Sampling IN MEDICALRESEARCH IN UGANDA grp 5.pptx

SAMPLING

1. SAMPLING METHODS DEFINITION sampling is the process of using a subset of a population to represent the whole population. To help illustrate this further, let’s look at data sampling methods with examples below.

2. Sampling definitions Population:  The total number of people or things you are interested in . Sample:  A smaller number within your population that will represent the whole Sampling:  The process and method of selecting your sample

3. Sampling frame The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population). Example: Sampling frame You are doing research on working conditions at a social media marketing company. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee. Sample size The number of individuals you should include in your sample depends on various factors, including the size and  variability  of the population and your research design. There are different sample size calculator  sample and formulas depending on what you want to achieve with statistical analysis.

4. Types of sampling There are two major types of sampling methods: probability and non-probability sampling. 1.Probability sampling , also known as random sampling is a kind of sample selection where randomization is used instead of deliberate choice. Each member of the population has a known, non-zero chance of being selected. 2.Non-probability sampling  techniques are where the researcher deliberately picks items or individuals for the sample based on non-random factors such as convenience, geographic availability, or costs.

5. Probability sampling methods 1. Simple random sampling With simple random sampling  , every element in the population has an equal chance of being selected as part of the sample. It’s something like picking a name out of a hat. Simple random sampling can be done by anonymizing the population – e.g. by assigning each item or person in the population a number and then picking numbers at random. Pros:  Simple random sampling is easy to do and cheap. Designed to ensure that every member of the population has an equal chance of being selected, it reduces the risk of bias compared to non-random sampling. Cons:  It offers no control for the researcher and may lead to unrepresentative groupings being picked by chance.

6. Types cont … 2. Systematic sampling With system sampling the random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked. Best practice is to sort your list in a random way to ensure that selections won’t be accidentally clustered together. This is commonly achieved using a random number generator. If that’s not available you might order your list alphabetically by first name and then pick every fifth name to eliminate bias, for example.

7. Next, you need to decide your sampling interval – for example, if your sample will be 10% of your full list, your sampling interval is one in 10 – and pick a random start between one and 10 – for example three. This means you would start with person number three on your list and pick every tenth person. Pros:  Systematic sampling is efficient and straightforward, especially when dealing with populations that have a clear order. It ensures a uniform selection across the population. Cons:  There’s a potential risk of introducing bias if there’s an unrecognized pattern in the population that aligns with the sampling interval

8. Types cont … 3 . Stratified sampling This  involves random selection within predefined groups. It’s a useful method for researchers wanting to determine what aspects of a sample are highly correlated with what’s being measured. They can then decide how to subdivide (stratify) it in a way that makes sense for the research. For example, you want to measure the height of students at a college where 80% of students are female and 20% are male. We know that gender is highly correlated with height, and if we took a simple random sample of 200 students (out of the 2,000 who attend the college), we could by chance get 200 females and not one male. This would bias our results and we would underestimate the height of students overall. Instead, we could stratify by gender and make sure that 20% of our sample (40 students) are male and 80% (160 students) are female.

9. Pros:  Stratified sampling enhances the representation of all identified subgroups within a population, leading to more accurate results in heterogeneous populations. Cons:  This method requires accurate knowledge about the population’s stratification, and its design and execution can be more intricate than other methods.

10. Types cont 4. Cluster sampling With cluster sampling, groups rather than individual units of the target population are selected at random for the sample. These might be pre-existing groups, such as people in certain zip codes or students belonging to an academic year. Cluster sampling can be done by selecting the entire cluster, or in the case of two-stage cluster sampling, by randomly selecting the cluster itself, then selecting at random again within the cluster.

11. Pros:  Cluster sampling is economically beneficial and logistically easier when dealing with vast and geographically dispersed populations. Cons:  Due to potential similarities within clusters, this method can introduce a greater sampling error compared to other methods.

