Sampling Methods PDF

Research II Quarter II Handout#5- WHAT IS SAMPLING? Any field of research constitute a universe or population. According to OECD, a universe or population “represents the entire group of units which is the focus of the study.” This means that a universe...

Research II Quarter II Handout#5- WHAT IS SAMPLING? Any field of research constitute a universe or population. According to OECD, a universe or population “represents the entire group of units which is the focus of the study.” This means that a universe or population consists of all the people who belong to a community, organization, institution, or those in a geographical location, or a special ethnic or economic group. A researcher identifies the universe or population in a study in consideration to purpose and coverage of the study. A population could also consist of non-human units such as farms, fields, establishments, houses, buildings, etc. Census Inquiry versus Sample Survey A census inquiry involves a data gathering process which is total enumeration, all members of the population are part of the inquiry. In sample survey, representatives are chosen from the population to be part of the data gathering process. In a sample survey or any study which requires the researcher to not include the entire population in a study, a SAMPLE DESIGN is badly needed. What is a SAMPLE DESIGN? A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design is determined before data are collected (Kothari, 2004). Sampling is the process of selecting units or representatives from a population of interest. This should be done so that by studying information from the chosen sample, results may fairly generalize. Sample refers to the representatives of a population. STEPS IN SAMPLE DESIGN 1. Type of Universe- The first step is to clearly define the universe or population. A universe can be finite or infinite. In a finite universe, the number of items is certain. In an infinite universe, the total number of items can not be identified. The people living in a certain barangay is a finite universe while the people who visit the mall on weekends is an infinite universe. 2. Sampling Unit – Before selecting the sample, the researcher must decide for the sampling unit. A sampling unit can be a geographical one such as state, district, village, etc. It could also be a social unit such as family, club, school, etc. or it may be an individual. The researcher will have to decide one or more of such units that he has to select for his study. 3. Source list- The source list or sampling frame includes all items in a universe (for finite). This is where the sample is to be drawn. If a source list is not available, the researcher should prepare it. A good source list is comprehensive, correct, reliable, and appropriate. 4. Size of sample. This refers to the number of items to be selected from the universe to constitute a sample. The size of the sample should neither be excessively large, nor to small. It should be optimum. An OPTIMUM SAMPLE is one which fulfills the Factors to consider in choosing the SAMPLE SIZE requirement of: 1. Efficiency 1. Size of population variance 2. Representativeness --Larger variance requires a bigger sample size. 3. Reliability 2. Parameters of Interest 4. Flexibility 3. Cost and Budgetary Constraints 5. Parameters of interest – In determining the sample design, one must consider the question of the specific population parameters which are of interest. Parameters of interest include certain characteristics in a population. These are the focus of your data. 6. Budgetary constraint – Cost consideration has a major impact in deciding the sample size and the type of sample. This may prompt the researcher to use a non-probability sample. 7. Sampling procedure/ technique – The researcher must decide the type of sample he will use. He must consider the technique to be used in selecting the items for the sample. In fact, this technique or procedure stans for the sample design itself. Systematic Bias and Sampling Error A systematic bias results from errors in the sampling procedures. It can not be reduced or eliminated by increasing the sample size. At best the causes responsible for these errors can be detected and corrected. A systematic bias is the result of one or more of the following factors: 1. Inappropriate sampling frame – A biased representation of the universe may result in a systematic bias. 2. Defective measuring device – If the measuring device is constantly in error, it will result in systematic bias. Biased questionnaires or interviewers may also bring systematic bias. 3. Non-respondents – A systematic bias may occur if the researcher was not able to include all samples in the study. This normally happens when the researcher failed to establish a contact with the sample, or he/she failed to receive a response from the sample. 4. Indeterminacy principle – This is especially true in qualitative research wherein the participants must be observed. Sometimes, participants act differently when kept under observation than what they do when kept in non-observed situations. 5. Natural bias in the reporting of data – This is often committed by the participant/ respondent wherein they tend to withhold the correct data or information needed in the research. This may cause systematic bias in the study. Sampling errors are the random variations in the sample estimates around the true population parameters. Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data (Hayes, 2020). The results found in the sample thus do not represent the results that would be obtained from the entire population. Sampling error can be reduced by randomizing sample selection and/or increasing the number of observations. Characteristics of a Good Sample Design A good sample design must… 1. result in a truly representative sample. 2. be such which results in a small sampling error. 3. be viable in the context of funds available for the research study. 4. be such so that systematic bias can be controlled in a better way. 5. generate a sample that will give results that can be applied in general, for the universe with a reasonable level of confidence. SAMPLING TECHNIQUES There are two main types of sampling techniques: probability sampling and non-probability sampling. Probability Sampling/ Random Sampling In probability sampling or random sampling, every item in the population/ universe has an equal chance of being included in sample. It has the greatest freedom from bias. But it could be costly in terms of time and energy for a given sampling error (Brown, 1947 cited in Taherdoost, 2016). Probability sampling involves random selection wherein the researcher can make statistical inferences about the whole group. This means that the results obtained from the samples are representative of the whole population. This sampling technique is mostly used in quantitative research. Types of Probability Sampling 1. Simple Random Sampling – This type of sampling gives equal opportunity to all items in the population. Sometimes, simple random sampling is called the fishbowl technique. It can be done by writing all items on a piece of paper. Then these are placed in a fishbowl and the items to be chosen as samples are randomly picked from the bowl. Another method is the use of random number generators or other techniques that are based entirely on chance. 