Agec 405 lecture ii

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Agec 405 lecture ii

  1. 1. DATA AND INFORMATION GATHERING 1
  2. 2. DefinitionsA population consists of all elements –individuals, items, or objects – whosecharacteristics are being studied. Thepopulation that is being studied is alsocalled the target population. 2
  3. 3. Population versus sampleA portion of the population selected forstudy 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 ofthe population is called a census. Thetechnique of collecting information from aportion of the population is called asample survey. 5
  6. 6. Population vs sample conti…A sample that represents thecharacteristics of the population as closelyas possible is called a representativesample. 6
  7. 7. Population vs sample conti…A sample drawn in such a way that eachelement of the population has a chance ofbeing selected is called a randomsample 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 element An observationor 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.DefinitionAn element or member of a sample orpopulation is a specific subject or object (forexample, a person, firm, item, state, orcountry) about which the information iscollected. 10
  11. 11. BASIC TERMS cont.DefinitionA variable is a characteristic under studythat assumes different values for differentelements. In contrast to a variable, thevalue of a constant is fixed. 11
  12. 12. BASIC TERMS cont.DefinitionThe value of a variable for an element iscalled an observation or measurement. 12
  13. 13. BASIC TERMS cont.DefinitionA data set is a collection of observationson 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 VariablesDefinitionA variable that can be measurednumerically is called a quantitativevariable. The data collected on aquantitative variable are calledquantitative data. 15
  16. 16. Quantitative Variables cont.DefinitionDiscrete variable are variables that canassume only certain values with nointermediate values. 16
  17. 17. Quantitative Variables cont.DefinitionA variable that can assume any numericalvalue over a certain interval or intervals iscalled a continuous variable. 17
  18. 18. Qualitative or Categorical VariablesDefinitionA variable that cannot assume a numericalvalue but can be classified into two or morenonnumeric categories is called aqualitative or categorical variable. Thedata collected on such a variable are calledqualitative data. 18
  19. 19. Figure 1.2 Types of variables. Variable Quantitative Qualitative or categorical (e.g., make of a computer, hair color, gender)Discrete (e.g., Continuous number of (e.g., length, houses, cars, age, height, accidents) weight, time) 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 quantitative 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 quantitaive eg weight of potatoes) 27
  28. 28. Classification (Time span) Cross-Section Data Time-Series Data Panel data 28
  29. 29. Cross-Section DataDefinitionData collected on different elements for thesame variables for the same period of timeare called cross-section data. 29
  30. 30. Time-Series DataDefinitionData collected on the same element for thesame variables at different points in time orfor 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 Std error of sample estimates SE n n 37
  38. 38. SAMPLING ERRORS 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 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 7. Design a questionnaire or data. other method of data2. Describe the data you need collection. to achieve this purpose. 8. Run a pilot study and check3. Check available secondary for problems. data and see how useful it is. 9. Train interviewers, observers4. Define the population and or experimenters. sampling frame to give 10. Do the main data collection. primary data. 11. Do follow-up, such as5. Choose the best sampling contacting non-respondents. method and sample size. 12. Analyse and present the6. Identify an appropriate results. sample. 40

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