Sampling Design - Research Ethics and Methods PDF

SAMPLING DESIGN Research Ethics and Methods CS 0673  LEARNING OBJECTIVES  Learn the reasons for sampling  Develop an understanding about different sampling methods  Distinguish between probability & nonprobability sampling  Discuss the relative ad...

SAMPLING DESIGN Research Ethics and Methods CS 0673  LEARNING OBJECTIVES  Learn the reasons for sampling  Develop an understanding about different sampling methods  Distinguish between probability & nonprobability sampling  Discuss the relative advantages & disadvantages of each sampling methods 2  SAMPLING A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population”  Why sample? Resources (time, money) and workload Gives results with known accuracy that can be calculated mathematically  Thesampling frame is the list from which the potential respondents are drawn Registrar’s office Class rosters 3 SAMPLING…… What is your population of interest?  To whom do you want to generalize your results?  All doctors  School children  Men  Women aged 15-45 years  Other Can you sample the entire population? 4 SAMPLING……. Three factors that influence sample representativeness  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 5 SAMPLING BREAKDOWN 6 SAMPLING……. STUDY POPULATION SAMPLE TARGET POPULATION 7  Types of Samples Probability (Random) Samples  Simple random sample  Systematic random sample  Stratified random sample  Multistage sample  Multiphase sample  Cluster sample Non-Probability Samples  Convenience sample  Purposive sample  Quota 8  Process The sampling process comprises several stages:  Defining the population of concern  Specifying a sampling frame, a set of items or events possible to measure  Specifying a sampling method for selecting items or events from the frame  Determining the sample size  Implementing the sampling plan  Sampling and data collecting  Reviewing the sampling process 9  Population definition 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. 1 0  Population definition……. Note: Sometimes the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups. For example: we might study rats in order to get a better understanding of human health, or we might study records from people born in 2008 in order to make predictions about people born in 2009. 1 1  SAMPLING FRAME  Instraightforward simple cases, it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in most cases this is not possible.  As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. It must be representative of the population 1 2  PROBABILITY SAMPLING A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined.  When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight. 1 3 PROBABILITY SAMPLING……. Probability sampling includes:  Simple Random Sampling,  Systematic Sampling,  Stratified Random Sampling,  Cluster Sampling  Multistage Sampling.  Multiphase sampling 1 4  NON-PROBABILITY SAMPLING  Is any sampling method where some elements of population have no chance of selection (these are sometimes referred to as 'out of coverage'), or where the probability of selection can't be accurately determined.  Itinvolves 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 nonrandom, nonprobability sampling not allows the estimation of sampling errors. 1 5 Example: We visit every household in a given street and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities.  Nonprobability Sampling includes:  Accidental Sampling  Quota Sampling  Purposive Sampling 1 6  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.  It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame.  A table of random number or lottery system is used to determine which units are to be selected. 1 7 SIMPLE RANDOM SAMPLING…….. Advantages  Estimates are easy to calculate.  Simple random sampling is always an EPS design, but not all EPS designs are simple random sampling. Disadvantages  If sampling frame large, this method impracticable.  Minority subgroups of interest in population may not be present in sample in sufficient numbers for study. 1 8  REPLACEMENT OF SELECTED UNITS  Sampling schemes may be without replacement ('WOR' - no element can be selected more than once in the same sample) or with replacement ('WR' - an element may appear multiple times in the one sample).  For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR 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 WOR design. 1 9 SYSTEMATIC SAMPLING  Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.  Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size).  It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list.  A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10'). 2 0 SYSTEMATIC SAMPLING…… As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is not “simple random sampling” because different subsets of the same size have different selection probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten probability of selection, but the set {4,13,24,34,...} has zero probability of selection. 2 1 SYSTEMATIC SAMPLING……  ADVANTAGES:  Sample easy to select  Suitable sampling frame can be identified easily  Sample evenly spread over entire reference population  DISADVANTAGES:  Sample may be biased if hidden periodicity in population coincides with that of selection.  Difficult to assess precision of estimate from one survey. 2 2 STRATIFIED SAMPLING  Where population embraces a number of distinct categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected.  Every unit in a stratum has same chance of being selected.  Using same sampling fraction for all strata ensures proportionate representation in the sample.  Adequate representation of minority subgroups of interest can be ensured by stratification & varying sampling fraction between strata as required. 2 3 STRATIFIED SAMPLING……  Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata.  