Note 3 Sampling,Frequency Distribution & Data Presentation Dr.Nora 070313
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Note 3 Sampling,Frequency Distribution & Data Presentation Dr.Nora 070313 Presentation Transcript

  • 1. APPLIED QUANTITATIVE METHODS (RPG 131) Semester 2, Academic Session 2012/2013Topic: Sampling, Frequency Distribution & Data Presentation Dr.Norazmawati Md.Sani @ Abd.Rahim (Dr.Nora) E08/102B 04-6533161 7 March 2013 1
  • 2. Content We lead1. Sampling2. Sampling Design3. How To Use Data4. What Is Statistics?5. Why Study Statistics?6. Why We Need Statistical Test?7. Types of Variables8. Basic Quantitative Research Approach9. Conclusions 2
  • 3. Sampling PopulationSample Sampling Population Frame Subject Element 3
  • 4. Population We lead Def; Refers to the entire group of people, events/things of interest that the researcher want to investigate.  Example: If the researcher is interested in investigating the student’s performance in USM, then all the USM students will form the population.4
  • 5. Population Frame We lead  Def; Is a listing of all the elements inthe population from which the sample is to be drawn.  Example: Population refers to all USM students. 1. Ali Bin Ahmad 2. Adam Bin Mansor 3. Anisah Bt Alias 4. Atiqah Bt Mustafa
  • 6. Element We lead  Def; An element is a single member of the population. Example: Each USM students is an element. 3rd Year 2nd Year 1st Year6
  • 7. Subject We lead Def; Is a single member of the sample. Example: 100 members from the total population formed the sample for the study; each USM students in the sample is a subject. 3rd Year 2nd Year 100 1st Year 7
  • 8. Sample We lead Def; Is a subset/subgroup of the population. It comprises some members selected from the population. SOME but not ALL elements of the population would form a sample. Example: 1st year students in USM. 3rd Year 2nd Year 1st Year 8
  • 9. Sampling We lead  Def; Is the process of selecting a sufficient number of elements from the population.  Example;  100  500  10009
  • 10. Why Sampling? We lead  Practically impossible to collect data/to test/to examine every10 element.
  • 11. Why Sampling? We lead  Limitation in time, cost & other human resources.11
  • 12. We lead Sampling Design Probability Non Probability Sampling Sampling12
  • 13. We lead13
  • 14. Unrestricted/Simple Random Sampling We lead The elements in the population have some known chance/probability of being selected as sample subject. Example; there are 1000 elements in the population & we need a sample of the probability of any one of them being chosen as a subject is 0.1 14
  • 15. Restricted/Complex Probability Sampling We lead  Offer a viable & sometimes more efficient alternative to the cumbersome of unrestricted sampling. 15
  • 16. We lead16
  • 17. Systematic Sampling We lead• Involves drawing every ‘n’th element in the population starting with a randomly chosen element.17
  • 18. Systematic Sampling We lead• Example; We want a sample of 35 households from a total population of 260 houses in a particular locality, then we could sample every seventh house starting from a random number from 1 to 7. Let us say that the random number is 7, then houses numbered 7,14, 21, 28, 35 & so on. 7 14 21 28 3518
  • 19. Stratified Random Sampling We lead • Involves a process of stratification/segregation. Use when there may be identifiable subgroups of elements within the population that may be expected to have different parameters on a variable of interest to the researchers.19
  • 20. Stratified Random Sampling We lead • Example; Managing Director interested in assessing the motivational level of their employees. This will be different for different group of people such as managerial level, supervisory level & clerical level. The result can be used for Managing Director to focus on certain group that has low20 motivation.
  • 21. Stratified Random Sampling We lead• In the stratified random sampling there is homogeneity within group & heterogeneity across groups. M S C21
  • 22. Cluster Sampling We lead Offer more heterogeneity within groups & more homogeneity among groups. M S C22
  • 23. Cluster Sampling We lead  A good example of different cluster are inputs offered by various department ofcompany president to enable him to make a decision on product development,budget allocations & marketing strategies. Product Budget Marketing Development Allocations Strategies 23
  • 24. Area Sampling We lead • Is a form of cluster sampling within an area. Use when the research pertains to populations withinidentifiable geographical areas such as countries, city blocks/particular boundaries within a locality. • Example; sampling the needs of consumersbefore opening a 24 hour convenience store in a particular part of town.24
  • 25. Double Sampling We lead• When a sample is used in a study to collect somepreliminary information of interest, & later asubsample of this primary sample is used to examinethe matter in more detail.• Example; a structured interview might indicatethat a subgroup of the respondents has more insightinto the problems of the organization. Theserespondents might be interviewed again withadditional questions. 25
  • 26. Non Probability Sampling We lead The elements do not have a known/predetermined chance of being selected as subject.26
  • 27. Non probability Sampling We lead • It means that the findings from the study of the sample cannot be confidently generalized to the27 population.
