Sampling and Sample Types

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Sample Size, Types, sampling Error etc

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Sampling and Sample Types

  1. 1. Mr. Sunil Kumar Pre PhD Student Roll No. 6421420 IHTM, M.D. University Sunil Kumar
  2. 2. SAMPLING Target Population or Universe The population to which the investigator wants to generalize his results Sampling Unit: smallest unit from which sample can be selected Sampling frame The sampling frame is the list from which the potential respondents are drawn  Telephone directory  List of five star Hotel  List of student Sampling scheme Method of selecting sampling units from sampling frame Sample: all selected respondent are sample
  3. 3. SAMPLE TARGET POPULATION SAMPLE UNIT SAMPLE Sunil Kumar • A population can be defined as including all people or items with the characteristic one wishes to understand. • Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.
  4. 4. SAMPLING BREAKDOWN Sunil Kumar All university in India All university Haryana List of Haryana university Three university in haryana
  5. 5. Why Sample? Get information about large populations  Lower cost  More accuracy of results  High speed of data collection  Availability of Population elements.  Less field time  When it‟s impossible to study the whole population Sunil Kumar
  6. 6. SAMPLING…… To whom do you want to generalize your results? All Five Star Hotel All Travel Agency All Hotel Customer Women aged 15-45 years Other Sample size : Minimum size is 30 no. Sunil Kumar
  7. 7. SAMPLING…….  3 factors that influence sample representative-ness Sampling procedure Sample size Participation (response)  When might you sample the entire population? When your population is very small When you have extensive resources When you don‟t expect a very high response Sunil Kumar
  8. 8. The sample must be: 1. representative of the population; 2. appropriately sized (the larger the better); 3. unbiased; 4. random (selections occur by chance); What is Good Sample? Sunil Kumar Merits of Sampling Size of population Fund required for the study Facilities Time
  9. 9. THE RESPRESENATION BASIS  PROBABILITY SAMPLING  NON PROBABILITY SAMPLING ELEMENT SELECTION TECHNIQE  RESTRICTED SAMPLING  UN RESTRICTED SAMPLING TYPES OF SAMPLE BASED ON TWO FACTORS: Sunil Kumar
  10. 10. •Probability sample – a method of sampling that uses of random selection so that all units/ cases in the population have an equal probability of being chosen. • Non-probability sample – does not involve random selection and methods are not based on the rationale of probability theory. Types of Sampling Sampling Techniques Probability Non- ProbabilitySunil Kumar
  11. 11.  Probability (Random) Samples  Simple random sample  Systematic random sample  Stratified random sample  Cluster sample Probability Sampling Simple Random Sampling Systematic Sampling Stratified Random Sampling Proportionate Dis Proportionate Cluster Sampling One- Stage Two Stage Multi- Stage Sunil Kumar
  12. 12. Non-Probability Samples  Convenience samples (ease of access) sample is selected from elements of a population that are easily accessible  Purposive sample (Judgmental Sampling) You chose who you think should be in the study  Quota Sampling  Snowball Sampling (friend of friend….etc.) Sunil Kumar
  13. 13. Sunil Kumar
  14. 14. SIMPLE RANDOM SAMPLING • Applicable when population is small, homogeneous & readily available • All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. A table of random number or lottery system is used to determine which units are to be selected. Advantage  Easy method to use  No need of prior information of population  Equal and independent chance of selection to every element Disadvantages  If sampling frame large, this method impracticable.  Does not represent proportionate reprenationSunil Kumar
  15. 15. Simple random sampling Every subset of a specified size n from the population has an equal chance of being selected Sunil Kumar
  16. 16. Suitability • This method is suitable for small homogeneous • Randomly selecting units from a sampling frame. „Random‟ means mathematically each unit from the sampling frame has an equal probability of being included in the sample. • Stages in random sampling: Define population Develop sampling frame Assign each unit a number Randomly select the required amount of random numbers Systematically select random numbers until it meets the sample size requirements Sunil Kumar
  17. 17. REPLACEMENT OF SELECTED UNITS  Sampling schemes may be without replacement or with replacement  For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a with replacement design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water (e.g. if we eat the fish), this becomes a without replacement design. Sunil Kumar
