Child Care and Early Education Research Connections

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The research glossary defines terms used in conducting social science and policy research, for example those describing methods, measurements, statistical procedures, and other aspects of research; the child care glossary defines terms used to describe aspects of child care and early education practice and policy.

A group that is selected from a larger group (the population). By studying the sample the researcher tries to draw valid conclusions about the population.
Sample Size
The number of subjects in a study. Larger samples are preferable to smaller samples, all else being equal.
The process of selecting a subgroup of a population (i.e. sample) that will be used to represent the entire population.
Sampling Bias
Distortions that occur when some members of a population are systematically excluded from the sample selection process. For example, if interviews are conducted over the phone, only individuals with telephones will be in the sample. This could produce bias if the researcher intends to draw conclusions about the entire population, including those with a phone and those without a phone.
Sampling Design (Sample Design)
The part of the research plan that specifies the method of selection and the number of individuals or organizations (schools, programs) who will be selected and asked to participate in the study. The sampling design (sample design) specifies the target population, the frame or list from which cases from that population will be selected, the approach that will be used to select the sample members (simple random sampling, stratified sampling, cluster sampling, or combinations of these), the number of sample units to be selected to achieve the study objectives.
Sampling Distribution
The frequency with which data values appear in the sample. The sampling distribution can be characterized by the mean and the variance of the sample.
Sampling Error
This is the error that occurs because all members of the population are not sampled and measured. The value of a statistic (e.g., mean or percentage) that is calculated from different samples that are drawn from the same population will not always be the same. For example, if several different samples of 5 people are drawn at random from the U.S. population, the average income of the 5 people in those samples will differ. (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.) It is not incorrect to have sampling error, and in fact statistical techniques take into account that sampling error will occur.
Sampling Frame
A list of the entire population eligible to be included within the specific parameters of a research study.
A group of survey questions that measures the same concept. For example, a researcher may be interested in individuals' gender role attitudes, and use several questions to determine their attitudes. This group of questions make up a gender role attitude scale.
Scaled Score
A mathematical transformation of a raw score so that scores can be compared across individuals and over time. The purpose of scaled scores is to report scores for all study participants on a consistent scale.