2. Systematic Sampling – In this type of sampling, every member of the population is listed with a number. Samples are chosen at regular nth intervals. For example, if surveying a sample of students, every fifth student in the list may be selected. 3. Stratified Random Sampling – The population is divided into strata (or subgroups) that may differ in important ways. This sampling enables you to draw a more precise conclusions by ensuring that every stratum is properly represented in the sample. A stratum (singular) is a natural set of items. It might be based on company size, gender, occupation, and others. Based on the overall proportions of the population, you calculate how many items should be sampled from each subgroup. Then you use simple random sampling or systematic sampling to select a sample from each subgroup. Example: There are 14 males and 26 females in a class. You want to ensure that the sample reflects the gender balance of the class, so you sort the population into two strata based on gender. Then you use random sampling in each group selecting 10 males (35%) and 20 females (65%) to complete 30 samples. 4. Cluster Sampling – This also involves dividing the population into subgroups called clusters, but each cluster should have similar characteristics to the whole sample. Instead of sampling from each cluster, you select an entire cluster to be your sample. Example: Your samples should be residents of Binalonan. But you don’t have the time to undergo survey in all the barangays. So, you divide the population according to barangay. You randomly select 5 barangays. You may include all residents in the 5 barangays, or you may randomly select your samples from these 5 barangays. 5. Multi-stage Sampling – It is a process of moving from a broad to a narrow sample, using a step by step process (Ackoff, 1953 cited in Taherdoost, 2016). The main purpose of multi-stage sampling is to select samples which are concentrated in a few geographical regions (Taherdoost, 2016). Example: A publisher of an automobile magazine wants to conduct a survey about their best practices regarding preventive maintenance services in the Philippines. He could simply take a random sample of automobile owners within the entire Philippine population. But this is time consuming and expensive. A cheaper alternative would be to use multi-stage sampling. This would involve dividing Philippines into several geographical regions. Some of these regions are chosen at random, and then subdivisions are made, perhaps based on local units. Next, some of these are again chosen at random and then divided into smaller areas, such as towns or cities. Nonprobability Sampling/ Nonrandom sapling This type of sampling technique is often associated with qualitative research. Samples to be included in the research are selected based on non-random criteria. Not all items in the population has a chance of being included. Nonprobability sampling is easier and cheaper, but it has a high risk of sampling bias. This means that the results obtained from the chosen samples can not be used to make valid statistical inferences about the whole population. 1. Quota Sampling – Participants are chosen based on predetermined characteristics so that the total sample will have the same distribution of characteristics as the wider population (Davis, 2005 cited in Taherdoost, 2016). 2. Snowball Sampling – Few cases are used to help encourage other cases to take part in the study, thereby increasing sample size. This approach is utilized if the population is so small, and they are difficult to access due to their closed nature (secret societies, inaccessible professions). 3. Convenience Sampling- Participants are selected because they are often readily and easily available. This is an easy and inexpensive way to gather initial data. But you cannot tell if the samples are representatives of the population so you cannot generalize the result. 4. Purposive or Judgmental Sampling – Particular settings, persons or events are selected deliberately in order to provide important information that cannot be obtained from other choices. SAMPLE PROCEDURES OF SAMPLING A. Random Sampling Method Hayley wants to carry out some research on her class. She wants a sample of 1212 people out of the 3030 in her class. Use a random sampling technique to determine the reference number of the students in the class who should be included in the sample. Do not include duplicated data. 1. List every member of the population. As there are 3030 students in the class, we have the numbers 11 to 3030. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 2. Associate each member of the population with a unique reference number. Each student is already numbered from 11 to 3030. 3. Use a random number generator to select the number of data points in the sample. B. Systematic Sampling A drinks company produces 12001200 bottles of pop every 3030 minutes. For quality control purposes, 1212 bottles are selected and checked. Each bottle passes through the machine in a single file. Using a systematic sampling technique, determine the bottles that will be selected for the sample. 1. Order the population and give each data entry a unique reference number. As each bottle passes through the machine in a single file, we can assume that the first bottle has a reference number 11, the second number 22, etc. 2. Calculate the number of items of data in the sample. As we want a sample of 1212 bottles and we are using a systematic sample, we need to choose the bottles using a sequence. The interval for the data selection is: So, we need to pick every 100th term in the data. 3. Use a random number generator to select the first item of data. As we need to pick every 100th term, the first number that will form the starting point in the sample selection must be randomly chosen from the first 100 terms. Using a random number generator, we get the number 2727, so we choose the first item of data in the sample to be the 27th bottle. 4. Select the remaining items of data following the given sequence. As we are selecting every 100th item in regular intervals, the next bottle will be number 127, 227, 327,127,227,327, and so on until we reach the 1200th bottle in 30 minutes. The sample will therefore contain the bottles with the reference numbers: 27, 127, 227, 327, 427, 527, 627, 727, 827, 927, 1027,27,127,227,327,427,527,627,727,827,927,1027, and 1127.1127. These numbers are in the sequence 100n-73