Ch. 5 Selecting Research Participants

principal methods of random sampling

.Sampling with replacement .Sampling without replacement

Target population

A group defined by a researcher's specific interest. Individuals in a target population typically share one characteristic

nonprobability sampling

A method of sampling in which the population is not completely known, individual probabilities cannot be known, and the selection is based on factors such as common sense or ease with an effort to maintain representativeness and avoid bias.

law of large numbers

In the field of statistics, the principle that states that the larger the sample size, the more likely it is that values obtained from the sample are similar to the actual values for the population.

convenience sampling

A nonprobability sampling method involving selection of individuals on the basis of their availability and willingness to respond; that is, because they are easy to get. Occasionally called accidental sampling or haphazard sampling.Convenience sampling is considered a weak form of sampling because the researcher makes no attempt to know the population or to use a random process in selection. Ex: asking random people if they want to be apart of research

quota sampling

A nonprobability sampling method; a type of convenience sampling involving identifying specific subgroups to be included in the sample and then establishing quotas for individuals to be sampled from each group. Ex:in a sample of 30 preschool children by establishing quotas for the number of individuals to be selected from each subgroup. Rather than simply taking the first 30 children, regardless of gender, who agree to participate, you impose a quota of 15 girls and 15 boys. After the quota of 15 boys is met, no other boys have a chance to participate in the study.

Systematic sampling

A probability sampling technique in which a sample is obtained by selecting every nth participant from a list containing the total population after a random starting point.The size of n is calculated by dividing the population size by the desired sample size. Ex:With a population of 100 children and a desired sample size of 25, the size of n in this example is . Therefore, every fourth individual after participant

simple random sampling

A probability sampling technique in which each individual in the population has an equal and independent chance of selection. The obvious goal of a simple random sample is to ensure that the selection procedure cannot discriminate among individuals and thereby result in a nonrepresentative sample. The process of simple random sampling consists of the following steps: 1.Clearly define the population from which you want to select a sample. 2.List all the members of the population. 3.Use a random process to select individuals from the list.

cluster sampling

A probability sampling technique involving random selection of groups instead of individuals from a population. Ex: Instead of selecting 300 students one at a time, the researcher can randomly select 10 classrooms (each with about 30 students) and still end up with 300 individuals in the sample.

stratified random sampling

A probability sampling technique that involves identifying specific subgroups to be included in the sample and then selecting equal-sized random samples from each pre-identified subgroup. The main advantage of a stratified random sample is that it guarantees that each of the different subgroups will be well represented with a relatively large group of individuals in the sample. Ex: majors% of college-size .psycology 10% -10 .english-45%-45 .biology-30%-30 .math-15%-15

proportionate stratified random sampling

A probability sampling technique that involves identifying specific subgroups to be included, determining what proportion of the population corresponds to each subgroup, and randomly selecting individuals so that the proportion for each subgroup in the sample exactly matches the corresponding proportion in the population. Also known as proportionate random sampling. Ex: majors-size .psycology -25 .english-25 .biology-25 .math-25

random process

A procedure that produces one outcome from a set of possible outcomes. The outcome must be unpredictable each time, and the process must guarantee that each of the possible outcomes is equally likely to occur.

biased sample

A sample with characteristics different from those of the population. If, for example, the individuals in a sample are smarter (or older or faster) than the individuals in the population, then the sample is biased.

probability sampling

A sampling method in which the entire population is known, each individual in the population has a specifiable probability of selection, and sampling is done using a random process based on the probabilities. Probability sampling has three important conditions: 1.The exact size of the population must be known and it must be possible to list all of the individuals. 2.Each individual in the population must have a specified probability of selection. 3..When a group of individuals are all assigned the same probability, the selection process must be unbiased so that all group members have an equal chance of being selected. Selection must be a random process, which simply means that every possible outcome is equally likely. For example, each time you toss a coin, the two possible outcomes (heads and tails) are equally likely.

sample

A set of individuals selected from a population, usually intended to represent the population in a research study.

Sampling without replacement

As the term indicates, this method removes each selected individual from the population before the next selection is made. Although the probability of being selected changes with each selection, this method guarantees that no individual appears more than once in a single sample. Because the probabilities change with each selection, this technique does not produce independent selections; the probability that you will be selected increases each time another person is selected and removed from the population

Combined-Strategy Sampling

Occasionally, researchers combine two or more sampling strategies to select participants. For example, a superintendent of schools may first divide his district into regions (e.g., north, south, east, and west), which involves stratified sampling. From the different regions, the superintendent may then select two third-grade classrooms, which involves cluster sampling. Selection strategies are commonly combined to optimize the chances that a sample is representative of a widely dispersed or broad-based population such as in a wide market survey or a political poll.

accessible population

The easily available segment of a target population. Researchers typically select their samples from this type of population.

Population

The entire set of individuals of interest to a researcher. Although the entire population usually does not participate in a research study, the results from the study will be generalized to the entire population. Also known as target population.

representativeness

The extent to which the characteristics of the sample accurately reflect the characteristics of the population.

sampling

The process of selecting individuals to participate in a research study.

sampling methods

The variety of ways of selecting individuals to participate in a research study. Also known as sampling techniques or sampling procedures.

Sampling with replacement

This method requires that an individual selected for the sample be recorded as a sample member and then returned to the population (replaced) before the next selection is made. This procedure ensures that the probability of selection remains constant throughout a series of selections. For example, if we select from a population of 100 individuals, the probability of selecting any particular individual is 1/100. To keep this same probability (1/100) for the second selection, it is necessary to return the first individual to the pool before the next is selected. Because the probabilities stay constant, this technique ensures that the selections are independent.

selection bias

When participants or subjects are selected in a manner that increases the probability of obtaining a biased sample. A threat to external validity that occurs when the selection process produces a sample with characteristics that are different from those in the population. Also known as sampling bias.