Sample design
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This document discusses different aspects of sampling design including probability and non-probability sampling methods, criteria for selecting sampling procedures such as inappropriate sampling frames and non-respondents, implications for sample design like sample size and reducing error, steps in the sampling design process, and characteristics of a good sample design such as being representative and minimizing bias and error.
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This document outlines a pilot study prepared by Mrs. Deva Pon Pushpam.I on the topic of pilot studies. It defines a pilot study as a small preliminary version of a larger study used to test feasibility and refine methods and procedures. The purposes of a pilot study are to determine feasibility, identify weaknesses, test instruments and data collection, obtain preliminary outcome data to calculate sample size, and refine the methodology. A pilot study uses similar design and methods to the main study, including control groups, blinding, and outcome measures. Data collection and analysis in a pilot study aim to test instruments and address study objectives through descriptive statistics. The conclusion is that pilot studies avoid costly failures in main studies by testing procedures beforehand.
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This document discusses various sampling designs and their characteristics. It describes probability sampling designs like simple random sampling which gives every unit an equal chance of selection. It also describes non-probability sampling designs like purposive sampling which involves deliberately choosing units. Specific probability designs discussed include systematic sampling, stratified sampling, cluster sampling, area sampling, and multi-stage sampling.
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Cluster sampling refers to a method where the population is divided into groups called clusters. A simple random sample of these clusters is selected, and then all or a subset of elements within the selected clusters are included in the final sample. It is cheaper than simple random sampling but has a higher chance of sampling error. The key aspects are that the population is divided into clusters, a random sample of clusters is taken, and then data is collected from elements within those clusters.
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This document discusses audit sampling methods. It defines audit sampling as selecting a subset of a population for the purpose of making inferences about the whole population. Audit sampling helps auditors efficiently gather evidence and detect errors or misstatements. The document discusses factors that affect sample size, different sampling methods like simple random sampling and stratified random sampling, and the purposes of test of controls and substantive testing. It also compares statistical and non-statistical sampling methods.
Sampling
The document discusses sampling, processes, errors, and techniques used in censuses. It defines sampling as selecting individuals to represent a larger group, and lists types of sampling as random and non-random. Random sampling includes simple random, systematic random, and stratified random. The sampling process is defined as determining the population, frame, units, method, size, and selection. Sampling error is caused by taking a sample instead of a census, while non-sampling errors occur in planning and execution. Techniques discussed include taking a census of the entire population to gather generalized data about it.
Quality Journey --Sampling Process.pdf
Introduction to Sampling
When to sample
Representative sample When to sample
How to guarantee a representative sample
Random , Systematic , Stratified , Clustered
Sampling Method
When to use Stratified Sampling
Sampling Bias/ Avoid Sampling Bias
The cost and ease of obtaining samples
Time constraints
Unknown characteristics of the population
Common Segmentation Factors - Common Segmentation Factors
What type - When - Where - Who
How Do I Determine Sample Size
Level of confidence
Precision or accuracy (∆)
Standard deviation of the population (σ), “How much variation is in the total data population”
An estimate of standard deviation is needed to start. As standard deviation increases, a larger sample size is needed to obtain reliable results
Sample Size For Continuous Data
Consider the following example:
We want to estimate average call length in handling customer inquiries, and we want our estimate to be accurate to within 1 minute. Based on a small random sample of 30 inquiries we know that the variation in call length, as measured by standard deviation, is 5 minutes. We want to have 95% confidence that the estimate will be in the range of specified accuracy – i.e., 1 minute.
Therefore, from the statistical theory we can answer according to the formula
Where n = sample size, u = standard deviation and ∆= degree of precision required. In our example, the required sample size is:
n = [(1.96*5)/1] 2 = 96.04 or 96 samples
Extending the same logic, we can find out the sample size required while dealing with discrete population
If the average population proportion non-defective is at ‘p’, population standard deviation can be calculated as
Sampling is the process of:
Collecting only a portion of the data that is available or could be available & drawing conclusions about the total population (statistical inference )
Audit sampling help auditors on doing their audit work at given period time
Sampling provides a good alternative to collect data in an effective and efficient manner
Sampling is the process of collecting a portion or subset of the total data that may be available.
All of the data available is often referred to as a Population (N).
The purpose of sampling is to draw conclusions about the population using the sample (n). This is know as statistical inference.
One of the first questions to ask is ‘Do I need to sample?” The major reason sampling is done is for efficiency reasons-it is often too costly or time consuming to measure all of the data. Sampling provides a good alternative to collect data in an effective and efficient manner. If the circumstances surrounding the data collection plan do not justify sampling, then sampling should not be done. This is often the case in low volume processes.
