Which type of random sampling method is separated into groups and every group is represented in the sample?
How Stratified Random Sampling WorksBy Julia Simkus, published Jan 28, 2022 Show
Stratified sampling is a method of random sampling where researchers first divide a population into smaller subgroups, or strata, based on shared characteristics of the members and then randomly select among these groups to form the final sample. These shared characteristics can include gender, age, sex, race, education level, or income. Key Terms
The process of classifying the population into groups before sampling is called stratification. The strata must be mutually exclusive, and all members of the population can only be in one stratum. When stratifying, researchers tend to use proportionate sampling where they maintain the correct proportions to represent the population as a whole. For example, if the larger population contains 40% history majors and 60% English majors, the final sample should reflect these percentages. Disproportionate sampling is typically only used when studying an underrepresented group. Applications: When is it used
AdvantagesEfficient and manageableBy organizing a population into groups with similar characteristics, researchers save data collection time and can better manage a sample that would otherwise be too large to analyze. CheapThe research costs for this method of sampling are minimized as researchers save money by dividing a large population into smaller groups containing similar members rather than sampling every individual of a larger population. AccuracyStratified sampling can produce more precise estimates than simple random sampling when members of the subpopulations are homogeneous relative to the entire population. This gives a study more statistical power. LimitationsToo many differences within the populationA population can't be organized into subgroups if there are too many differences within the population or there is not enough information about the population at hand. PlanningResearchers must ensure that every member of the population fits into only one stratum and all the stata collectively contain every member of the greater population. This involves extra planning and information gathering that simple random sampling does not require. Sampling errorsSampling errors can occur when the sample does not end up accurately representing the population as a whole. If this occurs, the researcher would need to restart the sampling process. Examples
Cluster Sampling vs Stratified SamplingStratified sampling and cluster sampling both involve dividing a large population up into smaller groups and then selecting randomly among the subgroups to form a sample. However, the main difference is that researchers in stratified sampling divide the population into groups based on age, religion, ethnicity, or income level and randomly choose from these strata to form a sample. Alternatively, researchers in cluster sampling will use naturally divided groups to separate the population (i.e., city blocks or school districts) and then randomly select elements from these clusters to be a part of the sample. Stratified Sampling vs Quota Sampling?Quota sampling and stratified sampling both involve dividing a population into mutually exclusive subgroups and sampling a predetermined number of individuals from each. However, the most significant difference between these two techniques is that quota sampling is a non-probability sampling method while stratified sampling is a probability sampling method. In a stratified sample, individuals within each stratum are selected at random while in a quota sample, researchers choose the sample as opposed to randomly selecting it. Stratified sampling is also known as quota random sampling. About the AuthorJulia Simkus is an undergraduate student at Princeton University, majoring in Psychology. She plans to pursue a PhD in Clinical Psychology upon graduation from Princeton in 2023. Julia has co-authored two journal articles, one titled “Substance Use Disorders and Behavioral Addictions During the COVID-19 Pandemic and COVID-19-Related Restrictions," which was published in Frontiers in Psychiatry in April 2021 and the other titled “Food Addiction: Latest Insights on the Clinical Implications," to be published in Handbook of Substance Misuse and Addictions: From Biology to Public Health in early 2022. How to reference this article:How to reference this article:Simkus, J. (2022, Jan 28). Simple Random Sampling: Definition, Steps and Examples. Simply Psychology. www.simplypsychology.org/stratified-random-sampling.html SourcesBarnett, R. C., & Baruch, G. K. (1985). Women's involvement in multiple roles and psychological distress. Journal of Personality and Social Psychology, 49(1), 135–145. Briere, J., & Elliott, D. M. (2003). Prevalence and psychological sequelae of self-reported childhood physical and sexual abuse in a general population sample of men and women. Child abuse & neglect, 27(10), 1205-1222. How to use stratified random sampling to your advantage. Qualtrics. (n.d.). Retrieved from https://www.qualtrics.com/experience-management/research/stratified-random-sampling/ Llewellyn, D. J., & Wilson, K. M. (2003). The controversial role of personality traits in entrepreneurial psychology. Education+ Training. Nickolas, S. (2021, May 19). How stratified random sampling works. Investopedia. Retrieved January 27, 2022, from https://www.investopedia.com/ask/answers/032615/what-are-some-examples-stratified-random-sampling.asp Shi, F. (2015). Study on a stratified sampling investigation method for resident travel and the sampling rate. Discrete Dynamics in Nature and Society, 2015. Syme, G. J., & Williams, K. D. (1993). The psychology of drinking water quality: an exploratory study. Water Resources Research, 29(12), 4003-4010. Home | About Us | Privacy Policy | Advertise | Contact Us Simply Psychology's content is for informational and educational purposes only. Our website is not intended to be a substitute for professional medical advice, diagnosis, or treatment. © Simply Scholar Ltd - All rights reserved Which type of random sampling method is separated into groups?Stratified random sample: The population is first split into groups. The overall sample consists of some members from every group. The members from each group are chosen randomly.
What sampling is group by group selection of sample?Cluster sampling
Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.
What is stratified random sampling technique?Stratified random sampling (also known as proportional random sampling and quota random sampling) is a probability sampling technique in which the total population is divided into homogenous groups (strata) to complete the sampling process.
Where is stratified random sampling used?When to use Stratified Random Sampling? Stratified random sampling is an extremely productive method of sampling in situations where the researcher intends to focus only on specific strata from the available population data. This way, the desired characteristics of the strata can be found in the survey sample.
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