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RESEARCH DESIGN (PART 2)

RESEARCH DESIGN (PART 2). Siti Rohaida Bt Mohamed Zainal, PhD School of Management siti_rohaida@usm.my. What is Sampling?. “Sampling is a process by which we study a small part of a population to make judgments about the entire population.”. Why Sampling is Needed?. Lower cost

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RESEARCH DESIGN (PART 2)

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  1. RESEARCH DESIGN (PART 2) Siti Rohaida Bt Mohamed Zainal, PhD School of Management siti_rohaida@usm.my

  2. What is Sampling? “Sampling is a process by which we study a small part of a population to make judgments about the entire population.”

  3. Why Sampling is Needed? • Lower cost • Greater speed of data collection • Greater accuracy

  4. Factors to Consider in Sample Design Research objectives Degree of accuracy Resources Time frame Knowledge of target population Research scope Statistical analysis needs

  5. The Nature of Sampling • Population • Population element • Sampling frame • Sample • Subject • Parameter • Statistics • Sampling error

  6. The Nature of Sampling • Population- total collection of elements about which we wish to make some inferences. • Population element - the individual participant or object on which the measurement is taken--the unit of study. • Sampling frame - the listing of population elements from which the sample will be drawn—i.e., master lists, directories etc

  7. The Nature of Sampling • Sample - apart of the population from which we actually collect information which is used to draw conclusions about the whole population • Subject - a single member of the sample • Parameter - characteristics of the population • Statistics - characteristics of the sample • Sampling error: any error in a survey that occurs because of the sample

  8. Inference Process Estimation & Hypothesis Testing Population Sample Statistics Sample

  9. Parameter and Statistics: Example • “Average income of engineers in Malaysia is RM5000” • Parameter • “Average income of engineers in Penang is RM5000” • Statistic Population Sample

  10. Define the Population Determine the Sampling Frame Select Sampling Techniques Determine the Sample Size Execute the Sampling Process The Sampling Design Process

  11. Define the Target Population Important factors in determining the sample size: • the importance of the decision • the nature of the research • the number of variables • the nature of the analysis • sample sizes used in similar studies • resource constraints-time and cost SAMPLE SIZE???

  12. Sampling Error Sampling error is any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size SAMPLE SIZE???

  13. Two Basic Sampling Methods • Probability samples: ones in which members of the population have a known chance (probability) of being selected into the sample • Non-probability samples: instances in which the chances (probability) of selecting members from the population into the sample are unknown

  14. Classification of Sampling Techniques SAMPLING TECHNIQUES Non-Probability Sampling Techniques Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Probability Sampling Techniques Simple Random Sampling Double Sampling Systematic Sampling Stratified Sampling Cluster Sampling

  15. NON-PROBABILITY SAMPLING

  16. Non-probability Samples Reasons to use: • Procedure satisfactorily meets the sampling objectives • Lower Cost • Limited Time • Total list population not available

  17. Time Non-probability Samples No need to generalize Feasibility Limited objectives Cost

  18. Non-probability Sampling Methods Convenience Based on ease of accessibility Deliberately select sample to conform to some criterion Judgmental Relevant characteristics are used to segregate the sample to improve its representativeness Quota Snowball Referred by current sample elements

  19. Convenience Sampling Convenience sampling – sample is selected base on ease of accessibility. Normally use in the early stage of exploratory study Often, respondents are selected because they happen to be in the right place at the right time. • use of students, and members of social organizations • mall intercept interviews without qualifying the respondents • “people on the street” interviews • pool of friends and contacts

  20. Judgmental Sampling • Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher or those conform to some criterion of interest. • Useful when looking for information that only a few “experts” can provide. Example: • Academic expertise • Purchase engineers selected in industrial marketing research • Expert witnesses used in court

  21. Quota Sampling Quota sampling – relevant characteristics are used to stratify the sample. • The first stage consists of developing categories of population elements. • In the second stage, sample elements are selected based on convenience or judgment. • Example: gender, religion, ethnicity, etc

  22. Quota Sampling Population Samplecompositioncomposition Characteristic Percentage Percentage NumberPostgraduate MA 60% 60% 600 PhD 40% 40% 400 ____ ____ ____ 100 100 1000

  23. Snowball Sampling In snowball sampling, an initial group of respondents is selected, usually at random. • After being interviewed, these respondents are asked to identify others who belong to the target population of interest. • Subsequent respondents are selected based on the referrals.