All items in the population have an equal chance of being chosen in the sample
Example: A customer satisfaction survey team picking the customers to be contacted at random
How to do random sampling
Generate random numbers from
SAMPLING.pptx
Sampling Techniques – The nature of sampling, Probability sampling design, Non-probability sampling design, Determination of sample size
Sampling and sample size determination
This document discusses sampling and sample size determination. It defines key terms like population, population element, census, and provides reasons for using a sample such as cost, speed, accuracy, and avoiding destruction of test units. It outlines the steps in sampling which include defining the target population, selecting a sampling frame, choosing between probability or non-probability sampling, determining the sampling unit, and addressing sources of error. It then discusses different probability and non-probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, convenience sampling, judgment sampling, quota sampling, and snowball sampling. Factors to consider when determining sample size are discussed such as population variance or heterogeneity, acceptable error or confidence interval, confidence level.
sampling.pptx
A sample is a subset of a larger population that is used to gather information about and make inferences regarding the whole population. It should accurately represent the population's characteristics. Samples allow for studies to be conducted more easily and quickly than examining entire populations. There are different sampling methods, including random sampling which gives everyone an equal chance of being selected, stratified sampling which divides the population into groups, and cluster sampling which selects groups rather than individuals. Non-probability sampling methods like convenience sampling rely on availability rather than random selection.
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This document discusses a survey conducted by Gallup Organization in October 1997 that asked US adults about the most urgent health problem facing the country. The survey found that 29% of respondents considered AIDS the top issue, with a margin of error of +/- 3%. This result was based on telephone interviews with 872 adults. It then examines concepts from survey sampling, such as sample size and how it relates to precision. It explores how statistics can be used to calculate a confidence interval around the point estimate from a sample and issues to consider in survey design, sampling, and analysis.
Sampling
This document discusses sampling, including the reasons for sampling, types of sampling methods, and key steps in the sampling process. Some of the main points covered include:
- Sampling is done when covering the entire population is impossible due to constraints like time, money or the population being infinite. It allows for quicker, more cost-effective and accurate analysis.
- The main types of sampling methods are probability sampling (e.g. simple random, stratified) and non-probability sampling (e.g. convenience, purposive). Probability methods allow each unit to be selected randomly and for estimating sampling error.
- Key steps involve defining the population and sample, determining sample size, choosing a sampling design and method, and
Census and sampling
Census collects data from the entire population, while sampling collects data from only a subset of the population. Sampling provides results more quickly and at lower cost but with less accuracy than a census. Probability sampling methods like simple random sampling, systematic sampling, and stratified sampling allow determining the probability of selecting samples, while non-probability methods do not. Both methods have advantages and disadvantages related to accuracy, costs, and ability to generalize to the total population. Errors can occur from how samples are selected and from other issues in data collection and processing.
Session 3 sample design
The document discusses sample design and data collection for impact evaluations, covering topics like sampling techniques, sample size calculations, developing a data collection plan, training enumerators, and pilot testing instruments. It provides an example of using a stratified multi-stage sampling design with 7,000 households to evaluate the socioeconomic impact of a water program. The goal of impact evaluations is to allow for the estimation and hypothesis testing of program impacts through rigorous evaluation and sample designs.
sampling
This document defines key terms related to sampling design and methods. It discusses the differences between populations, target populations, and accessible populations. It also defines samples, sampling frames, strata, random selection, and representativeness. The document outlines several probability sampling methods, including simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It also discusses non-probability sampling methods like convenience sampling, purposive sampling, and quota sampling.
Sampling
This document discusses different sampling methods and terms. It defines key concepts like population, sample, element, and provides characteristics of a good sample. It outlines advantages and limitations of sampling and categorizes sampling methods into probability and non-probability. Specific probability methods like simple random sampling, stratified sampling, and systematic sampling are described in detail along with their procedures and advantages/disadvantages. Non-probability methods like quota sampling, accidental sampling and judgmental sampling are also briefly introduced.
CH 3 Sampling (3).pptx.ppt
This document discusses different sampling techniques and determining sample size. It begins by outlining the objectives of learning about sampling methods, distinguishing between probability and non-probability sampling, and calculating sample sizes using appropriate formulas. The document then defines key sampling terms and discusses the reasons for sampling, including that it is less costly, more accessible, and useful than surveying an entire population. It describes different sampling methods like simple random sampling, systematic sampling, and stratified random sampling. It emphasizes that probability sampling allows results to be generalized to the overall population.
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Sample design
- 1. Sampling Design A. Thangamani ramalingam PT, MSc(psy),PGDRM, ACspss MIAP
- 2. Probability sampling Non probability sampling Different types of sample design
- 3. Statistics is the science of dealing with numbers
- 7. Criteria for selecting sampling procedure Inappropriate sampling frame Defective measuring device Non-respondents Indeterminancy principle Natural bias in the reporting of data while selecting a sampling procedure, researcher must ensure that the procedure causes a relatively small sampling error and helps to control the systematic bias in a better way.
- 8. Implications for sample design Sample size Selection of sample Less error Save time and money
- 9. steps in sampling design Type of universe Sampling unit sampling frame(source list) Sample size Parameters of interest Financial constraints Procedure of sampling
- 10. characteristics of good sample design A truly representative sample Small sampling error No systematic bias Less fund used Generalizability of results