  • 28. Non probability Sampling We lead • However researchers more concern about obtaining some preliminary information in a quick & inexpensive way.28
  • 29. Non Probability We lead SamplingConvenience sampling Purposive sampling Judgment sampling Quota sampling 29
  • 30. Convenience Sampling We lead• Involves collecting information from membersof the population who are convenientlyavailable to provide this information.• Example; ‘Pepsi Challenge’ contest with thepurpose of determining whether people preferone product over another, might be set up at ashopping mall visited by many shoppers.30
  • 31. Purposive Sampling We lead• Sometimes it necessary to obtain informationfrom specific targets, who will be able to providethe desired information/because they fulfill thecriteria set up by researcher. 2nd Year  Have 2 major type such as;  Judgment sampling  Quota sampling. 31
  • 32. Judgment Sampling We lead• Involves the choice of subjects who are in the bestposition to provide the information required.• Example; if a researcher wants to find out what ittakes for women managers to make it to the top, theonly people can give first hand information are thewomen managers who are the presidents, vice-presidents & important top level executives in workorganization. This is because they have a knowledge &perhaps be able to provide good data/information tothe researcher. 32
  • 33. Quota Sampling We lead• Is a form of proportionate stratified sampling in which a predetermined proportion of people are sampled from different groups, but on a convenience basis.• Example; The work attitude of Blue-collar Workers (BCW) in an organization are quite different from those of White-collar Workers (WCW). If there are 60% of BCW & 40% of WCW, & if total 30 people are to be interviewed, then a quota of 18 BCW & 12 WCW will be form of sample. 33
  • 34. How To Use Data We lead Data collection must relate to the research questions.  Eg. Student’s profile, absenteeism. 34
  • 35. What Is Statistics? We lead The science of collecting, organizing,presenting, analyzing, & interpreting data to assist in making more effective decisions. 1 2 3 4 5 35
  • 36. Why Study Statistics? We lead Numerical information is anywhere (in the newspapers, magazines reported the numerical information). 36
  • 37. Why Study Statistics? We lead  Statistical techniques are used to make decisions that affect our daily lives. Example: Medical researcher study the cure rates for certain diseases, based on the use of different drugs & different treatments. 37
  • 38. Why Study Statistics? We lead The knowledge of statistical methods will help you understand why decisions are made & give you a better understanding of how they affect you. This is related with conducting research. 38
  • 39. Why We Need Statistical Test? We lead Because in research, we seek scientific facts & answers to the research questions we have, by analyzing the data. 39
  • 40. Why We Need Statistical Test? We lead A variables must have different values for example gender, age, ethnic group etc. 40
  • 41. Why We Need Statistical Test? We lead Organizing the data:  analyzing them,  & making sense of the results,  we can answer our research questions. 41
  • 42. We leadData refers to the available raw information gathered. 42
  • 43. Types of Variables We lead 43
  • 44. Qualitative We leadDef; when the variable being studied is nonnumeric. – Example: State of birth, eye color. 44
  • 45. Quantitative We leadDef; When the variable studied can be reported numerically. 45
  • 46. Types of Quantitative Variables We lead Quantitative variablesDiscrete variables Continuous variables Has certain values. Can assume any value Eg: No. of children, within specific range. no. of employees. Eg: distance from KL to Penang. 46
  • 47. Basic Quantitative Research Approach We lead Basic Comparative Approach Basic Basic Descriptive Associational Approach Approach 47
  • 48. Basic Comparative Approach We lead• Comparison between groups. The IV had only 2 values/categories so a statistical comparison between the groups would be perform. – For example the study to compared 2 groups of student on exam performance scores. IV DV Men Exam Performance Scores Women 48
  • 49. Basic Comparative Approach We lead The result could be the exam performancescores of the women will be significantly higher than the exam performance scores for men. IV DV Men Exam Performance Scores Women 49
  • 50. Basic Associational Approach We lead• Use where the IV is usually continuous/has several ordered categories, usually 5/>. – For example age & self confidence. Age 20-29 30-39 Self 40-49 confidence 50-59 60-69 50
  • 51. Basic Descriptive Approach We lead• Has only 1 variable at a time so that no relationships are made. 51
  • 52. Basic Descriptive Approach We lead• Most research studies include some descriptive questions to describe the sample. 52
  • 53. CONCLUSIONS We lead 6 elements of sampling. Sampling Design =Probability & Non Probability sampling. How To Use Data = Data collection must relate to the research questions. Statistics = Science of collecting, organizing, presenting, analyzing, & interpreting data to assist in making more effective decisions. 2 Types of Variables = Discrete & Continuous Variables. 3 Basic Quantitative Research Approach. 53
  • 54. References We lead• Research methods for business: A Skill Building Approach by Uma Sekaran. John wiley and Sons,Inc.1992.• Basic Statistics for business and Economics: Third Edition by Douglas A. Lind, Robert D. Mason and William G. Marchal, Mc Graw Hill, 1994 54