Survey methodology | Sampling techniques
In statistics, survey sampling describes the process of selecting a sample of elements from a target population to conduct a survey. The term "survey" may refer to many different types or techniques of observation. In survey sampling it most often involves a questionnaire used to measure the characteristics and/or attitudes of people. Different ways of contacting members of a sample once they have been selected is the subject of survey data collection. The purpose of sampling is to reduce the cost and/or the amount of work that it would take to survey the entire target population. A survey that measures the entire target population is called a census. A sample refers to a group or section of a population from which information is to be obtained Survey samples can be broadly divided into two types: probability samples and super samples. Probability-based samples implement a sampling plan with specified probabilities (perhaps adapted probabilities specified by an adaptive procedure). Probability-based sampling allows design-based inference about the target population. The inferences are based on a known objective probability distribution that was specified in the study protocol. Inferences from probability-based surveys may still suffer from many types of bias. Surveys that are not based on probability sampling have greater difficulty measuring their bias or sampling error. Surveys based on non-probability samples often fail to represent the people in the target population. In academic and government survey research, probability sampling is a standard procedure. In the United States, the Office of Management and Budget's "List of Standards for Statistical Surveys" states that federally funded surveys must be performed: selecting samples using generally accepted statistical methods (e.g., probabilistic methods that can provide estimates of sampling error). Any use of nonprobability sampling methods (e.g., cut-off or model-based samples) must be justified statistically and be able to measure estimation error. Random sampling and design-based inference are supplemented by other statistical methods, such as model-assisted sampling and model-based sampling. For example, many surveys have substantial amounts of nonresponse. Even though the units are initially chosen with known probabilities, the nonresponse mechanisms are unknown. For surveys with substantial nonresponse, statisticians have proposed statistical models with which the data sets are analyzed. Issues related to survey sampling are discussed in several sources, including Salant and Dillman (1994). (Wikipedia).
Statistics - Types of sampling
This video will show you the many ways that you could sample. Remember to look for those small differences such as if you are breaking things into groups first. For more videos visit http://www.mysecretmathtutor.com
From playlist Statistics
Statistics: Introduction (12 of 13) Sampling: Definitions and Terms
Visit http://ilectureonline.com for more math and science lectures! We will review a sampling of definitions and terms of statistics: census, sampling frame, sampling plan, judgment sample, probability samples, random samples, systematic sample, stratified sample, and cluster sample. To
From playlist STATISTICS CH 1 INTRODUCTION
Sampling Techniques & Cautions (Full Length)
I define and discuss the differences of observational studies and experiments. I then discuss the difference between a sample and a census. I introduce two types of sampling techniques that yield biased results...Voluntary Response and Convenience Sampling. I discuss Stratified Random S
From playlist AP Statistics
I define and discuss the differences of observational studies and experiments. I then discuss the difference between a sample and a census, then introduce two types of sampling techniques that yield biased results...Voluntary Response and Convenience Sampling. Check out http://www.ProfRo
From playlist AP Statistics
This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com
From playlist Introduction to Statistics
Powered by https://www.numerise.com/ Surveys & questionnaires (2)
From playlist Collecting data
An overview of the most popular sampling methods used in statistics. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sampling-in-statistics
From playlist Sampling
What is purposive (deliberate) sampling? Types of purposive sampling, advantages and disadvantages. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sam
From playlist Sampling
Statistics Lesson #1: Sampling
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). It includes defining and looking at examples of five sampling methods: simple random sampling, convenience sampling, systematic sampling, stratified sampling, cluster sampling. We also l
From playlist Statistics
SICSS 2019 -- Survey research in the digital age
From playlist All Videos
New Galaxy Cluster Samples with DES, RASS and SPT: a prelude to eROSITA by Joseph J. Mohr
Program Cosmology - The Next Decade ORGANIZERS : Rishi Khatri, Subha Majumdar and Aseem Paranjape DATE : 03 January 2019 to 25 January 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore The great observational progress in cosmology has revealed some very intriguing puzzles, the most i
From playlist Cosmology - The Next Decade
Cautions about Sample Surveys, Causes of Bias, and Inference defined
I finish my three part introduction of Sample Surveys. In this lecture I define Undercoverage, Non-Response, discuss causes of Biased Results, define Inference, and preview Sample Distributions. Find free review test, useful notes and more at http://www.mathplane.com If you'd like to make
From playlist AP Statistics
Sampling Methods and Bias with Surveys: Crash Course Statistics #10
Today we’re going to talk about good and bad surveys. Surveys are everywhere, from user feedback surveys to telephone polls, and those questionnaires at your doctor's office. Still, with their ease to create and distribute, they're also susceptible to bias and error. So today we’re going t
From playlist Statistics
SICSS 2017 - Survey Research in the Digital Age (Day 4. June 22, 2017)
The first Summer Institute in Computational Social Science was held at Princeton University from June 18 to July 1, 2017, sponsored by the Russell Sage Foundation. For more details, please visit https://compsocialscience.github.io/summer-institute/2017/
From playlist SICSS 2017 - Surveys (6/22)
Statistics - 1.1 Intro to Statistics
The first video in the Statistics series explores the differences between a population and a sample and between a parameter and a statistic. Power Point: https://bellevueuniversity-my.sharepoint.com/:p:/g/personal/kbrehm_bellevue_edu/EVJ0yg01F7JCg0CuBdqPYj8BhiXmzIKEoOr0eEhKb8spfg?e=dre62P
From playlist Applied Statistics (Entire Course)
Probability and non-probability sampling
In this video, Professor Matthew Salganik discusses probability and non-probability sampling for survey research in the digital age. Link to slides: https://github.com/compsocialscience/summer-institute/blob/master/2020/materials/day4-surveys/02-nonprobability-sampling.pdf Links to other m
From playlist SICSS 2020
What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.
From playlist Sampling