12. Non-probability sampling methods This  methodology doesn’t offer the same bias-removal benefits as probability sampling, but there are times when these types of sampling are chosen for expediency or simplicity. Here are some forms of non-probability sampling and how they work. 1. Convenience sampling People or elements in a sample are selected on the basis of their accessibility and availability. If you are doing a research survey and you work at a university, for example, a convenience sample might consist of students or co-workers who happen to be on campus with open schedules who are willing to take your questionnaire  .

13. This kind of sample can have value, especially if it’s done as an early or preliminary step, but significant bias will be introduced. Pros:  Convenience sampling is the most straightforward method, requiring minimal planning, making it quick to implement. Cons:  Due to its non-random nature, the method is highly susceptible to biases, and the results are often lacking in their application to the real world.

14. Types cont.. 2. Quota sampling Like the probability-based stratified sampling method, this approach aims to achieve a spread across the target population by specifying who should be recruited for a survey according to certain groups or criteria. For example, your quota might include a certain number of males and a certain number of females. Alternatively, you might want your samples to be at a specific income level or in certain age brackets or ethnic groups.

15. Pros:  Quota sampling ensures certain subgroups are adequately represented, making it great for when random sampling isn’t feasible but representation is necessary. Cons:  The selection within each quota is non-random and researchers’ discretion can influence the representation, which both strongly increase the risk of bias.

16. Types cont.. 3. Purposive sampling Participants for the sample are chosen consciously by researchers based on their knowledge and understanding of the research question at hand or their goals. Also known as judgment sampling, this technique is unlikely to result in a representative , but it is a quick and fairly easy way to get a range of results or responses.

17. Pros:  Purposive sampling targets specific criteria or characteristics, making it ideal for studies that require specialized participants or specific conditions. Cons:  It’s highly subjective and based on researchers’ judgment, which can introduce biases and limit the study’s real-world application

18. Types cont.. 4. Snowball or referral sampling With this approach, people recruited to be part of a sample are asked to invite those they know to take part, who are then asked to invite their friends and family and so on. The participation radiates through a community of connected individuals like a snowball rolling downhill.

19. Pros:  Especially useful for hard-to-reach or secretive populations, snowball sampling is effective for certain niche studies. Cons:  The method can introduce bias due to the reliance on participant referrals, and the choice of initial seeds can significantly influence the final sample.

20. WHAT TO CONSIDER WHILE CHOOSING ASAMPLING METHOD 1) Define your research goals If you aim to get a general sense of a larger group, simple random or stratified sampling could be your best bet. For focused insights or studying unique communities, snowball or purposive sampling might be more suitable. The nature of the group you’re studying can guide your method. For a diverse group with different categories, stratified sampling can ensure all segments are covered. If they’re widely spread geographically cluster sampling becomes useful. If they’re arranged in a certain sequence or order, systematic sampling might be effective.

21. 2 ) Consider your constraints Your available time, budget and ease of accessing participants matter. Convenience or quota sampling can be practical for quicker studies, but they come with some trade-offs. If reaching everyone in your desired group is challenging, snowball or purposive sampling can be more feasible.

22. 3 ) Determine the reach of your findings Decide if you want your findings to represent a much broader group. For a wider representation, methods that include everyone fairly (like probability sampling  are a good option. For specialized insights into specific groups, non-probability sampling methods can be more suitable.

23. 4 ) Get feedback Before fully committing, discuss your chosen method with others in your field and consider a test run.

24. Advantages of Random Sampling 1. It offers a chance to perform data analysis that has less risk of carrying an error. Random sampling allows researchers to perform an analysis of the data that is collected with a lower margin of error. This is allowed because the sampling occurs within specific boundaries that dictate the sampling process. Because the whole process is randomized, the random sample reflects the entire population and this allows the data to provide accurate insights into specific subject matters.

25. Advantages of random…. 2 . There is an equal chance of selection. Random sampling allows everyone or everything within a defined region to have an equal chance of being selected. This helps to create more accuracy within the data collected because everyone and everything has a 50/50 opportunity. It is a process that builds an inherent “fairness” into the research being conducted because no previous information about the individuals or items involved are included in the data collection process.

