1. DATA TYPES

2. DATA TYPES
Why is it important to know the type of data you are going to collect in any research process?

3. DATA TYPES
Knowledge of the data to be collected is important because of the following reasons:
How will you assess its quality
Will be able to determine how its going to be collected (tools to be used for collection)
How is it going to be summarised
How it is going to be analysed (what packages or approaches are going to be employed)
How is it going to be presented/interpreted

4. DATA TYPES
The type of data is determined by the nature of the random variable which the data represents
Qualitative data
These are data which yield categorical or non-numeric responses.
These data are descriptive in nature.
The numbers representing the categories are arbitrary
Coded values can not be manipulated arithmetically

5. DATA TYPES Random Variable Data Codes and Response Categories
Area of residence
1) Rural area, 2) Urban area, 3) Farm, 4) Mine
Colour
1) Blue 2) Red 3) Green 4) Black
Sex
Male 2) Female
Level of education
No schooling 2) Primary 3) Secondary 4) Tertiary
There has been much development in some rural areas since independence
Strongly agree 2) Agree 3) Neutral 4) Disagree
5) Strongly disagree

6. QUANTITATIVE DATA Quantitative Data
These are data which yield numeric responses.
The data generated from a quantitative random variable can be meaningfully manipulated using conventional arithmetic operations (addition, subtraction, division and multiplications)

7. Quantitative Data Random Variable Response Range Data Age of Employee
18 to 65 years
eg 45years
Distance to work
1 to 50 km
eg 18km
Size of class
1 to 100
eg 60 students
Revenue
0 to 2000USD
eg 1500 USD
Number of OVC reached
0 to 3000OVC
eg 1000OVC
Number of complaints received
0 to 10
eg 3

8. Data Measurement Scales
Measurement is usually to do with physical objects( numeric--- weight, height et) or abstract (non-numeric-----personalities, feelings)
Nominal Scale(data)
Nominal scale is simply a system of assigning number symbols to events in order to label or differentiate them.
The lables are of equal weighting
No statistical operations can be meaningfully done
Is the least powerful level of measurement

9. Data Measurement Scales
Chi-square is the most common test where these types of data are used
It indicates no order or distance relationship and has no arithmetic origin.
A nominal scale simply describes differences between things by assigning them to categories. Eg
UZ Faculties: 1) Arts 2) Commerce 3) Science 4) Social Studies 5) Law etc
Orphanhood Status 1) Paternal 2) Maternal 3) Double
Tribe 1) Korekore 2) Zezuru 3) Ndau 4) Karanga

10. Data Measurement Scales
Ordinal Scale/Data
The ordinal scale places events in an order or involves ranking of responses.
Codes are or different weights or importance.
Distance between the codes can not be measured.
The real differences between adjacent ranks may not be equal.
The appropriate measure of central tendency
is the median.

11. Data Measurement Scales
Examples of Ordinal Scale
UZ Lecturers: 1) Lecturer 2) Senior Lecturer ) Associate Prof 4) Full Professor.
Degree Class: 1) Distinction 2) Upper Second ) Lower Second 4) Third
Vulnerability: 1) Highly Vulnerable 2) Moderately Vulnerable 3) Less Vulnerable
Likert Scale; from one extreme to the other extreme 1) Strongly agree 2) Agree 3) Neutral 4) Disagree 5) Strongly disagree

12. Data Measurement Scales
Interval scale/data
They are associated with quantitative data
Differences between values can be measured
It possesses both the order or ranking and distance properties
Does not possess an absolute origin (zero is arbitrary or hypothetical)
More powerful statistical measures can be
used with interval scales.

13. Data Measurement Scales
Mean is the appropriate measure of central tendency, while standard deviation is the most widely used measure of dispersion
Example
The classic example is Temperature
00C does not imply that there is no heat or cold
The ratio of the two temperatures, 30° and 60°,
means nothing because zero is an arbitrary point.

14. Data Measurement Scales
Ratio scale:
Ratio scales have an absolute or true zero of measurement.
Contains all the properties of the scales of measurement
Generally, all statistical techniques are usable with ratio scales
Multiplication and division can be used with this scale but not with other scales mentioned above.

15. Data Measurement Scales
Examples of ratio scale data
Weight
Age
Income
Time
Distance,