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The document discusses key concepts in sampling techniques and survey methods, including defining a population and sample, different types of sampling designs (e.g. simple random sampling), the importance of a sampling frame, total survey design which considers all aspects of designing and implementing a survey from objectives to analysis, and provides an example of designing a sampling plan to estimate the average farm acreage and corn acreage based on a population of 4 farms.
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This document discusses different sampling techniques used in statistical analysis. It defines key terms like population, sample, parameter, and statistic. It explains probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, systematic sampling, and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling, quota sampling, judgmental sampling, and snowball sampling. The document provides details on each sampling method, including their merits and demerits. It also discusses sources of sampling errors and ways to reduce them.
sampling techniques.pptx
The document discusses various sampling techniques used in statistical analysis. It defines key terms like population, sample, parameter, statistic, and sampling error. It then describes different probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, systematic sampling, and multistage sampling. Each method is explained along with its merits and demerits. The document provides an overview of important sampling concepts and techniques.
sampling methods
This document outlines different sampling methods used in quantitative and qualitative research. It discusses the purpose of sampling, stages in selecting a sample, and types of probability and non-probability sampling. For quantitative research, it describes random sampling, stratified random sampling, cluster sampling, and systematic sampling. For qualitative research, it discusses purposive sampling techniques like maximal variation sampling, typical case sampling, and theory or concept sampling. The document stresses the importance of representation and generalization in samples and notes some ethical considerations in data collection like maintaining trust and informing participants.
Sampling Techniques.pptx
This document discusses sampling techniques and sample types. It defines key terms like population, sample, sampling frame, and target population. It outlines the main stages in sample selection and describes four common probability sampling techniques: random sampling, stratified random sampling, cluster sampling, and systematic sampling. It provides examples of how each technique is implemented. The document also discusses non-probability sampling techniques like convenience sampling, purposive sampling, and quota sampling along with their disadvantages.
sampling
Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
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Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components of research including the problem statement, theory, variables, design, instrumentation, sampling, analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
Sampling Design
1. There are two main types of sampling designs - probability sampling and non-probability sampling. Probability sampling allows you to estimate errors and make statistical inferences, while non-probability sampling does not.
2. Some common probability sampling techniques include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. These aim to select a sample that accurately represents the population.
3. When designing a sample, researchers must determine the sampling unit, sampling frame, sample size, and sampling procedure based on factors like the population, budget, and goal of reducing bias and sampling error. Probability sampling is generally preferred but non-probability techniques may also be used for small populations.
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- 8. 1.3 Types of Surveys & Sampling Methods non-probabilistic Quota sample : elements are chosen in the field to meet predetermined number of cases in different categories (e.g. 40% men, 60% women) Expert sample : elements chosen on the basis of informed opinion that they are representative probabilistic Inferences about the underlying population cannot be made Probability of obtaining each sample can be computed, confidence intervals can be developed, bounds on sampling errors, etc. Simple Random Sampling Stratified Random Sampling Cluster Sampling Systematic Sampling
- 24. 1.7 Total survey design 案例： 调查北京市老年人的住房条件
Editor's Notes
- Difference between target population and sample population may arise and is problematic, under certain circumstances. Selection bias in trade analysis for example. Example of the poll in the US. If having a phone or not is a systematic process or a random process, then, the implications are different.
- Random NE haphazard Typically don’t enumerate samples, then inclusion probs Populations usually too big More likely to assign the elements selection probs and proceed from there
- Start on most difficult part of this class Essential to understand material in Ch2, especially early sections/this week’s lecs Possible to by chance select an unrep. sample using a stat design (e.g., SRS of class)
- Start on most difficult part of this class Essential to understand material in Ch2, especially early sections/this week’s lecs Possible to by chance select an unrep. sample using a stat design (e.g., SRS of class)
- SAMPLING FRAME EXAMPLE Target population = Ames households OU = household There is no list of households, but we can list out telephone numbers Frame = list of all possible (land line) telephone numbers in the Ames area SU = telephone number Frame includes non-working and business numbers that do not correspond to households Frame excludes households who have no land line phone
- What disinguishes SS from the rest of your statistics classes We usually start with a regression model or AOV model that assumes errors are normal (write these models and the error assumptions on the board) OLD NOTES Finite Infinite Land in US Yield of corn variety (inf. # conditions) People in CA Impact of chemical on pests Strata blocks Clusters split-plots Means Percentiles Randomization Model based, e.g., resids ~ normal (Model assisted)
- Sampling counties Sampling states, then counties
- SAMPLING FRAME EXAMPLE Target population = Ames households OU = household There is no list of households, but we can list out telephone numbers Frame = list of all possible (land line) telephone numbers in the Ames area SU = telephone number Frame includes non-working and business numbers that do not correspond to households Frame excludes households who have no land line phone