Types of Sampling PDF

Types of sampling Probability samples: Samples in which each members of the population have a known equal none zero chance (probability) of being selected in the study. Non-probability samples: Samples in which the chances (probability) of selecting members from the population are unk...

Types of sampling Probability samples: Samples in which each members of the population have a known equal none zero chance (probability) of being selected in the study. Non-probability samples: Samples in which the chances (probability) of selecting members from the population are unknown. Types of sampling Non-probability sampling  Non-probability samples: Advantages: Disadvantages:  Simple and easy to use.  Helpful for pilot studies and for hypothesis generation.  Highly vulnerable to selection bias.  Data collection can be facilitated in short duration of time.  Generalizability is unclear.  Cost effectiveness.  High level of sampling error.  1. Convenience Sampling Also known as Volunteer Sampling (ease of access). sample is selected from elements of a population that are easily accessible. Often, respondents are selected because they happen to be in the right place at the right time.  2. Judgmental Sampling (Purposive sampling) It is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher. The researcher selects a sample based on experience or knowledge of the group to be sampled (pre-specified) -people believed to be typical, normal or average for a particular phenomenon …called “Judgment” sampling.  3.Quota Sampling Individual assignment to the sample strata is non-random, based on quotas or proportions of each stratum that must be sampled with consecutive units (the first - come basis). In quota sampling the selection of the sample is made by the interviewer, who has been given quotas to fill from specified sub- groups of the population.  4. Snowball Sampling In this sample, an initial group of respondents is selected, usually at random. After being interviewed, these respondents are asked to identify others who belong to the target population of interest.  A type of non- probability sampling technique in which studying participants are recruited by first identifying a few subjects suitable for the research, and then asking the initial subjects to refer other qualified subjects.  Also known as: Network sampling. Probability sampling  Probability samples advantages: Random sampling. Each subject has a known probability of being selected. Allows generalization of results. Probability samples are the best. Ensure: Representativeness and Precision 1. Simple Random Sampling Principle:  Each element in the population has a known and equal probability of selection. Procedure:  Defining the population.  Choosing the sample size.  Need listing of all sampling units (“sampling frame”).  Number all units.  Randomly draw units. 1. Simple Random Sampling Methods: Lottery method. Table of random numbers. Computer generated random numbers.  1. Simple Random Sampling Disadvantages: Advantages: Known and equal chance of selection. Require knowledge of the complete sampling frame. Simple process and easy to understand. Non respondents or refusals.  2. Systematic sampling Principle: The sample is chosen by selecting a random starting point and then picking every element in succession from the sampling frame. The sampling interval, is determined by dividing the population size N by the sample size n and rounding to the nearest integer.  2. Systematic sampling Procedure: 1. Select a suitable sampling frame 2. Each element is assigned a number from 1 to N (pop. size) 3. Determine the sampling interval i:i=N/n. If i is a fraction, round to the nearest integer 4. Select a random number, r, between 1 and i, as explained in simple random sampling 5. The elements with the following numbers will comprise the systematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i 2. Systematic sampling  2. Systematic sampling N = 1200, and n = 60 → sampling fraction = 1200/60 = 20 List persons from 1 to 1200 Randomly select a number between 1 and 20 ? (ex : 8)  → 1st person selected = the 8th on the list  → 2nd person = 8 + 20 = the 28th etc.....  2. Systematic sampling Advantages: Disadvantages: Known and equal chance of any element being selected. Less expensive…faster than Small loss in sampling SRS. precision.  3. Stratified sampling Principle: A two-step process in which the population is partitioned into subpopulations, or strata. Elements are selected from each stratum by a random procedure, usually SRS.  3. Stratified sampling Procedure: Identify and define the population. Determine the desired sample size. Identify the variable and subgroups (strata) for which you want to guarantee appropriate, equal representation. Randomly select, using a table of random numbers) an “appropriate” number of individuals from each of the stratum, appropriate meaning an equal number of individuals. 3. Stratified sampling  3. Stratified sampling For example: If a department has 1000 employees consisting of 900 males and 100 females, and you intend on sampling 10% of the total, then you proceed randomly as usual, drawing 90 males at random and 10 females at random. If you had used the employee list of names, regardless of gender, you might not have obtained 10 females at random because there's so few of them.  3. Stratified sampling Disadvantages: Advantages: More complex sampling plan requiring different More accurate. sample sizes for each stratum.  4. Cluster sampling Principle: Involves selecting the sample units in groups. The target population is first divided into subpopulations, or clusters. Then a random sample of clusters is selected, based on a probability sampling technique such as SRS.  4. Cluster sampling Principle: Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible. Ideally, each cluster should be a small-scale representation of the population. Cluster: a group of sampling units close to each other i.e. crowding together in the same area or neighborhood.  4. Cluster sampling Procedure: Identify and define the population. Determine the desired sample size. Identify and define a logical cluster. List all clusters (or obtain a list) that make up the population of clusters. Estimate the average number of population members per cluster.  4. Cluster sampling Procedure: Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster. Randomly select the needed number of clusters by using a table of random numbers. Include in your study all population members in each selected cluster. 4. Cluster sampling  4. Cluster sampling Advantages: Disadvantages: Simple as complete list of Potential problem is that sampling units within cluster members are more population not required. likely to be alike, than those in another cluster (homogenous)…. Less travel/resources required.  5. Multi-stage sampling Principle: Cluster sampling repeated at a number of levels. 5. Multi-stage sampling  Sampling errors: Sampling error (random error):  Random difference between sample and population from which sample drawn Size of error can be measured in probability samples Expressed as “standard error” of mean, proportion… Standard error (or precision) depends upon: –Size of the sample –Distribution of character of interest in population  Sampling errors: Systematic error (or bias): Inaccurate response (information bias) Selection bias