This document provides an overview of key concepts in data collection and analysis. It defines different types of data, such as primary and secondary data, and variables like independent, dependent, and mediating variables. It also discusses important steps in data processing like editing, coding, data entry, and cleaning. The document explains why computers are useful for data processing and analysis. It then covers specific techniques for coding, entering data into spreadsheets, and cleaning data. Finally, it discusses different types of data analysis including descriptive and inferential statistics, and choosing the appropriate analytical techniques based on factors like sampling design and variable types.

1. By
Dr. E. Kanagaraj
Department of Social Work
School of Social Sciences
Mizoram University
Aizawl -796 009.

2. Data
What is Data?
What are its Types?
Primary vs Secondary
Quantitative Vs
Qualitative

3. Types of Data
 Primary Data and Secondary Data :
 Crossectional, Time Series and Pooled Data

4. Variable
What is Variable?
Continuous and Discrete
Metric and Non-metric
Independent, Dependent and Mediating

5. Data Processing
Editing
Coding : Code Book
Data Entry: Master Chart
Data Cleaning

6. Why Computer in Data Processing
and Analysis
Time
Resources
Energy
Errors
Use of Advanced Tools
Multivariate Techniques
Difficult?

7. Coding
Assigning numerals and Labeling
Structured, Pre-coded Instruments
Unstructured Questions
Coding according to research objective
Numerical coding most appropriate

8. Data Entry
Master Chart or Table
Variables in Column
Cases in Rows
Data entry in Ms Excel
Integration of Code Book and Master
Reading Excel Data in SPSS

9. Data Cleaning
Correcting Errors in Data Entry
Maximum, Minimum,
Frequency etc
Sorting and Correcting

10. Data Analysis
What is analysis
Explaining the one with the other
Association
Relationship: Linear vs Non linear
Comparison: Two or More Groups
Hypothesis Testing

11. Choice of Techniques
Sampling Design : Random Vs
Non random
Nature of Variable : Nominal,
Ordinal, Interval
Number of Variables
Number of Cases : Sample Size

12. Descriptive Statistics
Aim to describe the characteristics
Measures of Central Tendency
Measures of Dispersion

13. Inferential Statistics
Parameter – Population measure
Statistic – Sample measure
Univariate
Bivariate
Multivariate

14. Contact
Dr.E.Kanagaraj
mekanagaraj@gmail.com
Cell:9436362333

15. THANK YOU