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### Sampling

1. SAMPLING Presented to : Ma'am Zahra Presented by : Group “C”
2. GROUP MEMBERS • Urooj Umer • Samawia Iqbal • Aswa Nasir • Saira Abid • Nimra Javed
3. CONTENTS  Introduction  Stages of sample selection  Types of sampling  Merits and Demerits of sampling  Errors in sampling  Characteristics of good sample  Characteristics of sample design  Sample criteria  Sampling criterion errors  Ethics
4. UROOJ UMER TOPIC : INTRODUCTION TO SAMPLING CMS :404570
5. INTRODUCTION DEFINITION Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population . METHODOLOGY The methodology used to sample from a larger population depends on the type of analysis being performed , but may include simple random sampl ing or systemic sampling.
6. BASIC TERMS  POPULATION Any complete group of entities that shares some common set of characteristics.  POPULATION ELEMENT An individual member of population .  TARGET POPULATION Refers to the entire group of Individuals or objects to which researchers are interested in generalizing the conclusions.  CENSUS An investigation of all the individual elements that make up a population
7. CONT…..  SAMPLE A subset of population used to study the population, means a smaller manageable version of large group of population.  SAMPLING UNIT An individual member of population.  SAMPLING FRAME List of all elements.  SAMPLE DESIGN The method we use to select our sample.  SAMPLE STATISTICS The information obtained from our respondents .
8. PURPOSE OF SAMPLING  To estimate a population parameter.  To gain an impression of an area or collection of things.  To test hypothesis.  It is used when the data is unlimited.  Time , money and energy is saved.  It is impractical to study the entire population.
9. STAGES IN SAMPLE SELECTION Determine which sampling method will be chosen. Select a sampling frame. Define the target population. Plan procedures for selecting sampling unit. Determine sample size. Select actual sampling units. Conduct field work.
10. ASWA NASIR TOPIC : TYPES OF SAMPLING CMS : 404235
11. TYPES OF SAMPLING
12. PROBABILITY SAMPLING  In probability sampling a sample from larger population is chosen using a method based on theory of probab ility.  It relies on random judgment.  It can be used to estimate the distribution of an opinion in the entire population.  All persons have chances of being selected.  Results are more likely to reflect the entire population.  Probability sampling requires a sampling frame, and w hen a sampling frame is not possible, non probability s ampling is used.
13. 1. SIMPLE RANDOM SAMPLING  Simple random sampling is a process in which selection of sampling unit from population is based on chance.  Each member of the subset has an equal probability of being chosen.  It is meant to be an unbiased representation of a group. Selection process: • Identify and define the population • Determine the desires sample size • List all the members of population • Assign all the members on the list a number • Ensure that number are chosen randomly by random n umber table, lottery method, or using the number on currency notes.
14. 2. SYSTEMATIC SAMPLING  Systematic sampling involve the method by which larger population are selected according to a random starting point but with fixed periodic interval. Selection process: • Defining the population • Decide the sample size • Listing population and assigning number to cases • Calculating sampling interval • Select the first unit randomly • Select every kth unit from there
15. 3. STRATIFIED SAMPLING  The population is divided into two or more then two groups called strata, according to some criterion, such as geographic location, age or income and subsamples are randomly selected from each strata. Selection process: • To split the population into sections. • The strata are chosen to divide a population into important categories relevant to the research interest. • Then draw the sample in proportion to its size.
16. 4. CLUSTER SAMPLING  The process of randomly selecting intact groups, not individuals, within the defined population sharing similar characteristics.  The population of each cluster must be known.  The complete list of all the individual in the country is not necessary. Selection process: • Define the population. • Determine the desired sample size. • Identify and define a logical cluster. • Estimate the average number of population members. • Randomly select the needed number of cluster
17. NONPROBABLITY SA MPLING  In nonprobability sampling, a particular member of the population being chosen is unknown.  It relies on personal judgment.  Each element of the population does not have an e qual chance of being included in sample.  The researcher cannot estimate the error.
18. 1 . CONVENIENCE SAMPLING  Convenience sampling is made up of people who are easy to reach .  There is no other criteria to the sampling method except the people to be available and willing to participate.  It can also be called as accidental or haphazard sampling. Selection process: • Use of students. • Members of social organization. • People on the street interviews.
