Submodule 5 - Sampling Designs.pdf
- 1. SUB-MODULE 5: SAMPLING DESIGNS Ophelia M. Mendoza, DrPH Presented by Racquel C. Barcelo, Ph.D. during the Basic Research Methods (BRM) Course Phase 1 via Zoom Application
- 2. LEARNING OBJECTIVES By the end of the session, the learner should be able to: 1. Identify examples of the applications of sampling in health research; 2. Define the meaning of basic sampling concepts; 3. Differentiate between probability and non- probability sampling designs; and 4. Describe the procedures in implementing the basic probability sampling designs
- 3. What are the applications of sampling?
- 4. APPLICATIONS OF SAMPLING IN HEALTH RESEARCH a. Sampling of individuals • Determining the health status of populations • Evaluating the effectiveness of health measures
- 5. b. Sampling of health facilities/institutions • Evaluating the performance of health facilities (ex., level of utilization; adequacy of equipment, etc.) • Assessing the status of health facilities (ex., determining the level of disaster preparedness of hospitals)
- 6. c. Sampling of communities • Determining investments of LGUs in health (ex., % of total budget allocated for health; preparation and implementation of Disaster Preparedness Plan) • Determining status of communities in relation to environmental health and climate change variables (ex., level of air pollution; amount of rainfall; temperature change, etc.)
- 7. d. Sampling of non-human populations • Water sampling to determine potability • Sampling of shellfish to determine incidence of red tide or sampling of fish sold in markets to determine incidence of use of formalin as preservative
- 8. What are the advantages of sampling?
- 9. ADVANTAGES OF SAMPLING a. It is cheaper. b. It is faster. c. Better quality of information can be collected. d. More comprehensive data may be obtained. e. It is the only possible method when the procedure is destructive.
- 12. POPULATION the entire group of individuals or items of interest in the study TARGET POPULATION the group from which representative information is desired and to which inferences will be made. Whatever conclusions will be derived from the study, will be generalized to the target population. SAMPLING POPULATION the population from which a sample will actually be taken Ideally, the target population should be the same as the sampling population. However there are certain instances when there is a gap between the two, resulting from limited resources and other field realities. When this occurs, what is important is for the investigator to determine the extent and direction of the bias (if any) created by the gap between the target and the sampling population
- 13. POPULATION TARGET POPULATION SAMPLING POPULATION the entire group of individuals or items of interest in the study the group from which representative information is desired and to which inferences will be made. Whatever conclusions will be derived from the study, will be generalized to the target population. the population from which a sample will actually be taken
- 15. an object or a person on which a measurement is actually taken units which are chosen in selecting the sample a listing or any other material like spot maps or aerial photographs which shows or accounts for the target population. It is a collection of the sampling units ELEMENTARY UNIT/ELEMENT SAMPLING UNIT SAMPLING FRAME
- 16. ELEMENT SAMPLING UNIT SAMPLING FRAME Source: https://www.pngkit.com/bigpic/u2t4t4y3q8a9r5o0/
- 17. What are the types of sampling designs?
- 18. Probability The rules and procedures for selecting the sample and estimating the parameters are explicitly and rigidly specified. The reliability of the resulting estimates can be determined. Most quantitative studies use probability sampling designs in the selection of subjects. *Each member in the target population is given equal chance to be selected. Non-Probability The probability of each member of the sampling population to be selected in the sample is difficult to determine or cannot be specified, hence the reliability of the resulting estimates of the sample results cannot be assessed. The external validity of the results becomes an issue Used in qualitative studies.
- 19. What are the components of a sampling design?
- 20. COMPONENTS OF A SAMPLING DESIGN a. WHERE?-- geographic area to be covered by the survey b. WHO?– elements (households, mothers, infants, etc.) to be studied in the survey. In cases when the subjects of the survey are not in a position to provide the information (ex., young children, sick elderly), the actual survey respondents must be indicated c. HOW MANY?– sample size; number of elements to be included in the survey and its basis. The values of important parameters considered in sample size determination must be explicitly indicated (ex., specific variable used as basis, anticipated value of the variable, confidence level; margin of error; power of the test, etc.)
- 21. COMPONENTS OF A SAMPLING DESIGN d. HOW TO SELECT?– procedures to be followed in selecting the elements to be included. If stratification variables are used, these should be mentioned with a concise justification why they were considered. If multi-stage sampling is used, the sampling units at each level of selection and the corresponding sampling frames used must be mentioned. e. WHEN?– refers to the time period for the conduct of the survey. This is an important consideration when the variable being studied has seasonality
- 22. What are NON- PROBABILITY sampling designs?
- 23. Qualitative Study Designs Purposive Sampling- selecting the individuals as samples according to the purposes of the researcher as his controls (representative of the total population)
- 24. Judgment Sampling- also known as authoritative sampling, researcher selects source of information based on knowledge or professional judgment
- 25. Convenience or Accidental Sampling The samples are taken because they are the most available
- 26. Snow-ball technique or Referral Sampling Research participants help recruit other participants for a study
- 27. What are the types of PROBABILITY sampling designs?
- 28. SIMPLE RANDOM SAMPLING Characteristics: Every element in the population has an equal chance of being included in the sample Procedures for Sample Selection a. Prepare the sampling frame b. Number all the population elements in the sampling frame chronologically from 1 to N, where N is the population size c. Determine the required sample size, n.