26. Advantages of simple random ……. 3 . It requires less knowledge to complete the research. A researcher does not need to have specific knowledge about the data being collected to be effective at their job. Researchers could ask someone who they prefer to be the next President of the United States without knowing anything about US political structures. In random sampling, a question is asked and then answered. An item is reviewed for a specific feature. If the researcher can perform that task and collect the data, then they’ve done their job.

27. Advantages of simple random ……. 4. It is the simplest form of data collection. This type of research involves basic observation and recording skills. It requires no basic skills out of the population base or the items being researched. It also removes any classification errors that may be involved if other forms of data collection were being used. Although the simplicity can cause some unintended problems when a sample is not a genuine reflection of the average population being reviewed, the data collected is generally reliable and accurate.

28. Advantages of simple random sampling… 5. Multiple types of randomness can be included to reduce researcher bias. There are two common approaches that are used for random sampling to limit any potential bias in the data. The first is a lottery method, which involves having a population group drawing to see who will be included and who will not. Researchers can also use random numbers that are assigned to specific individuals and then have a random collection of those number selected to be part of the project.

29. Advantages of simple random ……. 6. It is easier to form sample groups. Because random sampling takes a few from a large population, the ease of forming a sample group out of the larger frame is incredibly easy. This makes it possible to begin the process of data collection faster than other forms of data collection may allow.

30. Advantages of simple random ……. 7. Findings can be applied to the entire population base. Because of the processes that allow for random sampling, the data collected can produce results for the larger frame because there is such little relevance of bias within the findings. The generalized representation that is present allows for research findings to be equally generalized.

31. Disadvantages of Random Sampling 1. No additional knowledge is taken into consideration. Although random sampling removes an unconscious bias that exists, it does not remove an intentional bias from the process. Researchers can choose regions for random sampling where they believe specific results can be obtained to support their own personal bias. No additional knowledge is given consideration from the random sampling, but the additional knowledge offered by the researcher gathering the data is not always removed.

32. Disadvantages of Random Sampling cont.. 2. It is a complex and time-consuming method of research. With random sampling, every person or thing must be individually interviewed or reviewed so that the data can be properly collected. When individuals are in groups, their answers tend to be influenced by the answers of others. This means a researcher must work with every individual on a 1-on-1 basis. This requires more resources, reduces efficiencies, and takes more time than other research methods when it is done correctly.

33. Disadvantages of Random Sampling cont.. 3. Researchers are required to have experience and a high skill level. A researcher may not be required to have specific knowledge to conduct random sampling successfully, but they do need to be experienced in the process of data collection. There must be an awareness by the researcher when conducting 1-on-1 interviews that the data being offered is accurate or not. A high skill level is required of the researcher so they can separate accurate data that has been collected from inaccurate data. If that skill is not present, the accuracy of the conclusions produced by the offered data may be brought into question.

34. Disadvantages of Random Sampling cont.. 4. There is an added monetary cost to the process. Because the research must happen at the individual level, there is an added monetary cost to random sampling when compared to other data collection methods. There is an added time cost that must be included with the research process as well. The results, when collected accurately, can be highly beneficial to those who are going to use the data, but the monetary cost of the research may outweigh the actual gains that can be obtained from solutions created from the data.

35. Disadvantages of Random Sampling cont.. 5. No guarantee that the results will be universal is offered. Random sampling is designed to be a representation of a community or demographic, but there is no guarantee that the data collected is reflective of the community on average. In US politics, a random sample might collect 6 Democrats, 3 Republicans, and 1 Independents, though the actual population base might be 6 Republicans, 3 Democrats, and 1 Independent for every 10 people in the community. Asking who they want to be their President would likely have a Democratic candidate in the lead when the whole community would likely prefer the Republican .

36. Disadvantages of Random Sampling cont.. 6.It requires population grouping to be effective. If the population being surveyed is diverse in its character and content, or it is widely dispersed, then the information collected may not serve as an accurate representation of the entire population. These issues also make it difficult to contact specific groups or people to have them included in the research or to properly catalog the data so that it can serve its purpose.