  18. 18. • Similar to simple random sample. No table of random numbers – select directly from sampling frame. Ratio between sample size and population size Systematic Sampling Define population Develop sampling frame Decide the sample size Work out what fraction of the frame the sample size represents Select according to fraction (100 sample from 1,000 frame then 10% so every 10th unit) First unit select by random numbers then every nth unit selected (e.g. every 10th) Sunil Kumar
  19. 19. ADVANTAGES:  Sample easy to select  Suitable sampling frame can be identified easily  Sample evenly spread over entire reference population  Cost effective DISADVANTAGES:  Sample may be biased if hidden periodicity in population coincides with that of selection.  Each element does not get equal chance  Ignorance of all element between two n element Systematic Sampling Sunil Kumar
  20. 20. Systematic sampling Every member ( for example: every 20th person) is selected from a list of all population members. Sunil Kumar
  21. 21.  The population is divided into two or more groups called strata, according to some criterion, such as geographic location, grade level, age, or income, and subsamples are randomly selected from each strata. Sunil Kumar
  22. 22. Stratified random sampling can be classified in to a. Proportionate stratified sampling It involves drawing a sample from each stratum in proportion to the letter‟s share in total population b. Disproportionate stratified sampling proportionate representation is not given to strata it necessery involves giving over representation to some strata and under representation to other. Sunil Kumar
  23. 23. STRATIFIED SAMPLING…… Advantage :  Enhancement of representativeness to each sample  Higher statistical efficiency  Easy to carry out Disadvantage:  Classification error  Time consuming and expensive  Prior knowledge of composition and of distribution of population Sunil Kumar
  24. 24.  Cluster sampling is an example of 'two-stage sampling' .  First stage a sample of areas is chosen;  Second stage a sample of respondents within those areas is selected.  Population divided into clusters of homogeneous units, usually based on geographical contiguity.  Sampling units are groups rather than individuals.  A sample of such clusters is then selected.  All units from the selected clusters are studied.  The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed Sunil Kumar
  25. 25. Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1 Sunil Kumar
  26. 26. CLUSTER SAMPLING……. Advantages :  Cuts down on the cost of preparing a sampling frame. This can reduce travel and other administrative costs. Disadvantages: sampling error is higher for a simple random sample of same size. Often used to evaluate vaccination coverage in EPI Sunil Kumar
  27. 27. • Cluster sampling: selecting a sample based on specific, naturally occurring groups (clusters) within a population. - Example: randomly selecting 20 hospitals from a list of all hospitals in England. Multi-stage sampling: cluster sampling repeated at a number of levels.- Example: randomly selecting hospitals by county and then a sample of patients from each selected hospital. Complex form of cluster sampling in which two or more levels of units are embedded one in the other. First stage, random number of districts chosen in all states. Followed by random number of talukas, villages. Then third stage units will be houses. All ultimate units (houses, for instance) selected at last step are surveyed. Cluster/ multi-stage random sample Sunil Kumar
  28. 28. Difference Between Strata and Clusters  Although strata and clusters are both non- overlapping subsets of the population, they differ in several ways.  All strata are represented in the sample; but only a subset of clusters are in the sample.  With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous Sunil Kumar
  29. 29. Non Probability CONVENIENCE SAMPLING  Sometimes known as grab or opportunity sampling or accidental or haphazard sampling.  Selection of whichever individuals are easiest to reach  It is done at the “convenience” of the researcher  For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week.  This type of sampling is most useful for pilot testing.  In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Sunil Kumar
  30. 30. Advantage: A sample selected for ease of access, immediately known population group and good response rate. Disadvantage: cannot generalise findings (do not know what population group the sample is representative of) so cannot move beyond describing the sample. •Problems of reliability •Do respondents represent the target population •Results are not generalizable Convenience Sampling Sunil Kumar Use results that are easy to get
