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   DATA AND INFORMATION         GATHERING                       1
Definitions   A population consists of all elements –    individuals, items, or objects – whose    characteristics are be...
Population versus sample   A portion of the population selected for    study is referred to as a sample .                ...
Figure 1.1 Population and sample.Population                                    Sample                                     ...
Population vs sample conti…   A survey that includes every number of    the population is called a census . The    techni...
Population vs sample conti…   A sample that represents the    characteristics of the population as closely    as possible...
Population vs sample conti…   A sample drawn in such a way that each    element of the population has a chance of    bein...
Reasons for use of samples   These are easier, faster, cheaper and    more convenient than a census.   A good sample is ...
BASIC TERMS       Table 1.1 2001 Sales of Seven Ghana Companies                                      2001 Sales           ...
BASIC TERMS cont.   Definition   An element or member of a sample or    population is a specific subject or object (for ...
BASIC TERMS cont.   Definition   A variable is a characteristic under study    that assumes different values for differe...
BASIC TERMS cont.   Definition   The value of a variable for an element is    called an observation or measurement .    ...
BASIC TERMS cont.   Definition   A data set is a collection of observations    on one or more variables.                ...
Classification of data (Nature)   Quantitative Variables or data       Discrete Variables       Continuous Variables  ...
Quantitative Variables   Definition   A variable that can be measured    numerically is called a quantitative    variabl...
Quantitative Variables cont.   Definition   Discrete variable are variables that can    assume only certain values with ...
Quantitative Variables cont.   Definition   A variable that can assume any numerical    value over a certain interval or...
Qualitative or Categorical      Variables   Definition   A variable that cannot assume a numerical    value but can be c...
Figure 1.2 Types of variables.                                 19
Types of Qualitative data collection                methods        In-depth interview with:          individual responde...
Types of Qualitative data collection                methods        Group interview in the form of:          Community me...
Qualitative interviews       Qualitative interviews can be;        Informal        conversational                 Usual...
Limitations of qualitative             interviews   No qualitative data can be generated in a    way that can provide gen...
Quantitative methods   Most widely used method is structured    survey. Structured Survey entails    administering a writ...
Advantages of Structured           Surveys   Standardized mode of interview & construction of    questions implies biases...
Constraints on options for        data collections   Available resources – funding & skills   Time   Nature of research...
Classification of data (range) Several ways of classifying data Nominal Data (Difficult to quantify with  meaningful unit...
Classification (Time span)   Cross-Section Data   Time-Series Data   Panel data                             28
Cross-Section Data   Definition   Data collected on different elements for the    same variables for the same period of ...
Time-Series Data   Definition   Data collected on the same element for the    same variables at different points in time...
Panel data   Definition    Data collected on different elements for    the same variable at different points in    time p...
Classification of data (Source)   Primary data – it is new data collected    by an organisation or individual for a    sp...
Sampling Techniques       Probability Sampling            This is where every item has a calculable             chance o...
Non-probability Sampling         This is where someone has some choice          in who or what is selected         This ...
Sampling Techniques       Informal/non-probability Sampling           Purposive           Snow balling           Syste...
SAMPLING ERRORS    Two sources of error   Non-Sampling error due to:       Enumeration       Data input       Measurem...
SAMPLING ERRORS   Sampling error is unavoidable   If Sampling is based on probability theory, the    sampling error can ...
SAMPLING ERRORS               σ   Since SE =                n   SE can be reduced by increasing n   Suppose we want to ...
SAMPLING ERRORS        1     1 σ   σ   σ    Then SE =     =   =        2     2 n 2 n   4n   This implies sample size shou...
Steps in data collection1.   Define the purpose of the        1.   Design a questionnaire or     data.                    ...
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Data and information gathering

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Data and information gathering

  1. 1.  DATA AND INFORMATION GATHERING 1
  2. 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 . 2
  3. 3. Population versus sample A portion of the population selected for study is referred to as a sample . 3
  4. 4. Figure 1.1 Population and sample.Population Sample 4
  5. 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 . 5
  6. 6. Population vs sample conti… A sample that represents the characteristics of the population as closely as possible is called a representative sample . 6
  7. 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 7
  8. 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. 8
  9. 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 observationAn 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. 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. 10
  11. 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. 11
  12. 12. BASIC TERMS cont. Definition The value of a variable for an element is called an observation or measurement . 12
  13. 13. BASIC TERMS cont. Definition A data set is a collection of observations on one or more variables. 13
  14. 14. Classification of data (Nature) Quantitative Variables or data  Discrete Variables  Continuous Variables Qualitative/Categorical Variables or data 14
  15. 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 . 15
  16. 16. Quantitative Variables cont. Definition Discrete variable are variables that can assume only certain values with no intermediate values. 16
  17. 17. Quantitative Variables cont. Definition A variable that can assume any numerical value over a certain interval or intervals is called a continuous variable . 17
  18. 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 . 18
  19. 19. Figure 1.2 Types of variables. 19
  20. 20. Types of Qualitative data collection methods In-depth interview with:  individual respondent Good for  key informant exploration research  General respondent 20
  21. 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 21
  22. 22. Qualitative interviews Qualitative interviews can be;  Informal  conversational Usually  Topic focused guided by a checklist  Semi-structured open ended questionnaire 22
  23. 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 23
  24. 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) 24
  25. 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 ….. 25
  26. 26. Constraints on options for data collections Available resources – funding & skills Time Nature of research (objectives) 26
  27. 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) 27
  28. 28. Classification (Time span) Cross-Section Data Time-Series Data Panel data 28
  29. 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 . 29
  30. 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 . 30
  31. 31. Panel data Definition Data collected on different elements for the same variable at different points in time periods are called panel data . 31
  32. 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. 32
  33. 33. Sampling Techniques  Probability Sampling  This is where every item has a calculable chance of selection  e.i. random sampling4 33
  34. 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 selection4 34
  35. 35. Sampling Techniques  Informal/non-probability Sampling  Purposive  Snow balling  Systematic  Stratified  Quota  Multi-stage  Cluster4 35
  36. 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 36
  37. 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 37
  38. 38. SAMPLING ERRORS σ Since SE = n SE can be reduced by increasing n Suppose we want to decrease SE by ½ (50%) 38
  39. 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. 39
  40. 40. Steps in data collection1. Define the purpose of the 1. Design a questionnaire or data. other method of data2. Describe the data you need collection. to achieve this purpose. 2. Run a pilot study and check3. Check available secondary for problems. data and see how useful it is. 3. Train interviewers, observers4. Define the population and or experimenters. sampling frame to give 4. Do the main data collection. primary data. 5. Do follow-up, such as5. Choose the best sampling contacting non-respondents. method and sample size. 6. Analyse and present the6. Identify an appropriate results. sample. 40

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