- 29. SIMPLE RANDOM SAMPLING Con’t. Procedures for Sample Selection d. Select n numbers at random between 1 and N, using either the lottery method or computer –generated random numbers using a software like Excel e. The population elements in the list whose numbers correspond to the n numbers randomly selected will comprise the simple random sample
- 30. STRATIFIED RANDOM SAMPLING Characteristics: This design is used when the investigator wants to: a. ensure that groups of interest or subsections of the population considered important for the study are adequately represented b. derive reasonably precise estimates for important subsections of the population
- 31. STRATIFIED RANDOM SAMPLING Procedures: 1. Identify the stratification variable. 2. Classify the population elements according to the categories of the stratification variable 3. Number the population elements chronologically from 1 to N, within each category of the stratification variable. 4. Determine the sample size needed from each stratum 5. Within each stratum, select the required number of samples by simple random sampling.
- 32. COMPARISON OF THE METHOD OF SAMPLE SELECTION BETWEEN SIMPLE AND STRATIFIED RANDOM SAMPLING Suppose we have the following: N=800 households of which: NUrban = 320 and Nrural = 480 n=200 households of which: nUrban = 80 and nRural = 120 SAMPLING DESIGN SAMPLING FRAME METHOD OF SAMPLE SELECTION Two sampling frames are needed: a. For URBAN areas: List of 320 urban households, chronologically numbered between 1 and 320 b. For RURAL areas: List of 480 rural households, chronologically numbered between 1 and 480 Urban and rural samples are selected separately, as follows: a. For urban areas, 80 numbers are selected at random between 1 and 320 b. For rural areas, 120 numbers are selected at random between 1 and 480 Stratified random sampling Simple random sampling List of 800 households, numbered chronologically from 1 to 800 Select 200 numbers at random, between 1 and 800
- 33. ALLOCATION OF SAMPLES TO THE DIFFERENT STRATA BY PROPORTIONAL ALLOCATION Suppose we want to allocate 250 samples to 3 sample barangays included in the study. These 3 barangays have the following populations: BARANGAY POPULATION SIZE NUMBER % A 3000 15.0 B 10500 52.5 C 6500 32.5 TOTAL 20000 100.0
- 34. ALLOCATION OF SAMPLES TO THE DIFFERENT STRATA BY PROPORTIONAL ALLOCATION The 250 samples can be allocated to the 3 barangays to reflect the population distribution as follows: BARANGAY POPULATION SIZE SAMPLE SIZE NUMBER % NUMBER % A 3000 15.0 38 15.0 B 10500 52.5 131 52.5 C 6500 32.5 81 32.5 TOTAL 20000 100.0 250 100.0
- 35. SYSTEMATIC SAMPLING Characteristics a. Every element has an equal chance of being selected. b. It is often used under the following conditions: the population elements are too many to list or to number chronologically a frame is not available c. It is often used in combination with other designs
- 36. SYSTEMATIC SAMPLING Procedures: 1. Determine the required sample size, n. 2. Determine the sampling interval, k, where: k = N/n 3. Select a number at random between 1 and k. The population element in the frame corresponding to the random number selected will be the first to be included in the sample 4. Include in the sample survey every kth population element after the first random number selected
- 37. EXAMPLE OF SYSTEMATIC SAMPLING Using the same example presented earlier where N=800 and n=200 SAMPLING DESIGN SAMPLING FRAME METHOD OF SAMPLE SELECTION Systematic sampling Not needed 1. Compute for the sampling interval, k where k=N/n. Therefore k=800/200 = 4. This means that for every 4 households in the population, 1 household will be selected as sample 2. Select a random number between 1 and 4. Suppose #2 was selected. Therefore the second household in the population to be studied is included as sample. 3. Every 4th household thereafter will be included on the study. These include households number 2, 6, 10, 14, 18, 22, 26, 30, 34, 38. etc.
- 39. CLUSTER SAMPLING Characteristics: a. It is used when a frame for the individual elementary units in the population is not available. However, a frame for groups or clusters of elements is available b. The sampling unit is different from the elementary unit. *population is grouped into clusters or small units (blocks, districts, municipalities or cities)
- 40. CLUSTER SAMPLING Procedures: 1. Identify the groups or clusters of elementary units. It is best if the sizes of the clusters are not too big and do not vary much from each other. 2. Select a random sample of clusters. 3. All elements in the selected clusters will be included in the survey.
- 41. Ethnobotanical Survey BENGUET: 16 ° 33’ N latitude & 120 ° 34’ to 52’ E longitude ATOK BAKUN BOKOD BUGUIAS ITOGON KABAYAN KAPANGAN KIBUNGAN LA TRINIDAD MANKAYAN SABLAN TUBA TUBLAY
- 42. MULTI-STAGE SAMPLING Characteristics: a. It is generally used when the survey has a wide coverage and a sampling frame for the elementary units is difficult to obtain. b. Sampling is done in successive stages. c. Data collection is concentrated only on the samples selected at each stage, resulting in lower cost per unit of inquiry. d. Statistical analysis of the data is more complicated.
- 43. MULTI-STAGE SAMPLING Procedures for Sample Selection: 1. Determine the number of stages of selection to be used in the sampling design and the sampling units to be used at each stage. 2. Determine the sample size necessary for each stage of selection. 3. Prepare the sampling frame for the 1st stage of selection, and select at random a sample of primary sampling units (PSUs). 4. For each of the PSUs earlier selected, prepare the sampling frame for the 2nd stage of selection. Randomly select the corresponding number of secondary sampling units (SSUs) from each PSU included in the sample. 5. Repeat the process of frame preparation and sample selection until the last stage of sampling is reached.