  24. PROBABILITY SAMPLING

  25. Simple Random Sampling All elements in the population are considered and each has equal chance to be selected. Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected. • Advantages • High generalisability of the findings • Easy to implement with random number table. • Disadvantages • Requires list of population elements • Time consuming • Uses larger sample sizes

  26. Systematic Random Sampling • The sample is chosen by selecting a random starting point and then picking every kth element from the sampling frame. • To draw a systematic sample, the steps are as follows: • Identify, list, and number the elements in the population • Identify the skip interval • Identify the random start • Draw a sample by choosing every kth element. • * kth element is the skip interval

  27. Systematic Random Sampling EXAMPLE: There are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, k, is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on. • Disadvantages • Systematic biases are possible • Advantages • Simple to design • Easier than simple random if population frame is available

  28. All Postgraduates Masters PhD Sample Stratified Sampling • Population is divided into sub-population and subjects are selected randomly. • Homogeneity within group and heterogeneity across groups.

  29. Stratified Sampling • A two-step process in which the population is partitioned into sub-population. • Elements are selected from each sub-population by a random procedure, usually simple random sampling. • The elements within each sub-populationshould be as homogeneous as possible, but the elements across sub-population should be as heterogeneous as possible. • The stratification variables should also be closely related to the characteristic of interest.

  30. Stratified Sampling Example: University students can be divided into: • Gender • Race • School/department • Class level: undergraduate and postgraduate • Off campus and on-campus

  31. Stratified Sampling • Advantages • Most efficient among all probability designs. • Increased statistical efficiency • Provides data to represent subgroups • Disadvantages • Stratification must be meaningful • Time consuming

  32. Cluster Sampling All Managers in Malaysia • Population is divided into clusters. • Heterogeneity within group and homogeneity across groups. Kuala Lumpur Johor Penang Sample

  33. Cluster Sampling Population Element Possible Clusters in Malaysia Malaysian adult population States Districts Metropolitan Statistical Area Housing Area Households

  34. Cluster Sampling • The target population is first divided into mutually exclusive clusters. • Then a random sample of clusters is selected, based on a probability sampling technique • Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible. • Ideally, each cluster should be a small-scale representation of the population.

  35. Area Sampling (example of cluster) A cluster sampling technique applied to a population with well-defined political or geographic boundaries.

  36. Double Sampling • The same sample or a subset of the sample is studied twice. • Double and multiple sampling plans were invented to give a questionable lot another chance. • For example: a structured interview might indicate that a subgroup of the respondents has more insights into a problem in the organization, then, these respondents might be approached again with additional questions.

  37. Double Sampling

  38. What Is a Valid Sample? Accurate Precise The degree to which bias is absent from the sample. The sample is drawn properly. The degree to which the sample selected closely represent the population.

  39. What Is a Valid Sample? High accuracy but low precision High precision but low accuracy

  40. RULE OF THUMB FOR SAMPLE SIZE: According to ROSCOE (1975): Sample size larger than 30 and less than 500 are appropriate for most research. Where samples are to be broken into subsamples (male/female, masters/PhD etc), a minimum sample size of 30 for each category is necessary. In multivariate research, sample size should be, preferably, 10 times (or more) as large as the number of variables in the study.

  41. LET’S RECAP...

  42. Nonprobability Sampling Methods Convenience sampling relies upon convenience and access Judgment sampling relies upon belief that participants fit characteristics Quota sampling emphasizes representation of specific characteristics Snowball sampling relies upon respondent referrals of others with like characteristics

  43. Exercise 1 A researcher wants a sample of 35 households from a total population of 260 houses in Medan, Indonesia. He samples every 7th house starting from a random number of 1 to 7. He then choose houses numbered 7, 14, 21, 28 and so on. What type of sampling technique does the researcher adopt? a)    a simple random sampling b)    a stratified random sampling c)    a cluster sampling d)    a systematic random sampling 43

  44. Exercise 2 A pharmaceutical company wants to trace the effects of a new drug on patients with specific health problems. It then contacts such individuals and with the group of voluntarily consenting patients, tests the drugs. What type of sampling is appropriate? a)     a simple random sample b)    a stratified random sample c)     a cluster sample d)    a judgmental sample 44

  45. Exercise 3 The director of human resources of a manufacturing firm wants to offer stress management seminars to the personnel who are exposed to high levels of stress. He predicts that three groups are most prone to stress; (1) those who handle dangerous chemicals, (2) counselors who listen to problems, and (3) those who handle production line. What type of sampling is most appropriate in this case? a)     a simple random sample b)    a stratified random sample c)     a cluster sample d)    a judgmental sample 45

  46. The end Questions?

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