The groups must be mutually exclusive and cover the population. Stratified sampling provides greater precision than a simple random sample of the same size. Cluster Sampling Cluster sampling is generally used to control costs and when it is geographically impossible to undertake a simple random sample. For example, in a household survey with face-to-face interviews, it is difficult and expensive to survey households across the nation using a simple random sample design. Instead, researchers will randomly select geographic areas for example, counties , then randomly select households within these areas.
This creates a cluster sample, in which respondents are clustered together geographically. Survey research studies often use a combination of these probability methods to select their samples.
Multistage sampling is a probability sampling technique where sampling is carried out in several stages. It is often used to select samples when a single frame is not available to select members for a study sample. For example, there is no single list of all children enrolled in public school kindergartens across the U. Therefore, researchers who need a sample of kindergarten children will first select a sample of schools with kindergarten programs from a school frame e. Lists of all kindergarten classrooms in selected schools are developed and a sample of classrooms selected in each of the sampled schools Stage 2.
Finally, lists of children in the sampled classrooms are compiled and a sample of children is selected from each of the classroom lists Stage 3. Many of the national surveys of child care and early education e. Multistage, cluster and stratified sampling require that certain adjustments be made during the statistical analysis.
Sampling or analysis weights are often used to account for differences in the probability of selection into the sample as well as for other factors e. Standard errors are calculated using methodologies that are different from those used for a simple random sample. The extent to which estimates of the population mean, proportion and other population values differ from the true values of these is affected by these errors.outer-edge-design.com/components/work/3271-galaxy-note.php
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Sampling error is the error that occurs because all members of the population are not sampled and measured. The value of a statistic e. For example, if several different samples of 5, people are drawn at random from the U. In one sample, Bill Gates may have been selected at random from the population, which would lead to a very high mean income for that sample.
Researchers use a statistic called the standard error to measure the extent to which estimated statistics percentages, means, and coefficients vary from what would be found in other samples. The smaller the standard error, the more precise are the estimates from the sample. Generally, standard errors and sample size are negatively related, that is, larger samples have smaller standard errors.
Nonsampling error includes all errors that can affect the accuracy of research findings other than errors associated with selecting the sample sampling error. They can occur in any phase of a research study planning and design, data collection, or data processing. They include errors that occur due to coverage error when units in the target population are missing from the sampling frame , nonresponse to surveys nonresponse error , measurement errors due to interviewer or respondent behavior, errors introduced by how survey questions were worded or by how data were collected e.
While sampling error is limited to sample surveys, nonsampling error can occur in all surveys. Measurement Error Measurement error is the difference between the value measured in a survey or on a test and the true value in the population. Some factors that contribute to measurement error include the environment in which a survey or test is administered e.
- Seminar on Formulation of Data Gathering Tools.
- Understanding and Evaluating Survey Research.
- Data Collection.
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Measurement error falls into two broad categories: systematic error and random error. Systematic error is the more serious of the two. Systematic error Occurs when the survey responses are systematically different from the target population responses. It is caused by factors that systematically affect the measurement of a variable across the sample.
For example, if a researcher only surveyed individuals who answered their phone between 9 and 5, Monday through Friday, the survey results would be biased toward individuals who are available to answer the phone during those hours e. It can include both nonobservational and observational error. Nonobservational error -- Error introduced when individuals in the target population are systematically excluded from the sample, such as in the example above.
Observational error -- Error introduced when respondents systematically answer survey question incorrectly. For example, surveys that ask respondents how much they weigh may underestimate the population's weight because some respondents are likely to report their weight as less than it actually is. Systematic errors tend to have an effect on responses and scores that is consistently in one direction positive or negative. As a result, they contribute to bias in estimates. Random error Random error is an expected part of survey research, and statistical techniques are designed to account for this sort of measurement error.
It is caused by factors that randomly affect measurement of the variable across the sample. Random error occurs because of natural and uncontrollable variations in the survey process, i. For example, a researcher may administer a survey about marital happiness. However, some respondents may have had a fight with their spouse the evening prior to the survey, while other respondents' spouses may have cooked the respondent's favorite meal. The survey responses will be affected by the random day on which the respondents were chosen to participate in the study.
With random error, the positive and negative influences on the survey measures are expected to balance out.
Unlike systematic errors, random errors do not have a consistent positive or negative effect on measurement. Instead, across the sample the effects are both positive and negative. Such errors are often considered noise and add variability, though not bias, to the data. Resources See the following for additional information about the different types and sources of errors: Nonresponse Error, Measurement Error and Mode of Data Collection Total Survey Error: Design, Implementation, and Evaluation Data Accuracy Administrative Data Administrative data are an important source of information for social science research.
For example, school records have been used to track trends in student academic performance.
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Administrative data generally refers to data collected as part of the management and operations of a publicly funded program or service. Today, use of administrative data is becoming increasingly common in research about child care and early education. These data often are a relatively cost-effective way to learn more about the individuals and families using a particular service or participating in a particular program, but they do have some important limitations. The advantages and disadvantages of using administrative data are described here.
Issues pertaining to the access to such data are discussed.
Terms relating to administrative data and its use in research studies are defined in the Research Glossary. Advantages of Administrative Data Data Limitations Obtaining and Learning About Administrative Data Sampling Advantages of Administrative Data Administrative data make possible analyses at the state and local levels that are rarely possible using national survey data.
Such data often contain detailed, accurate measures of participation in various social programs. They typically include large numbers of cases, making possible many different types of analyses. Potential for linking data from several programs in order to get a more complete picture of individuals and the services received. At the state level, such data provide effective ways for assessing state-specific programs and can be useful for several forms of program evaluation. The large sample sizes allow small program effects to be more easily detected, and permit effects to be estimated for different groups.
It is less expensive to obtain administrative data than to collect data directly on the same group. Limitations of Administrative Data Administrative data are collected to manage services and comply with government reporting regulations.
Because the original purpose of the data is not research, this presents several challenges. The administrative data only describe the individuals or families using a service and provide no information about similar people who do not use the service. The potential observation period for any subject being studied e. Generally, only those services that are publicly funded are included in the administrative data. For example, a researcher cannot rely on subsidy data to learn about all child care providers in the state or on non-subsidized forms of child care being used to augment child care that is subsidized.
Many variables used in administrative data are not updated regularly, so it is important to learn how and when each variable is collected. For instance, an "earnings" variable in administrative data for subsidized child care generally is entered at the time that eligibility is determined and then updated when eligibility is redetermined.
When this is the case, there is no way to know, using administrative data alone, what a family earns in the months between eligibility determination and redetermination. Important variables needed for a particular research study may not be collected in administrative data. Because the data are limited to data on program participants, information on those eligible for the program but who are not enrolled is often not available.