Sampling techniques

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Sampling techniques

  1. 1. Sampling techniques What is sampling? A shortcut method for investigating a whole population Data is gathered on a small part of the whole parent population or sampling frame, and used to inform what the whole picture is like Why sample? In reality there is simply not enough; time, energy, money, labour/man power, equipment, access to suitable sites to measure every single item or site within the parent population or whole sampling frame. Therefore an appropriate sampling strategy is adopted to obtain a representative, and statistically valid sample of the whole. Random sampling Least biased of all sampling techniques, there is no subjectivity - each member of the total population has an equal chance of being selected Can be obtained using random number tables Systematic sampling Samples are chosen in a systematic, or regular way. They are evenly/regularly distributed in a spatial context, for example every two metres along a transect line They can be at equal/regular intervals in a temporal context, for example every half hour or at set times of the day They can be regularly numbered, for example every 10th house or person Stratified sampling This method is used when the parent population or sampling frame is made up of sub-sets of known size. These sub-sets make up different proportions of the total, and therefore sampling should be stratified to ensure that results are proportional and representative of the whole. A. Stratified systematic sampling The population can be divided into known groups, and each group sampled using a systematic approach. The number sampled in each group should be in proportion to its known size in the parent population. For example: the make-up of different social groups in the population of a town can be obtained, and then the number of questionnaires carried out in different parts of the town can be stratified in line with this information. A systematic approach can still be used by asking every fifth person.
  2. 2. B. Stratified random sampling A wide range of data and fieldwork situations can lend themselves to this approach - wherever there are two study areas being compared, for example two woodlands, river catchments, rock types or a population with sub-sets of known size, for example woodland with distinctly different habitats. Random point, line or area techniques can be used as long as the number of measurements taken is in proportion to the size of the whole. Cluster Sampling Cluster sampling is a sampling technique where the entire population is divided into groups, or clusters, and a random sample of these clusters are selected. All observations in the selected clusters are included in the sample. Quota Sampling A sampling method of gathering representative data from a group. As opposed to random sampling, quota sampling requires that representative individuals are chosen out of a specific subgroup. For example, a researcher might ask for a sample of 100 females, or 100 individuals between the ages of 20-30. DIFFERENT TYPES OF SAMPLING Probability Sampling A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection.  Simple random sampling  In this technique, each member of the population has an equal chance of being selected as subject. The entire process of sampling is done in a single step with each subject selected independently of the other members of the population.  Systematic sampling  is also known as proportional random sampling. This is a probability sampling technique wherein the subjects are initially grouped into different classifications such as age, socioeconomic status or gender.  
  3. 3.  Stratified sampling  can be likened to an arithmetic progression wherein the difference between any two consecutive numbers is the same. Say for example you are in a clinic and you have 100 patients. Multi-stage sampling  This probability sampling technique involves a combination of two or more sampling techniques enumerated above. In most of the complex researches done in the field or in the lab, it is not suited to use just a single type of probability sampling.  Cluster sampling  is done when simple random sampling is almost impossible because of the size of the population. Just imagine doing a simple random sampling when the population in question is the entire population of Asia Non-probability  sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.  Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher.  Sequential sampling is a non-probability sampling technique wherein the researcher picks a single or a group of subjects in a given time interval, conducts his study, analyzes the results then picks another group of subjects if needed and so on.  Quota sampling is a non-probability sampling technique wherein the assembled sample has the same proportions of individuals as the entire population with respect to known characteristics, traits or focused phenomenon.  Judgmental sampling is a non-probability sampling technique where the researcher selects units to be sampled based on their knowledge and professional judgment.  Snowball sampling is a non-probability sampling technique that is used by researchers to identify potential subjects in studies where subjects are hard to locate.
  4. 4. Basic Statistical Symbols[*] A distribution, also called the distribution for the independent variable A distribution that is that the not same as X, also called the dependent variable Sum all the elements [numbers] in the distribution 2 Square all the elements [numbers] then add them to each other ( )2 Add all the elements then square the summation N The number of elements in a population distribution n The number of elements in a sample distribution X1 The first element [number] in a distribution X2 The second element [number] in a distribution Xn The last element in a distribution … All the elements between two points in a distribution Y Multiply X1 by Y1, X2 by Y2, …, Xn by Ynand sum the distribution Greek letter for alpha: the intercept or a type I error Greek letter for beta: the slope or a type II error Greek letter for sigma, meaning the standard deviation for the population s Letter s, meaning the standard deviation for the sample s2 Letter s square, meaing the variance for the sample s1 In a time series, the first data point; subscripts note the location of the data point sn – 1 In a time series, the second to the last data point. sn In a time series, the last data point. Greek letter mu, meaning the [arithmetic] mean of the population Also call "X-bar" meaning the [arithmetic] mean of the sample Md Median Mo Mode Gm Geometric mean Hm Harmonic mean  
  5. 5. Data Collection Methods As we have seen in the definition of statistics, data collection is a fundamental aspect and as a consequence, there are different methods of collecting data which when used on one particular set will result in different kinds of data. Let's move on to look at these individual methods of collection in order to better understand the types of data that will result. Census Data Collection is a method of collecting data whereby all the data from each and every member of the population is collected. Sample Data Collection which is commonly just referred to as sampling, is a method which collects data from only a chosen portion of the population. Experimental Data Collection involves one performing an experiment and then collecting the data to be further analyzed. Experiments involve tests and the results of these tests are your data. Observational Data Collection method involves not carrying out an experiment but observing without influencing the population at all. Observational data collection is popular in studying trends and behaviors of society where, for example, the lives of a bunch of people are observed and data is collected for the different aspects of their live.

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