37. Disadvantages of Random Sampling cont.. 7. It is easy to get the data wrong just as it is easy to get right. The application of random sampling is only effective when all potential respondents are included within the large sampling frame. Everyone or everything that is within the demographic or group being analyzed must be included for the random sampling to be accurate. If the sampling frame is exclusionary, even in a way that is unintended, then the effectiveness of the data can be called into question and the results can no longer be generalized to the larger group.

38. Systematic sampling Systematic sampling is a type of probability sampling that takes members for a larger population from a random starting point. It uses fixed, periodic intervals to create a sampling group that generates data for researchers to evaluate .

39. Advantages of Systematic Sampling 1. It is simple and convenient to use. Researchers can create, analyze, and conduct samples easily when using this method because of its structure. The algorithm to make selections is predetermined, which means the only randomized component of the work involves the selection of the first individual. Then the selection process moves across the linear or circular pattern initiated until the desired population group is ready for review.

40. Advantages of Systematic Sampling cont.. 2. There isn’t a need to number each member of a sample. Researchers can represent an entire population quickly and easily when using systematic sampling. There isn’t a need to number each member of the sample because the goal is to create representative data of the entire group without specific individualized identifiers.

41. Advantages of Systematic Sampling cont.. 3. It reduces the potential for bias in the information. Other methods of probability sampling can have a high risk of creating highly-biased clusters even when researchers take steps to avoid this issue.

42. Advantages of Systematic Sampling cont.. 4 . This method creates an even distribution of members to form samples. The factor of risk that’s involved with this sampling method is quite minimal. Even when the population under review is exceptionally diverse, this process is beneficial because of the structured distribution of members to form the sample. That means the data collected during a research project has a better chance of being an authentic representation of the entire demographic.

43. Advantages of Systematic Sampling cont.. 5 . It reduces the risk of favoritism. Researchers have no control over who gets selected for systematic sampling, which means it creates the benefits of randomized selection while providing a buffer against favoritism in the data collection efforts. It provides a low risk of data manipulation during the work collection process while keeping the sampling work highly productive on broad subjects while there’s a negligible risk of error.

44. Disadvantages of Systematic Sampling 1. This process requires a close approximation of a population. The systematic sampling method must assume that the size of the population in specific demographics is available and measurable. If that isn’t possible, then this method requires a reasonable approximation of the demographic in question. 

45. Disadvantages of Systematic Sampling cont.. 2. Some populations can detect the pattern of sampling. If a smaller population group is under review, then the systematic sampling method can get detected by some participants.

46. Disadvantages of Systematic Sampling cont.. 3 . It creates a fractional chance of selection. The systematic sampling method creates fractional chances for selection, which is not the same as an equal chance

47. Disadvantages of Systematic Sampling cont.. 5 . Systematic sampling is less random than a simple random sampling effort. If randomness is the top priority for research, then systematic sampling is not the best option to choose. Although it takes less time and isn’t as tedious as other methods of data collection, there is a predictable nature to its efforts that can influence the final results.

48. ADVANTAGES OF CLUSTER SAMPLING Cluster sampling reduces variability. All forms of sampling create estimates. What cluster sampling provides is an estimation process that is more accurate when the clusters have been put together appropriately.

49. ADVANTAGES OF CLUSTER SAMPLING cont.. Cluster sampling can be taken from multiple areas. Clusters can be defined within a single community, multiple communities, or multiple demographics. The procedures used for obtaining information follow the same process, no matter how large the sample happens to be. That means researchers can generate usable information about a neighborhood by using a random sample of certain homes.

50. ADVANTAGES OF CLUSTER SAMPLING cont.. Cluster sampling creates large data samples. It is much easier to create larger samples of data using cluster samples because of its structure. Once the clusters have been designed and placed, the information being collected is similar from each cluster.

