This document summarizes statistical data analysis. It discusses the meaning of data analysis as inspecting, cleansing, transforming and modeling data to discover useful information for decision making. The objectives and steps of data analysis are defined as defining objectives, preparing data, descriptive analysis, confirmatory analysis, interpretation and reporting. Quantitative analysis involves measures like mean and standard deviation while qualitative analysis examines interviews and documents for common patterns. Benefits to business include informed decision making, identifying trends, cost efficiency and strategic planning. Methods of data interpretation are collecting clean data, choosing qualitative or quantitative analysis methods, observing qualitative data and using statistical measures for quantitative data.

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