  31. 31. Judgmental sampling or Purposive sampling  - The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched  Selected based on an experienced individual‟s belief  Advantages  Based on the experienced person‟s judgment  Disadvantages  Cannot measure the respresentativeness of the sample Sunil Kumar
  32. 32. QUOTA SAMPLING  The population is first segmented into mutually exclusive sub- groups, just as in stratified sampling.  Then judgment used to select subjects or units from each segment based on a specified proportion.  For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.  It is this second step which makes the technique one of non- probability sampling.  In quota sampling the selection of the sample is non-random.  For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years Sunil Kumar
  33. 33.  Quota sampling  Based on prespecified quotas regarding demographics, attitudes, behaviors, etc  Advantages  Contains specific subgroups in the proportions desired  May reduce bias  easy to manage, quick  Disadvantages  Dependent on subjective decisions  Not possible to generalize  only reflects population in terms of the quota, possibility of bias in selection, no standard error Types of Non probability Sampling Designs Sunil Kumar
  34. 34. Sunil Kumar  Snowball Sampling  Useful when a population is hidden or difficult to gain access to. The contact with an initial group is used to make contact with others.  Respondents identify additional people to included in the study  The defined target market is small and unique  Compiling a list of sampling units is very difficult  Advantages  Identifying small, hard-to reach uniquely defined target population  Useful in qualitative research  access to difficult to reach populations (other methods may not yield any results).  Disadvantages  Bias can be present  Limited generalizability  not representative of the population and will result in a biased sample as it is self-selecting.
  35. 35. Potential Sources of Error in Research Designs Surrogate Information Error Measurement Error Population Definition Error Sampling Frame Error Data Analysis Error Respondent Selection Error Questioning Error Recording Error Cheating Error Inability Error Unwillingness Error Total Error Non-sampling Error Random Sampling Error Non-response Error Response Error Interviewer Error Respondent Error Researcher Error Sunil Kumar
  36. 36. Errors in Hospitality Research  The total error is the variation between the true mean value in the population of the variable of interest and the observed mean value obtained in the marketing research project.  Random sampling error is the variation between the true mean value for the population and the true mean value for the original sample.  Non-sampling errors can be attributed to sources other than sampling, and they may be random or nonrandom: including errors in problem definition, approach, scales, questionnaire design, interviewing methods, and data preparation and analysis. Non- sampling errors consist of non-response errors and response errors.  Non-response error arises when some of the respondents included in the sample do not respond.  Response error arises when respondents give inaccurate answers or their answers are misrecorded or misanalyzed Sunil Kumar
  37. 37. • The larger the sample size the more likely error in the sample will decrease. •But, beyond a certain point increasing sample size does not provide large reductions in sampling error. •Accuracy is a reflection of the sampling error and confidence level of the data. Sampling Error and Confidence Sunil Kumar
  38. 38. Errors in Sampling  Non-Observation Errors  Sampling error: naturally occurs  Coverage error: people sampled do not match the population of interest  Underrepresentation  Non-response: won’t or can’t participate Sunil Kumar
  39. 39. Errors of Observation  Interview error- interaction between interviewer and person being surveyed  Respondent error: respondents have difficult time answering the question  Measurement error: inaccurate responses when person doesn’t understand question or poorly worded question  Errors in data collection Sunil Kumar
  40. 40. Sunil Kumar DESINGED BY Sunil Kumar Research Scholar/ Food Production Faculty Institute of Hotel and Tourism Management, MAHARSHI DAYANAND UNIVERSITY, ROHTAK Haryana- 124001 INDIA Ph. No. 09996000499 email: skihm86@yahoo.com , balhara86@gmail.com linkedin:- in.linkedin.com/in/ihmsunilkumar facebook: www.facebook.com/ihmsunilkumar webpage: chefsunilkumar.tripod.com

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