51. Disadvantages of Cluster Sampling It is easier to create biased data within cluster sampling. The design of each cluster is the foundation of the data that will be gathered from the sampling process. Accurate clusters that represent the population being studied will generate accurate results

52. Quota ; merits Cost effective-it doesn’t require a complete list of the population or complex sampling techniques. Convenience-its easy to complement especially when there is no sampling frame or when the population is difficult to access. Representatives-quota sampling produces samples that are representatives of the population with certain x-tics e.g. age, gender or income. Flexibility-it allows flexibility in selecting participant. Suitability for small populations.

53. Demerits Since the participants are not selected randomly, the sample may not be truly representative of the population. Sampling bias Difficulty in defining quotas that accurately reflect the population especially if there limited information available about the population. Potential for interviewer bias if there is preconceived notion about the population.

54. Snowball sampling; merits Accessibility-its useful for researching hard to reach populations. Cost effective- interms of time and resources. Rapid recruitment- this is because participants recruit new members from their social networks. Diversity of participants- it can lead to a diverse sample when participants are encouraged to refer individuals from different backgrounds. Establishes trust- participants are likely to participate if referred by someone they trust hence enhancing the quality of data collected. Used for exploratory research or when little is known about the target population.

55. Demerits Limited reach- individuals who aren’t connected within the social network may not be reached hence a biased sample. Ethical concerns- such as potential coercion of participants by their peers to take part in the study. Limited control- the researcher may have limited control over the sampling process which can make it difficult to ensure quality and samples diversity. Lack of generalizability-samples may not be generalizable to the larger population due to non-random selection of participants. Bias

56. Purposive; merits Targeted selection- helps select most informative respondents, ensuring the sample represents relevant experiences. Efficiency- more efficient than random sampling In-depth understanding- one can understand groups of participants with specific x-tics. Cost effectiveness in terms of resources and time. Strategic insights- researchers can select participants to ensure a range of view points

57. Demerits Bias- researchers may unconsciously select participants who confirm their preconceived notions or hypothesis leading to skewed results. Difficulty in selection- it can be challenging to identify and select participants who truly represent the x-tics or experiences of interest. Subjectivity- researchers must rely on their judgement to choose participants which can introduce personal bias. Ethical concerns- there may be concerns if certain groups are consistently excluded or over presented in the sample, hence questions about fairness.

58. Convenience merits Ease of implementation Cost effective Accessibility- participants are readily available Demerits Sampling bias Limited generalizability Under-representation and over-representation Lack of control over selection of participants

59. Probability ; Simple random Ease of implementation Unbiased- every member has an equal chance of being selected. Statistical inference- it allows for valid statistical inferences to be made about the population. Transparency- its easy to explain and understand which enhances the transparency of the findings.

60. Demerits Not suitable for large populations- due to the time and cost involved in ensuring every element, its not practical. Underrepresentation and overrepresentation. Requires a complete list of the population which may not be available.

61. Systematic merits Ease of implementation Uniformity Regular intervals can provide a good representation of the population. Reduces sampling errors. Cost effective Useful for large populations

62. Demerits Limited randomness- if the order of the population is not truly random, it may not provide a representative sample. Difficulty in adjusting the sample size. Requires a list of the population. Vulnerability to bias.

63. Stratified merits Increased accuracy compare to simple random sampling. Representativeness- ensures that each stratum of the population is presented. Reduction of sampling errors. Comparative analysis- allows for comparative analysis between different strata. Flexibility

64. Demerits Potential for over sampling Increased costs Not suitable for small populations Potential for misclassification. Requires knowledge of population x-tics Complexity and time consuming.

65. Cluster merits Cost effective Its simple Useful for multistage sampling Its practical when the population is geographically dispersed or difficult to access.

66. Demerits Increased variability if the clusters are not homogenous. Potential for bias Complexity in analyzing data Increased cost of data collection.

67. GROUP MEMBERS ACHIA AGATHA TUMUKUGIZE JOSEPH MUKOSE YUSUF KABARANGIRA ANNAH K Y AMBAD D E KATO J O EL NANJEGO VICTORIA