business Data Interpretation Methods
- 3. Meaning: • Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. • Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data are collected and analyzed to answer questions, test hypotheses or disprove theories.
- 4. Objectives & Steps: • Defining the Objectives of Analysis • Preparing the data • Descriptive Analysis • Confirmatory Analysis • Interpretation • Reporting
- 5. Types: • Qualitative data analysis: In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives. • Observations • Documents • Interviews
- 6. • Quantitative data analysis: Quantitative analysis involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings. • Mean • Standard Deviation • Frequency distribution Types:
- 7. Benefits to Business: • Informed decision-making • Anticipating needs with trends identification • Cost efficiency • Clear foresight
- 8. Data Interpretation Methods: • Collect your data and make it as clean as possible. • Choose the type of analysis to perform: qualitative or quantitative, and apply the methods respectively to each. • Qualitative analysis: observe, document and interview notice, collect and think about things. • Quantitative analysis: you lead a research with a lot of numerical data to be analyzed through various statistical methods – mean, standard deviation or frequency distribution for instance. • Take a step back: and think about your data from various perspectives, and what it means for various participants or actors of the project. • Reflect on your own thinking and reasoning: and be aware of the many pitfalls data analysis and interpretation carries. Correlation versus causation, subjective bias, false information and inaccurate data, etc