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- 1. 1 NAME: ABDULAZIZ BELLO REG NO: 2101271037 COURSE: COMMUNICATION RESEARCH DEPT: HNDI MASS COMMUNICATION (MO) TITLE: ASSIGNMENT QUESTION: Define probability Types of probability Define non probability Types of non probability INTRODUCTION Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. What is probability sampling? Probability sampling is a technique in which the researcher chooses samples from a larger population using a method based on probability theory. For a participant to be considered as a probability sample, he/she must be selected using a random selection. Types of probability sampling There are four commonly used types of probability sampling designs: Simple random sampling Stratified sampling Systematic sampling Cluster sampling Simple random sampling Simple random sampling gathers a random selection from the entire population, where each unit has an equal chance of selection. This is the most common way to select a random sample. To compile a list of the units in your research population, consider using a random number generator. There are several free ones available online, such as random.org, calculator.net, and randomnumbergenerator.org.
- 2. 2 Example: Simple random samplingYou are researching the political views of a municipality of 4,000 inhabitants. You have access to a list with all 4,000 people, anonymized for privacy reasons. You have established that you need a sample of 100 people for your research. Writing down the names of all 4,000 inhabitants by hand to randomly draw 100 of them would be impractical and time-consuming, as well as questionable for ethical reasons. Instead, you decide to use a random number generator to draw a simple random sample. If the first number generated by the program is 1735, this means that resident #1735 on your list should be selected to be part of the sample. You continue by matching each number with the respective resident on the list. Stratified sampling Stratified sampling collects a random selection of a sample from within certain strata, or subgroups within the population. Each subgroup is separated from the others on the basis of a common characteristic, such as gender, race, or religion. This way, you can ensure that all subgroups of a given population are adequately represented within your sample population. For example, if you are dividing a student population by college majors, Engineering, Linguistics, and Physical Education students are three different strata within that population. To split your population into different subgroups, first choose which characteristic you would like to divide them by. Then you can select your sample from each subgroup. You can do this in one of two ways: By selecting an equal number of units from each subgroup By selecting units from each subgroup equal to their proportion in the total population Systematic sampling Systematic sampling draws a random sample from the target population by selecting units at regular intervals starting from a random point. This method is useful in situations where records of your target population already exist, such as records of an agency’s clients, enrollment lists of university students, or a company’s employment records. Any of these can be used as a sampling frame.
- 3. 3 To start your systematic sample, you first need to divide your sampling frame into a number of segments, called intervals. You calculate these by dividing your population size by the desired sample size. Then, from the first interval, you select one unit using simple random sampling. The selection of the next units from other intervals depends upon the position of the unit selected in the first interval. Cluster sampling Cluster sampling is the process of dividing the target population into groups, called clusters. A randomly selected subsection of these groups then forms your sample. Cluster sampling is an efficient approach when you want to study large, geographically dispersed populations. It usually involves existing groups that are similar to each other in some way (e.g., classes in a school). There are two types of cluster sampling: Single (or one-stage) cluster sampling, when you divide the entire population into clusters Multistage cluster sampling, when you divide the cluster further into more clusters, in order to narrow down the sample size WHAT IS NON-PROBABILITY SAMPLING? Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research. Non-probability sampling is a method in which not all population members have an equal chance of participating in the study, unlike probability sampling. Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample compared to pre-determined sample size). Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations.
- 4. 4 TYPES OF NON-PROBABILITY SAMPLING Convenience sampling: Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population. Ideally, in research, it is good to test a sample that represents the population. But, in some research, the population is too large to examine and consider the entire population. It is one of the reasons why researchers rely on convenience sampling, which is the most common non-probability sampling method, because of its speed, cost-effectiveness, and ease of availability of the sample. Consecutive sampling: This non-probability sampling method is very similar to convenience sampling, with a slight variation. Here, the researcher picks a single person or a group of a sample, conducts research over a period, analyzes the results, and then moves on to another subject or group if needed. Consecutive sampling technique gives the researcher a chance to work with many topics and fine-tune his/her research by collecting results that have vital insights. Quota sampling: Hypothetically consider, a researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization, also known as the population. To understand better about a population, the researcher will need only a sample, not the entire population. Further, the researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups. Judgmental or Purposive sampling: In the judgmental sampling method, researchers select the samples based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those people who they deem fit to participate in the research study. Judgmental or purposive sampling is not a scientific method of sampling, and the downside to this sampling
- 5. 5 technique is that the preconceived notions of a researcher can influence the results. Thus, this research technique involves a high amount of ambiguity. Snowball sampling: Snowball sampling helps researchers find a sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample. Non-probability sampling examples Here are three simple examples of non-probability sampling to understand the subject better. 1. An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample. 2. In an organization, for studying the career goals of 500 employees, technically, the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata using quota sampling. 3. Researchers also use this type of sampling to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer to other subjects suffering from the same ailment to form a subjective sample to carry out the study. References "Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory", Alan Stuart and Keith Ord, 6th Ed, (2009), ISBN 978-0-534-24312-8. William Feller, An Introduction to Probability Theory and Its Applications, (Vol 1), 3rd Ed, (1968), Wiley, ISBN 0-471-25708-7. Probability Theory The Britannica website Hacking, Ian (1965). The Logic of Statistical Inference. Cambridge University Press. ISBN 978-0-521-05165-1.[page needed] Finetti, Bruno de (1970). "Logical foundations and measurement of subjective probability". Acta Psychologica. 34: 129–145. doi:10.1016/0001-6918(70)90012- 0.