1. RESEARCH METHODS Lecture 31

2. DATA PRESENTATION

3. Tables and Graphs
Tabuler and graphic presentation may take number of forms.
Purpose is to facilitate the summarization and communication of the meaning.
Bar charts, pie charts, curve diagrams, pictograms, and other forms can create strong visual impression.

4. Facilitation by Computer Technology
Commercial packages like:
SAS, Statistical Package for the Social Sciences (SPSS), SYSTAT, Epi Info, MINITAB.
User friendly packages are at the command of researcher.

5. Frequency Distribution
Several useful techniques for displaying data.
Easiest way to describe the numerical data is with a frequency distribution.
Data of 400 students with different demographic characteristics.
Let look at by gender.

6. Table 1: Frequency distribution of students by gender .
Gender Frequency Percent
Male
Female
Total

7. Graphic Presentation Common types: histograms, bar charts, pie charts
Graphic presentation lays emphasis on visual presentation over summary statistics.
Summary statistics may obscure, conceal, or even misrepresent the underlying structure of data.

8. Interpretation
Presented data have to be translated into some meaningful understanding.
Explain the meaning for drawing inferences and conclusions.
In order for interpretation, data have to be meaningfully analyzed.
Statistics help for analysis.

9. Statistics Different meanings:
Set of collected numbers (No. of people living in a city)
Branch of applied mathematics: used to manipulate and summarize the features of numbers.
Use both. Descriptive statistics often used for univariate analysis.

10. Bivariate Tables Bivariate contingency table widely used.
The table is based on cross tabulation.
Cases are organized in the table on the basis of two variables at the same time.
A contingency table is formed by cross-tabilating two variables.
Contingent because the cases in each category of a variable get distributed into each category of second variable

11. Constructing bivariate and percentaged table
Let us take two variables:
Age of the respondents.
Attitude toward women empowerment.
Age of the respondents ranged from 25 to 70 years.
The attitude index has three categories of ‘highly favorable,’ ‘Medium favorable,’ and ‘low favorable’

12. First let us make two univariate tables
The age variable has so many categories that making a bi-variate table with that much categories becomes unweildy and meaningless.
Regroup (recode) the age categories into three i.e. under 40, 40 – 60, 61+

13. Univariate table for age
Table 2: Age of the respondents .
Age (Yrs.) Frequency Percent
Under
40 –
Total

14. Univariate table for attitude
Table 3: Attitude towards women
empowerment
Attitude Frequency Percent
Hi Favorable
Med Favorable
Lo Favorable
Total

15. Bivariate table Table 4: Age by attitude towards women . empowerment .
Age (in years)
Level of under – Total
attitude F. % F % F % F %
Hi Favorable
Med. Favorable
Lo Favorable
Total