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SAMPLING TECHNIQUES

SAMPLING TECHNIQUES. THE SMART STUDENT.com. LEARNING OBJECTIVES. To identify the need for sampling in research To appreciate the importance of technique of sampling in affecting the quality of research to understand the different types of sampling techniques

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SAMPLING TECHNIQUES

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  1. SAMPLING TECHNIQUES THE SMART STUDENT.com

  2. LEARNING OBJECTIVES • To identify the need for sampling in research • To appreciate the importance of technique of sampling in affecting the quality of research • to understand the different types of sampling techniques • To appreciate the possible issues in sampling

  3. Why Sample? • Why not study everyone? • Debate about Census vs. sampling

  4. Problems in Sampling? • What problems do you know about? • What issues are you aware of? • What questions do you have?

  5. Key Sampling Concepts • What we want to generalise? • The theoretical population • What population can we get access to? • The study population • How can we get access to them ? • The sampling frame • Who is in your study? • The sample

  6. Sampling Process • Units of analysis(people) list or procedure Target population Population of interest Sampling frame List /rule defining the population method Actual population to which generalizations are made List of target sample Target sample Sample (people actually studied)

  7. Sampling Frame • The list or procedure defining the POPULATION. (From which the sample will be drawn.) • Distinguish sampling frame from sample. • Examples: – Telephone book – Voter list – Random digit dialing • Essential for probability sampling, but can be defined for non=probability sampling

  8. Types of Samples PROBABILITY Simple random systematic random Stratified cluster Complex multi stage random Random cluster stratified random NON PROBABILITY Convenience purposive qouta

  9. Simple Random Sampling • Each element in the population has an equal probability of selection AND each combination of elements has an equal probability of selection • Names drawn out of a hat • Random numbers to select elements from an ordered list

  10. Stratified Random Sampling-1 • Divide population into groups that differ in important ways • Basis for grouping must be known before sampling • Select random sample • from within each group

  11. Random Sampling Procedures Sample points are proportionally represented within population subgroups. The subgroups are chosen with the needs of the study in mind. e.g., N = 8000 and n = 200 Male = 43% and Female = 57% .43 x n = .43 x 200 = 86 males + .57 x n = .57 x 200 = 114 females n = 200

  12. Stratified Random Sampling-2 • reduces error compared to simple random sampling • Tradeoff between the cost of doing the stratification and smaller sample size • Probabilities of selection may be different for different groups • comparisons

  13. Systematic Random Sampling Systematic random sampling is a method of probability sampling in which the defined target population is ordered and the sample is selected according to position using a skip interval

  14. Steps in Drawing a Systematic Random Sample 1: Obtain a list of units that contains an acceptable frame of the target population 2: Determine the number of units in the list and the desired sample size 3: Compute the skip interval 4: Determine a random start point 5: Beginning at the start point, select the units by choosing each unit that corresponds to the skip interval

  15. SYSTEMATIC Random Sampling • Sample points are spread over entire sampling frame. • Determine the sampling interval N/n • where N = population size;n= sample size • e.g., N = 8000 and n = 200  N/n = 8000/200 = 40 • 2) Determine one random number (k) in the first interval. e.g., k = 12 • 3) The sample contains the kth element in each sampling interval. • i.e., 1st interval 12th element • 2nd interval 12 + 40 = 52nd element • 3rd interval 12 + 80 = 92nd element • 4th interval  12 + 120 = 132nd element … • 200th interval  12 + 7960 = 7072nd element

  16. CLUSTER Random Sampling Sample points are all the members of a naturally occurring unit (cluster). 1) The target population is divided into natural occurring subgroups (clusters). e.g., sports clubs 2) Subgroups are randomly chosen. e.g., AM COLLEGE HOCKEY CLUB, CYCLING CLUB, PARA –GLIDING CLUB, TABLE TENNIS CLUB (WOMEN'S), VOLLEYBALL CLUB (MEN'S) 3) Sample points are all elements in chosen clusters.

  17. 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

  18. Sampling Frame is Crucial inProbability Sampling • If the sampling frame is a poor fit to the population of interest, random sampling from that frame cannot fix the problem • Elements not in the sampling frame have zero probability of selection. Generalizations can be made ONLY to the actual population defined by the sampling frame

  19. BIAS IN SAMPLING-5 sources • Any deviation from rules- self selection volunteers • Omission of hard to identify peolple missing persistant absentees • Replacement of previously selected individuals • Difficult to trace after being included in frame/uncooperative • Large scale refusal • List/sampling frame goes out of date

  20. ANY QUESTIONS ??????

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