1.  DATA AND INFORMATION
GATHERING
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2. Definitions
 A population consists of all elements –
individuals, items, or objects – whose
characteristics are being studied. The
population that is being studied is also
called the target population .
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3. Population versus sample
 A portion of the population selected for
study is referred to as a sample .
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4. Figure 1.1 Population and sample.
Population
Sample
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5. Population vs sample conti…
 A survey that includes every number of
the population is called a census . The
technique of collecting information from a
portion of the population is called a
sample survey .
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6. Population vs sample conti…
 A sample that represents the
characteristics of the population as closely
as possible is called a representative
sample .
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7. Population vs sample conti…
 A sample drawn in such a way that each
element of the population has a chance of
being selected is called a random
sample
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8. Reasons for use of samples
 These are easier, faster, cheaper and
more convenient than a census.
 A good sample is almost as reliable as a
census.
 They analyse a representative from the
population.
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9. BASIC TERMS
Table 1.1 2001 Sales of Seven Ghana Companies
2001 Sales Variable
Company (millions of dollars)
Wal-Mart Stores 217,799
IBM 85,866 An observation
An element or
a member General Motors 177,260 or measurement
Dell Computer 31,168
Procter & Gamble 39,262
JC Penney 32,004
Home Depot 53,553 9

10. BASIC TERMS cont.
 Definition
 An element or member of a sample or
population is a specific subject or object (for
example, a person, firm, item, state, or
country) about which the information is
collected.
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11. BASIC TERMS cont.
 Definition
 A variable is a characteristic under study
that assumes different values for different
elements. In contrast to a variable, the
value of a constant is fixed.
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12. BASIC TERMS cont.
 Definition
 The value of a variable for an element is
called an observation or measurement .
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13. BASIC TERMS cont.
 Definition
 A data set is a collection of observations
on one or more variables.
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14. Classification of data (Nature)
 Quantitative Variables or data
 Discrete Variables
 Continuous Variables
 Qualitative/Categorical Variables or data
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15. Quantitative Variables
 Definition
 A variable that can be measured
numerically is called a quantitative
variable . The data collected on a
quantitative variable are called
quantitative data .
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16. Quantitative Variables cont.
 Definition
 Discrete variable are variables that can
assume only certain values with no
intermediate values.
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17. Quantitative Variables cont.
 Definition
 A variable that can assume any numerical
value over a certain interval or intervals is
called a continuous variable .
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18. Qualitative or Categorical
Variables
 Definition
 A variable that cannot assume a numerical
value but can be classified into two or more
nonnumeric categories is called a
qualitative or categorical variable . The
data collected on such a variable are called
qualitative data .
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19. Figure 1.2 Types of variables.
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20. Types of Qualitative data collection
methods
 In-depth interview with:
 individual respondent Good for
 key informant exploration
research
 General respondent
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21. Types of Qualitative data collection
methods
 Group interview in the form of:
 Community meeting
 Focus group discussion
 Participant Observation –
 Direct extensive observation of an
activity, behaviour or relationship
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22. Qualitative interviews
 Qualitative interviews can be;
 Informal
 conversational Usually
 Topic focused guided by a
checklist
 Semi-structured open ended
questionnaire
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23. Limitations of qualitative
interviews
 No qualitative data can be generated in a
way that can provide general estimate
 Cannot use these methods with probability
samples
 Findings are susceptible to biases which
can arise out of inaccurate judgments of
interviewers and interviewees
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24. Quantitative methods
 Most widely used method is structured
survey. Structured Survey entails
administering a written questionnaire to a
sample of respondents.
 Structured survey conducted:
 At a point in time
OR
 At regular intervals (useful for tracking change and
for collecting flow data)
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25. Advantages of Structured
Surveys
 Standardized mode of interview & construction of
questions implies biases introduced by the
enumerator’s style or respondent’s
misunderstanding is controlled / minimized
 Sample is usually drawn according to sampling
theory therefore Sample results can be used to
derive estimates for the whole population
 Quantitative data may be obtained from secondary
sources such as records, publications …..
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26. Constraints on options for
data collections
 Available resources – funding & skills
 Time
 Nature of research (objectives)
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27. Classification of data (range)
Several ways of classifying data
 Nominal Data (Difficult to quantify with
meaningful units, more qualitative)
 Ordinal Data (measurement is achieved by
ranking e.g. the use of a 1 to 5 rating scale
from ‘strongly agree’ to ‘strongly disagree’)
 Cardinal Data (Attributes can be measured ie
more quantitative eg weight of potatoes)
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28. Classification (Time span)
 Cross-Section Data
 Time-Series Data
 Panel data
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29. Cross-Section Data
 Definition
 Data collected on different elements for the
same variables for the same period of time
are called cross-section data .
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30. Time-Series Data
 Definition
 Data collected on the same element for the
same variables at different points in time or for
different periods of time are called time-
series data .
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31. Panel data
 Definition
Data collected on different elements for
the same variable at different points in
time periods are called panel data .
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32. Classification of data (Source)
 Primary data – it is new data collected
by an organisation or individual for a
specific purpose.
 Secondary data – is existing data
collected by other organisations or for
other purposes.
 We have to balance the costs and
benefits of collecting primary data.
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33. Sampling Techniques
 Probability Sampling
 This is where every item has a calculable
chance of selection
 e.i. random sampling
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34. Non-probability Sampling
 This is where someone has some choice
in who or what is selected
 This would mean that some people or
organisations had a zero chance of
selection
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35. Sampling Techniques
 Informal/non-probability Sampling
 Purposive
 Snow balling
 Systematic
 Stratified
 Quota
 Multi-stage
 Cluster
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36. SAMPLING ERRORS
Two sources of error
 Non-Sampling error due to:
 Enumeration
 Data input
 Measurement inaccuracy
 Refusal to respond
 Sampling error due to:
 Sample is part of a population and cannot
perfectly represent the population
 Different samples may produce difference results
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37. SAMPLING ERRORS
 Sampling error is unavoidable
 If Sampling is based on probability theory, the
sampling error can be calculated.
Total Error = Sampling error + Non - sampling error
SD σ
Std error of sample estimates → SE = =
n n
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38. SAMPLING ERRORS
σ
 Since SE =
n
 SE can be reduced by increasing n
 Suppose we want to decrease SE by ½ (50%)
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39. SAMPLING ERRORS
1 1 σ σ σ
Then SE = = =
2 2 n 2 n 4n
 This implies sample size should be increased 4x!
but the larger the sample, the higher the non-
sampling error.
 Therefore there is always a trade-off between
sampling error and non-sampling error.
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40. Steps in data collection
1. Define the purpose of the 1. Design a questionnaire or
data. other method of data
2. Describe the data you need collection.
to achieve this purpose. 2. Run a pilot study and check
3. Check available secondary for problems.
data and see how useful it is. 3. Train interviewers, observers
4. Define the population and or experimenters.
sampling frame to give 4. Do the main data collection.
primary data. 5. Do follow-up, such as
5. Choose the best sampling contacting non-respondents.
method and sample size. 6. Analyse and present the
6. Identify an appropriate results.
sample.
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