7 dan 8

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7 dan 8

  1. 1. CHAPTER 7 SAMPLING DESIGN
  2. 2. SAMPLING
  3. 3. SAMPLING The process of selecting a sufficient number of elements from the population, so that results from analyzing the sample are generalizable to the population.
  4. 4. REASONS FOR SAMPLING
  5. 5. Less cost Less time Less errors due to less fatigue Destruction of elements avoided
  6. 6. SAMPLE SIZE DECISION There are variety sample size decision that available . The choice can be defend on the following:  Population  The element  Population frame  Sample  Sampling unit  The subject
  7. 7. SAMPLE SIZE DECISION a) Population -Refer to the entire group of people, events or things of interest that the population that the researches wishes to investigate. b) Element - Single member of the population. The census is a count of all elements in the human population. c) Population frame - the listing of all the element in the population from which the sample is drawn. It is also known as sampling frame.
  8. 8. SAMPLE SIZE DECISION d) Sample -Subset of the population. It is a subgroup of the population selected using sampling method or design. e) Sampling unit -the element or set of the elements that is available for selection in some stage of the sampling process. f) Subject -a subject is a single member of the sample.
  9. 9. The sampling process Define the population Execute the sampling process Determine the appropriate sample size Determine the sample frame Determine the sampling design
  10. 10. Sample Size Most research Sub-samples • > 30 < 500 are appropriate • Min 30 for each category Multivariate research Experimental research • At least 10 times more than the number of variables • Can be low as 10 to 20
  11. 11. Sample size Precision • How close the estimate to the true population characteristics with low margin of error Confidence • How certain the estimate will really hold true for the population. • Commonly accepted confidence level ≤0.05 (95% confidence)
  12. 12. Population Geographical Boundaries & Time Elements Defined in terms
  13. 13. Sample Frame Physical representation of all the elements in the population from which the sample is drawn Make sure that sample frame the latest and most upto-date to avoid coverage error
  14. 14. Sampling Design Target population of focus to the study The exact parameters need to be investigated Availability of sampling frame Sample size needed Costs associated to the sampling design Time frame available for data collection
  15. 15. Sampling Design Probability sampling Nonprobability sampling
  16. 16. SAMPLING TECHNIQUES Probability Sampling Simple Random Sampling Systematic Sampling Stratified Random Sampling Cluster Sampling
  17. 17. Simple Random Sampling PROCEDURE – Each element has a known and equal chance of being selected CHARACTERISTICS – Highly generalizable – Easily understood – Reliable population necessary frame
  18. 18. Systematic Sampling PROCEDURE – Each nth element, starting with random choice of an element between 1 and n CHARACTERISTICS – Easier than simple random sampling – Systematic biases when
  19. 19. Cluster Sampling PROCEDURE – Divide of population in clusters – Random selection of clusters – Include all elements from selected clusters CHARACTERISTICS – Intercluster homogeneity – Intracluster heterogeneity – Easy and cost efficient – Low correspondence with reality
  20. 20. Stratified Sampling PROCEDURE – The process of dividing members of the population into homogeneous subgroups before sampling – There are two types Cof stratified Stratum A B random sampling: Population size 100 200 300 1/2 1/2 •Sampling fraction Proportionate 1/2 Final sample size 50 100 150
  21. 21. •Disproportionate Stratum A B C Population size 100 200 300 Sampling fraction 1/2 ¾ 1/3 Final sample size 50 150 100 CHARACTERISTICS – Interstrata heterogeneity – Intrastratum homogeneity – Includes all relevant subpopulations
  22. 22. Nonprobability Sampling Convenienc e Sampling Members of the population are chosen based on their relative ease of access. Judgment Sampling The researcher chooses the sample based on who they think would be appropriate for the study. Quota Sampling A quota is established (say 65% women) and researchers are free to choose any respondent they wish as long as the quota is met.
  23. 23. 5 Common Sampling Errors o POPULATION SPECIFICATION ERROR o SAMPLE FRAME ERROR o SELECTION ERROR o NON-RESPONSE o SAMPLING ERRORS
  24. 24. Measurement of Variables Operational definition Scales
  25. 25. Measurement the assignment of numbers or other symbols to characteristics (or attributes) of objects according to a pre-specified set of rules. (Characteristics of) Objects Type of variables Object – house, countries, restaurants. One lends itself to objective and precise measurement; Examples of characteristics of objects are arousal seeking tendency, achievement motivation, organizational effectiveness The other is more nebulous and does not lend itself to accurate measurement because of its abstract and subjective nature.
  26. 26. Operationalizing Concepts Operationalizing is done by looking at the behavioural dimensions, facets, or properties denoted by the concept. Operationalizing concepts: reduction of abstract concepts to render them measurable in a tangible way. 26
  27. 27. Example 27
  28. 28. Scale Tool or mechanism by which individuals are distinguished as to how they differ from one another on the variables of interest to our study. 28
  29. 29. 4 TYPES OF SCALES
  30. 30. Nominal Scale • A nominal scale is one that allows the researcher to assign subjects to certain categories or groups. • What is your department? O Marketing O Maintenance O Finance O Production O Servicing O Personnel O Sales O Public Relations O Accounting • What is your gender? O Male O Female 30
  31. 31. Ordinal Scale Ordinal scale: not only categorizes variables in such a way as to denote differences among various categories, it also rank-orders categories in some meaningful way. What is the highest level of education you have completed? O Less than High School O High School O College Degree O Masters Degree O Doctoral Degree 31
  32. 32. Interval Scale • Interval scale: whereas the nominal scale allows us only to qualitatively distinguish groups by categorizing them into mutually exclusive and collectively exhaustive sets, and the ordinal scale to rank-order the preferences, the interval scale lets us measure the distance between any two points on the scale. 32
  33. 33. • Circle the number that represents your feelings at this particular moment best. There are no right or wrong answers. Please answer every question. 1. I invest more in my work than I get out of it I disagree completely 1 2 3 4 5 I agree completely 2. I exert myself too much considering what I get back in return I disagree completely 1 2 3 4 5 I agree completely 3. For the efforts I put into the organization, I get much in return I disagree completely 1 2 3 4 5 I agree completely 33
  34. 34. Ratio scale • Indicates not only the magnitude of the differences but also their proportion.
  35. 35. THE END!!! THANK YOU FOR YOUR CONCENTRATION!!!

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