Upcoming SlideShare
×

# probability sampling

2,352

Published on

sampling .

Published in: Education, Technology
1 Like
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total Views
2,352
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
117
0
Likes
1
Embeds 0
No embeds

No notes for slide

### probability sampling

1. 1. Non Probability Sampling &Determining Size of Sample
2. 2. Non Probability Sampling• Non probability sampling is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as out of coverage„ / under covered), or where the probability of selection cant be accurately determined.• It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection.• Hence, because the selection of elements is non random, non probability sampling does not allow the estimation of sampling errors.
3. 3. Probability Sampling V/S Non Probability Sampling• Non probability sampling does not involve random selection and probability sampling does.• With a probabilistic sample, we know the odds or probability that we have represented the population well. With non probability samples, we may or may not represent the population well• In general, researchers prefer probabilistic or random sampling methods over non probabilistic ones, and consider them to be more accurate and rigorous.• However, in applied social research there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling.
4. 4. Methods of Non Probability Sampling Accidental, Haphazard or Convenience SamplingPurposive Sampling or Judgmental SamplingQuota SamplingSnowball Sampling
5. 5. Accidental, Haphazard or Convenience Sampling• A type of non probability sampling which involves the sample being drawn from that part of the population which is close to hand.• That is, a population is selected because it is readily available and convenient.• It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through the internet or phone.
6. 6. Accidental, Haphazard or Convenience Sampling Several important considerations for researchers using convenience samples include:• Are there controls within the research design or experiment which can serve to lessen the impact of a non-random convenience sample, thereby ensuring the results will be more representative of the population?• Is there good reason to believe that a particular convenience sample would or should respond or behave differently than a random sample from the same population?• Is the question being asked by the research one that can adequately be answered using a convenience sample?
7. 7. Purposive or Judgmental 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.• Purposive sampling can be very useful for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not the primary concern.• With a purposive sample, you are likely to get the opinions of your target population, but you are also likely to overweight subgroups in your population that are more readily accessible.
8. 8. Quota SamplingIn quota sampling, you select people non randomly according to some fixed quota. There are two types of quota sampling :• Proportional Quota: Represent major characteristics of population by proportion. E.g. 40% women and 60% men. Also have to decide the specific characteristics for the quota. E.g. gender, age, education, race, religion etc.)• Non – Proportional Quota: Specific minimum size of cases in each category. Not concerned with upper limit of quota. Smaller groups are adequately represented in sample.
9. 9. Snowballing Sampling• In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study.• You then ask them to recommend others who they may know who also meet the criteria.• This method would hardly lead to representative samples, there are times when it may be the best method available.• Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find.
10. 10. Overview of All Sampling Methods• Convenience Sampling: Use whos available.• Purposive Sampling: Selection based on purpose.• Quota Sampling: Keep going until the sample size is reached.• Proportionate Quota Sampling: Balance across groups by population proportion.• Non Proportionate Quota Sampling: Study a minimum number in each sub-group.• Snowball Sampling: Get sampled people to nominate others.
11. 11. Determining Size of Sample There are 5 steps in deciding size of sample:o Determining Goalso Determine desired Precision Of Resultso Determine confidence levelo Estimate Degree of Variabilityo Estimate the Response Rate
12. 12. Determining Goals• Know the size of the population with which you‟re dealing. If your population is small (200 people or less), it may be preferable to do a census of everyone in the population, rather than a sample.• Decide the methods and design of the sample you‟re going to draw and the specific attributes or concepts you‟re trying to measure.• Know what kind of resources you have available
13. 13. Determine desired Precision of Results• The level of precision is the closeness with which the sample predicts where the true values in the population lie.• The difference between the sample and the real population is called the sampling error.• For example, if the value in a survey says that 65% of farmers use a particular pesticide, and the sampling error is ±3%, we know that in the real-world population, between 62% and 68% are likely to use this pesticide.• This range is also commonly referred to as the margin of error.
14. 14. Determine the Confidence Level• The confidence level involves the risk you‟re willing to accept that your sample is within the average or “bell curve” of the population.• A confidence level of 90% means that, were the population sampled 100 times in the same manner, 90 of these samples would have the true population value within the range of precision and 10 would be unrepresentative samples.• Higher confidence levels require larger sample sizes• If the confidence level chosen is too low, results will be “statistically insignificant”.
15. 15. Estimate the Degree of Variability• Variability is the degree to which the attributes or concepts being measured in the questions are distributed throughout the population.• A heterogeneous population divided more or less 50%- 50% on an attribute or a concept, will be harder to measure precisely than a homogeneous population, divided say 80%-20%.• Therefore, the higher the degree of variability one expect the distribution of a concept to be in target audience, the larger the sample size must be to obtain the same level of precision.
16. 16. Estimate The Response Rate• The base sample size is the number of responses you must get back when you conduct your survey.• Since not everyone will respond, it is needed to increase sample size, and perhaps the number of contacts attempt to account for these non-responses.• To estimate response rate that one is likely to get, one should take into consideration the method of survey and the population involved.• Direct contact and multiple contacts increase response, as does a population which is interested in the issues, involved, or connected to the institution doing the surveying, or, limited or specialized in character
17. 17. Some tips “Determining sample size” Rules of thumb:* anything ≥ 30 cases* smaller population needs greater sampling intensity* type of sample Statistical rules:* level of accuracy required* a priori population parameter* type of sample
18. 18. Why Sample Size Matters?• Too large → waste time, resources and money• Too small → inaccurate results• Generalize ability of the study results• Minimum sample size needed to estimate a population parameter.
19. 19. Thank You
1. #### A particular slide catching your eye?

Clipping is a handy way to collect important slides you want to go back to later.