DATA TYPES

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

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

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

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

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)

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

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

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

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.

Data Measurement Scales Examples of Ordinal Scale UZ Lecturers: 1) Lecturer 2) Senior Lecturer 3) Associate Prof 4) Full Professor. Degree Class: 1) Distinction 2) Upper Second 3) 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

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.

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.

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.

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