19. 2. JUDGMENTAL SAMPLING  Judgmental sampling is a technique in which researcher select sample units to be sampled based on their knowledge and professional judgment.  It is used when a limited number of individuals possess the trait of interest.  It can be used when the researcher knows a reliable professional or authority that he thinks is capable of assembling a representative sample.  It is used when researcher feels that other sampling techniques will consume more time.
20. 3. QUOTA SAMPLING Quota sampling is a technique in which the assembled sample has the same proportion of individuals as the entire population with respect to known characteristics. Selection process: • Divide the population into subgroups. • Identify the proportion of subgroups in the population . • Researcher select subjects from various subgroups. • Ensure that the sample is representative of the entire population.
21. 4. SNOWBALL SAMPLING  Snowball sampling is a technique where existing study subjects recruit f uture subjects  Data that is gathered is useful in research.  It is often used in hidden populations such as drug users which are diffic ult for researcher to access. Selection process: • Identify the potential subjects in the population. • Only one or two subjects can be found initially. • Participants should be made aware that they don't have to provide any o ther names. • These steps are repeated until the needed sample size is found.
22. DIVISIONS QUALITATIVE SAMPLING QUANTITATIVE SAMPLING  Purposive sampling  Self selection sampling  Probability sampling  Non probability sampling
23. NIMRA JAVED TOPIC : MERITS AND DEMERITS CMS : 40456 9
24. MERITS AND DEMERITS OF SAMPLING MERITS:  Less time consuming.  No repetition of query.  Accuracy of data is high.  Scope of sampling is high.  Better rapport.  Detailed information.
25. DEMERITS  Chances of biasness.  Improper selection.  Exclusion of data.  Proper size of sample is a difficult job.  Lack of knowledge regarding the topic.  Without proper planning, results would be unreliable.
26. PROBABILITY SAMPLING: MERITS: DEMERITS: Cost effective. Involves lesser degree of judgement. Only specific samples types are collected. Easier way of sampling. Redundant and monotonous work. Quick to gather, takes less time. Easily compiled by non experience p erson.
27. 1. SIMPLE RANDOM SAMPLING: MERITS: DEMERITS: Whole population represented by the sample. Less range of variation. Conclusion in less time. Redundant and monotonous work. Less costly. Lesser degree of judgement, can be done by nontechnical person. Easier way of sampling.
28. 2. SYSTAMATIC RANDOM SAMPLING MERITS: DEMERITS: More convenient. Size of population may not be known before sampling. Easy to understand. Assumes that the population is uniform. Cost effective. Difficulty in gathering people in a crowd. Higher degree of control. Less time consuming.
29. 3. STRATIFIED RANDOM SAMPLING MERITS: DEMERITS: More precise. Requires more work. Minimizes the biasness. Hard to classify each kind of population. Greater accuracy. No over-represented or under-represented.
30. 4 . CLUSTER SAMPLING MERITS: DEMERITS: Less costly for surveys. Least representative of population. It can apprehend both population and clust er. Statistically less efficient. Very useful when population are large and spread over a large area . Sampling error is high. Do not need specific names within populati on.
31. NON POBABILITY SAMPLING MERITS: DEMERITS: Cost effective. Excessive dependency on judgement. Works best if the exhaustive population is not defined. Needs much purpose-oriented pollsters. Best method of sampling if sound knowled ge on the subject. Focuses on simplicity over effectiveness.
32. 1. CONVENIENCE SAMPLING MERITS: DEMERITS: Easy method. Fails to represent whole population. Less time consuming. Whole system may become useless. Economic way of sampling
33. 2. SNOWBALL SAMPLING: MERITS: DEMERITS: Referral system helps find sample quickly. Potential sampling bias. Works for hesitant subject. Chance of lack of co-operation. Secretive groups can be identified easily. Peer network might not exist.
34. 3. QUOTA SAMPLING: MERITS: DEMERITS: Saves time. Non-random Extra information speeds up sampling proc ess. Ignoring important characteristics for ease. High accuracy.
35. 4. JUDGEMENTAL SAMPLING: MERITS: DEMERITS: Less costly, More accessible, More convenient. No guarantee that chosen sample are true. Select only those individual whom are relev ant to research purpose. Potential for inaccuracy in researcher’s crit eria and resulting in sample selection.
