Sampling

 

Hello Students! I'm Teacher Cielo and today we shall discuss all the underlying concepts for sampling! Before we proceed I would like you to help someone today. Please read the text below. “Yehudi is a student researcher. He’s interested in studying why people believe the way they do about the issue on Extra-Judicial Killing. He puts together a survey asking people for reasons to support their side about the issue on Extra-Judicial Killing. Yehudi is puzzled as to who will be his respondents of the study. He wants his research to say something about the opinions of the Grade 11 and 12 Senior High School Students in Bohol, but it wouldn’t be possible for him to give the survey to every Grade 11 and 12 Senior High School Students in Bohol because that would take forever. So, he needs to develop a sample, or group of subjects. This is done through a process called sampling. The goal is to choose a sample that represents the whole population so that Yehudi can make inferences about the population from his sample. Now, what do you think is the appropriate sampling procedure to use?”   The following task should be done by the students: 1.    Formulate the criteria for choosing the participants in the study. 2.    What do you think is the appropriate sampling procedure to use?         In order to help Mr. Yehudi, kindly study the notes below. I.                     What is Sampling? Sampling is a process through which a researcher selects a portion or segment from the population at the center of the researcher’s study. Population is a group of persons or objects that possess some common characteristics that are of interest to the researcher, and about which the researcher seeks to learn more. There are two groups of population: the target population and the accessible population. Target population. It is composed of the entire group of people or objects to which the researcher wishes to generalize the findings of the study. Accessible population. It is a portion of the population to which the researcher has reasonable access.   II. Types of Sampling   TYPES OF SAMPLING 1.       Probability Sampling. It is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. All the members have an equal opportunity to be a part of the sample with this selection parameter. 2.       Non-probability Sampling. In this sampling method, the members are chosen at random. This sampling method is not fixed or predefined selection. Types of probability sampling with examples: There are four types of probability sampling techniques: ·     Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample. For example, in an organization of 500 employees, if the HR team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected. ·     Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inference from the feedback. For example, if the United States government wishes to evaluate the number of immigrants living in the Mainland US, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful immigration data. ·     Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. This type of sampling method has a predefined range, and hence this sampling technique is the least time-consuming. For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10). ·     Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately. For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results. Uses of probability sampling There are multiple uses of probability sampling: ·     Reduce Sample Bias: Using the probability sampling method, the bias in the sample derived from a population is negligible to non-existent. The selection of the sample mainly depicts the understanding and the inference of the researcher. Probability sampling leads to higher quality data collection as the sample appropriately represents the population. ·     Diverse Population: When the population is vast and diverse, it is essential to have adequate representation so that the data is not skewed towards one demographic. For example, if Square would like to understand the people that could make their point-of-sale devices, a survey conducted from a sample of people across the US from different industries and socio-economic backgrounds helps. ·     Create an Accurate Sample: Probability sampling helps the researchers plan and create an accurate sample. This helps to obtain well-defined data.   Brain Break! How can we use sampling in our daily lives?         Types of non-probability sampling with examples Four types of non-probability sampling explain the purpose of this sampling method in a better manner: ·     Convenience sampling: This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling, because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations where there are resource limitations such as the initial stages of research, convenience sampling is used. For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly. ·     Judgmental or purposive sampling: Judgemental or purposive samples are formed by the discretion of the researcher. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample. ·     Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, it will be extremely challenging to survey shelterless people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method in situations where the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information. ·     Quota sampling:  In Quota sampling, the selection of members in this sampling technique happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples.   How important is to have an accurate sampling procedure in surveys for this coming election?   This time I want you to ponder on the questions Mr. Yehudi asked and helped him  answer it. I know that it will be hard but it will get better as long as we don't stop learning.      Brain Break