Drawbacks of using stratified sampling:  Sampling frame of entire population has to be prepared separately for each stratum  When examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata  In some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than other methods 2 4 STRATIFIED SAMPLING……. Draw a sample from each stratum 2 5  POSTSTRATIFICATION  Stratification is sometimes introduced after the sampling phase in a process called "poststratification“.  This approach is typically implemented due to a lack of prior knowledge of an appropriate stratifying variable or when the experimenter lacks the necessary information to create a stratifying variable during the sampling phase.  Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, poststratification can be used to implement weighting, which can improve the precision of a sample's estimates. 2 6  OVERSAMPLING  Choice-based sampling is one of the stratified sampling strategies. In this, data are stratified on the target and a sample is taken from each strata so that the rare target class will be more represented in the sample. The model is then built on this biased sample.  The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample.  The results must be adjusted to correct for the oversampling. 2 7  CLUSTER SAMPLING  Clustersampling 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. 2 8 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 2 9  CLUSTER SAMPLING… Identification of clusters  List all cities, towns, villages & wards of cities with their population falling in target area under study.  Calculate cumulative population & divide by 30, this gives sampling interval.  Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster.  Random no.+ sampling interval = population of 2nd cluster.  Second cluster + sampling interval = 4th cluster.  Last or 30th cluster = 29th cluster + sampling interval 3 0 CLUSTER SAMPLING… Two types of cluster sampling methods.  One-stage sampling: All of the elements within selected clusters are included in the sample.  Two-stage sampling: A subset of elements within selected clusters are randomly selected for inclusion in the sample. 3 1  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 3 2  MULTISTAGE SAMPLING  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 are 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. 3 3 MULTI-STAGE SAMPLING…  This technique, is essentially the process of taking random samples of preceding random samples.  Not as effective as true random sampling, but probably solves more of the problems inherent to random sampling.  An effective strategy because it banks on multiple randomizations. As such, extremely useful.  Multistage sampling used frequently when a complete list of all members of the population not exists and is inappropriate.  Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large, and perhaps unnecessary, costs associated with traditional cluster sampling. 3 4 MULTI PHASE SAMPLING Part of the information collected from whole sample & part from subsample.  InTb survey MT in all cases – Phase I  X-Ray chest in MT +ve cases – Phase II  Sputum examination in X-Ray +ve cases - Phase III  Survey by such procedure is less costly, less laborious & more purposeful 3 5  MATCHED RANDOM SAMPLING A method of assigning participants to groups in which pairs of participants are first matched on some characteristic and then individually assigned randomly to groups.  Two samples in which the members are clearly paired or are matched explicitly by the researcher. For example, IQ measurements or pairs of identical twins.  Those samples in which the same attribute, or variable, is measured twice on each subject, under different circumstances. Commonly called repeated measures.  Examples include the times of a group of athletes for 1500m before and after a week of special training; the milk yields of cows before and after being fed a particular diet. 3 6  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. 3 7  CONVENIENCE SAMPLING  Sometimes known as grab or opportunity sampling or accidental or haphazard sampling.  A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand.  The researcher using such sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.  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. 3 8 CONVENIENCE SAMPLING… ◦ Use results that are easy to get 39 3 9 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 4 0  PANEL SAMPLING  Method of first selecting a group of participants through a random sampling method and then asking that group for the same information again several times over a period of time.  Therefore, each participant is given same survey or interview at two or more time points; each period of data collection called a "wave".  This sampling methodology often chosen for large scale or nation- wide studies in order to gauge changes in the population with regard to any number of variables from chronic illness to job stress to weekly food expenditures.  Panel sampling can also be used to inform researchers about within- person health changes due to age or help explain changes in continuous dependent variables such as spousal interaction. 4 1  Event sampling Event Sampling Methodology (ESM) is a new form of sampling method that allows researchers to study ongoing experiences and events that vary across and within days in its naturally-occurring environment. Because of the frequent sampling of events inherent in ESM, it enables researchers to measure the typology of activity and detect the temporal and dynamic fluctuations of work experiences. 4 2 What sampling method u recommend?  Determining proportion of undernourished five year olds in a village.  Investigating nutritional status of preschool children.  Selecting maternity records for the study of previous abortions or duration of postnatal stay.  In estimation of immunization coverage in a province, data on seven children aged 12-23 months in 30 clusters are used to determine proportion of fully immunized children in the province.  Give reasons why cluster sampling is used in this survey. 4 3 Thank You 44