36. SAMAWIA IQBAL TOPIC : SAMPLE SIZE AND ERRORS IN SAMPLING CMS : 404641
37. SAMPLE SIZE: DEFINITION: Sample size measures the number of individual sample and observations used in a survey or experiment. CHOSING SAMPLE SIZE: Experience. Target variance. Statistical test. Confidence level.
38. DETERMINING OF SAMPLE SIZE: It is the mathematical determination of the number of subjects that are to be included in the study. When a sample is taken from a population the findings are gen eralized to the whole population. Optimum sample size determination is required for the following: 1. To allow appropriate analysis. 2. To provide the desired level of accuracy. 3. To allow validity of significance test.
39. FACTORS FOR DETERMINING SAMPLE SIZE: 1. Number of groups and subgroups within a sample. 2. Value of information in the study. 3. Accuracy level required in studies. 4. Cost of sample. 5. Variability of the population. 6. Nature of data and data size. 7. Kind and number of comparisons. 8. Homogeneity of samples.
40. CONT….. ADVANTAGES OF HAVING A LARGE SAMPLE SIZE: DISADVANTAGES OF HAVI NG SMALL SAMPLE SIZE: Low Sampling Error. Variability. Precision. Undercoverage Bias. Confidence Intervals. Voluntary Response Bias. Margin of error decreases. Less accuracy.
41. ERRORS IN SAMPLING: 1. Sampling error. 2. Non-sampling error. 3. Processing error. 4. Response error. 5. Open and closed end question error. 6. Lying in sampling error. 7. Non-responsive error. 8. Dropout. 9. Result error.
42. SAIRA ABID TOPIC : CHARACTERISTICS AND CRITERIA OF SAMPLING CMS: 4o4730
43. CHARACTERISTICS OF A SAMPLE: A GOOD SAMPLE CONTAINS: Gender Age Income Education level Geographical location Sample size
44. CHARACTERISTICS OF SAMPLE DESIGN:  Sample design must result in a truly representative sample.  Sample design must be such which results in a small sample error.  Sample design must be viable in the context of funds available for the research study.  Sample design must be such so that systematic bias can be controlled in a better way.  Sample should be such that the results of sample study can be applied in general for the universe with a reasonable level of confidence.  Representative.  Appropriate size.  Unbiased.  Random.
45. SAMPLING CRITERIA : DEFINITION: A complete set of elements that possess some common characteristics defined by the researcher. Merits of criterion sampling:  It is useful for identifying and understanding cases that are information rich .  It can provide important qualitative component to qualitative data
46. TYPES OF CRITERIA
47. SAMPLING CRITERIA ERRORS: The errors in sampling criteria are as follows:  Inappropriate sampling frame.  Defective measuring device.  Non-respondents.  Indeterminancy principle.  Natural bias in reporting the data.
48. ETHICS:  Researcher’s ethical responsibility to safeguard the story teller by maintai ning the understood purpose of research.  The relationship should be based on trust between the researcher and pa rticipants  Inform participants of the purpose of the study.  Research participants should not be subjected to harm in any way.  Respect for the dignity of research participants should be prioritized.  Full consent should be obtained from the participants prior to the study.  Protection of the privacy of the research participant has to be ensured.
49. CONT…. • Adequate level of confidentiality of the research data should be ensured. • Anonymity of the individual and organizations participating in the research has to be ensured. • Any deception or exaggeration about aims and objective of the research much be avoided. • Communication in regards of research should be done with honesty. • Avoid using any biased data, false information and findings.
50. REFRENCES  https://www.slideshare.net/SituoLiu/introduction-to-sampling- 27708118  https://www.slideshare.net/jeweliiuc/shttps://www.questionpro .com/blog/types-of-sampling-for-social-research/ampling-136 38951  https://www.slideshare.net/RAFIULLAH13/sampling-its-types  conomicsdiscussion.net/statistics/merits-and-demerits-of-sam pling-method-of-data-collection/2343  https://en.wikipedia.org/wiki/Sampling_error  https://www.google.com/search?q=criteria+of+sampling&sour ce=lnms&tbm=isch&sa=X&ved=0ahUKEwjrmbvZyaPkAhXwz IUKHXRRDNMQ_AUIESgB&biw=1366&bih=657#imgrc
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