Collection and Presentation of Data.pptx

RESEARCH

1. Collection and Presentation of Data By: Genelyn R. Baluyos , EdD

2. There is no formula for selecting the best method to be used in gathering data. It depends on the researchers’ design of the study, the type of data, the time available to complete the study, and the financial capacity. Collection of Data

3. 1. Interview method Direct – The researcher personally interviews the respondent. It needs well-trained interviewers to personally interview the respondents. It is appropriate if the needed information is minimal, say 30. Indirect Method - uses the telephone to interview respondents. Methods of Collecting Data

4. 2. Questionnaire Method A questionnaire is a list of well planned questions written on paper which can be either personally administered or mailed by the researcher to the respondents.

5. 3. Empirical Observation Method - used commonly in psychological and anthropological studies. - can be done though participant observation, non-participant observation, or controlled observation

6. 4. Test Method -uses standard test. It is commonly used in psychological research and psychiatry. Ex. IQ tests, aptitude tests, Achievement tests 5. Registration Method- the examples ofdata gathered using this method are those obtained from the NSO, LTO, DepEd , CHED, SEC, etc. - license of firearms, birth cert, marriage contract, motor registrations.

7. 6. Mechanical Devices - uses camera, projector, video tape and tape recorder, x-ray machine, microscope, ultrasound, ct scan, telescopes, radar,etc .

8. Testing the Validity and Reliability of Research Inmstrument Objectives: Describe valid and reliable research instruments Test the validity and reliability of research instruments

9. What is validity? Validity means the degree to which the research instrument measures what it purpose to measure. Validity is truthfulness or veracity of information.

10. For instance, the test item in English (Mythology) is “Who is the goddess of beauty?” of the 120 students in English (Mythology), 120 or 120 percent answered that the “goddess of beauty is Venus.” Their answer is correct and both valid and reliable. Valid in the sense that their answer is correct or true. Reliable because their answer is consistent.

11. A valid test is always valid, but a reliable test is not always valid. For example, the test item in Mathematics is “How many meters are there in one kilometer?” Of the 100 students in Mathematics, 10 or 10 percent said that there are 1 000 meters in one kilometer. The answer is correct and valid.

12. But 90 or 90 percent answered that “there are 950 meter in one kilometer.” Their answer is incorrect but reliable because it is consistent. Thus, a reliable test is not always valid.

13. Testing the Validity of the Research Instrument Before testing the validity of the research instrument, the researcher must choose experts in the field to validate the questionnaire (research instrument).

14. In validating the questionnaire, the options retain (3), revise (2), and reject (1) are at the end of each item. Then weighted mean (x) is computed to determine if the item is to be retained, revised, or deleted.

15. Reliability Reliability means consistency of information. The information can be both reliable and valid if it is consistent and true. But sometimes an information is incorrect although it is consistent. Hence, it is reliable but not valid due to incorrect information, thus a valid information is always valid, but a reliable information may not always be valid.

16. For instance, the information regarding the performance of Mr. Z as Statistics professor. His actual performance is OUTSTANDING, but his friends said that his performance is Very Satisfactory. The information is reliable because it is consistent, but not valid because the truth is OUTSTANDING. Hence, a reliable information may not be always valid.

17. Most Common Method of Testing the Reliability of a Good Research Instrument Internal consistency- use minitab or SPSS software

18. Probability Sample A probability sample is a sample selected such that each item or person in the population being studied has a known likelihood of being included in the sample.

19. A population (universe) is the collection of things under consideration A sample is a portion of the population selected for analysis

20. A parameter is a summary measure computed to describe a characteristic of the population. Ex. The population mean IQ of the students in a certain university is 105. A statistic is a summary measure computed to describe a characteristic of the sample Ex. The sample mean IQ of 35 students in a certain university is 105.

21. Population and Sample Basic Business Statistics, 8e © 2002 Prentice-Hall, Inc. Chap 1- 22 Population Sample Use parameters to summarize features Use statistics to summarize features Inference on the population from the sample

22. What is the difference between population and sample? Population is defined as groups of people, animals, places, things, ideas to which any conclusions based on the characteristics of a sample will be applied. Sample is a subgroup of the population.

23. Parameter and Statistics Parameter is a numerical measure that describes a characteristics of a population. Example: The population mean of the electricity bill of the residents of a certain city is Php 1500.00.

24. Sample is a numerical measure that is used to describe a characteristic of a sample. Example: The sample mean of the electricity bill of 20 residents of a certain city is Php 1 450.00

25. Having found the instrument valid and reliable, the researcher now is ready to float the questionnaire to the respondents. Before the collection of data, it is necessary to determine the sample size if the population is very large. Sampling Techniques

26. Reasons for Drawing a Sample Less time consuming than a census Less costly to administer than a census Less cumbersome/bulky and more practical to administer than a census of the targeted population

27. To compute the sample size, the Slovin’s formula is used. n= N/ 1+N(e) (e) Where n=sample size N=number of cases e= margin of error

28. Example: N=2500 n = 2500 1+2500(0.05) (0.05) = 344.8 = 345

29. Census taking/ complete enumeration - is a vital tool if the information gathered would be used for administrative purposes and if it is of local or national concern.

30. 1. Random sampling=is the method of selecting a sample size (n) from a universe (N)such that each member of the population has an equal chance of being included in the sample and all possible combinations of size (n) have an equal chance of being selected as the sample. Sampling Techniques

31. Lottery or Fishbowl Technique Sampling with the use of Table of Random Numbers Systematic Sampling Stratified Random Sampling Random Sampling Techniques

32. Systematic Sampling-it involves selecting every nth element series representing the population. A complete listing is required in this method. k=N or population size/n or sample size

33. Simple stratified Random Sampling Ex first year -200 50 second -200 50 third year 200 50 fourth year 200 50 N=800 n=200

34. Stratified Proportional Random Sampling Ex first year 200 15% =30 second 200 25% =50 third year 200 27.5 =50 fourth year200 32.5% =65 N=800 n=200

35. Suppose a researcher wants to determine the average income of families in barangay having 3 000 families that are distributed in 5 purok . Compute for the sample size n at 5% marginof error.

36. Slovin’s formula: n= N/ 1+N(e) (e) n=3 000 / 1+ 3 000 (0.05)(0.05) = 353

37. Purok Population Percentage Sample Size 1 800 27 .27*353=95 2 400 13 46 3 500 17 60 4 600 20 71 5 7000 23 81 N 3000 1000 353

38. Judgment or Purposive Sampling Quota Sampling - researcher’s own convenience, commonly used in opinion polls Cluster sampling - used when the target population is too large Non-random Sampling

39. Incidental Sampling -samples most available Convenience Sampling -used in TV viewers and listeners

40. Compute the sample size required for each population: Hotel A 600 Hotel B 300 Hotel C 790 Exercise

41. Reference Priscila S. Altares , et.al.,(2003)Elementary Statistics:A Modern Approach, REX Bookstore, Inc., Manila Parreno and Jimenez, 2014, Basic Statistics, C&E Publishing, Inc.

42. Laurentina P. Calmorin and Ma. Lauremelch C. Piedad (2009) Stattistics with Computer, REX Bookstore, Inc.