data and information gathering
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DATA AND INFORMATION
GATHERING
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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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Population versus sample
A portion of the population selected for study is referred to as a sample.
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Figure 1.1 Population and sample.
Population
Sample
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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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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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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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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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BASIC TERMS Table 1.1 2001 Sales of Seven Ghana Companies
Company2001 Sales
(millions of dollars)
Wal-Mart StoresIBMGeneral MotorsDell ComputerProcter & GambleJC PenneyHome Depot
217,79985,866
177,26031,16839,26232,00453,553
An element or a
member
An observation or
measurement
Variable
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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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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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BASIC TERMS cont.
Definition The value of a variable for an element
is called an observation or measurement.
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BASIC TERMS cont.
Definition A data set is a collection of
observations on one or more variables.
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Classification of data (Nature)
Quantitative Variables or data Discrete Variables Continuous Variables
Qualitative/Categorical Variables or data
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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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Quantitative Variables cont.
Definition Discrete variable are variables that
can assume only certain values with no intermediate values.
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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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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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Figure 1.2 Types of variables.
Variable
Quantitative Qualitative orcategorical (e.g.,
make of a computer,hair color, gender)
Continuous(e.g., length,age, height,weight, time)
Discrete (e.g.,number of
houses, cars,accidents)
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Types of Qualitative data collection methods
In-depth interview with: individual respondent key informant General respondent
Good for exploratio
n research
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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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Qualitative interviews
Qualitative interviews can be; Informal conversational Topic focused
Semi-structured open ended questionnaire
Usually guided by a checklist
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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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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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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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Constraints on options for data collections
Available resources – funding & skills
Time Nature of research (objectives)
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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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Classification (Time span)
Cross-Section Data Time-Series Data Panel data
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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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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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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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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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Sampling Techniques
Probability Sampling This is where every item has a
calculable chance of selection e.i. random sampling
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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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Sampling Techniques
Informal/non-probability Sampling Purposive Snow balling Systematic Stratified Quota Multi-stage Cluster
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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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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
Std error of sample estimates SEn n
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SAMPLING ERRORS
Since
SE can be reduced by increasing n Suppose we want to decrease SE by ½
(50%)
SEn
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SAMPLING ERRORS
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.
1 1 2 2 2 4
Then SEn n n
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Steps in data collection
1. Define the purpose of the data.
2. Describe the data you need to achieve this purpose.
3. Check available secondary data and see how useful it is.
4. Define the population and sampling frame to give primary data.
5. Choose the best sampling method and sample size.
6. Identify an appropriate sample.
1. Design a questionnaire or other method of data collection.
2. Run a pilot study and check for problems.
3. Train interviewers, observers or experimenters.
4. Do the main data collection.
5. Do follow-up, such as contacting non-respondents.
6. Analyse and present the results.