Research Methods CH1, 2, 3 & 4 Exam

Reliance on available subjects

(also called convenience or haphazard sampling) involves using people in close proximity, or people who are gathered in one place at one time. It's considered the weaker forms of sampling. Strengths: easy and efficient Weaknesses: can be unrepresentative of general populations.

Disproportionate Sample and Weight

Depending on the nature of one's population, one may wish to sample a disproportionate amount of one or more elements, or use weighting to sample. Weighting regards assigning different weights to cases that were selected into a sample with different probabilities of selection.

The History of Sampling

1. 1936 Literary Digest presidential poll ( names selected from phone directories and automobile registration list....Sampling frame issue; it only selected the wealthy and did not have an accurate sample representation. 2. 1948 Gallup presidential poll- used a method called quota sampling and selects people to match a set of characteristics (e.g. the poor, those living in rural vs. urban environments). Issues- predictions were based on old data: the quotas were not valid indicators of the U.S. population, and Gallup incorrectly predicted ThomasDewey defeating HarryTruman.

Types of sampling methods

1. Non probability sampling; sampling represents techniques where samples are not selected by using probability theory. 2. Probability sampling; samples selected according to some sort of random assignment.

TWO advantages to probability sampling:

1. Probability samples are typically more representative than other types of samples because biases are avoided. 2. Probability theory permits researchers to estimate the accuracy or representativeness of a sample.

Four types of nonprobability sampling

1. Reliance on available subjects 2. Purposive (or judgmental) sampling 3. Snowball sampling 4. Quota sampling

Types of Sampling Designs

1. Simple random sampling 2. Systematic sampling 3. Stratified sampling

Probability sampling

A general term for samples selected in accord with probability theory. Probability samples are often used for large-scale surveys. Probability sampling can be a very effective tool in research...IF it is done properly. Nonprobability sampling cannot guarantee that a sample is representative of a population. Probability sampling is useful because it helps ensure that the same variations that exist in a population are represented in a sample.

Stratified sampling

A modification of either simple random or systematic sampling. Stratification refers to the grouping of units composing a population into homogenous groups (strata) before sampling. Stratified sampling is slightly more accurate than simple random sampling.

Snowball sampling

A non probability sampling method whereby each person interviewed is asked to suggest additional people for interviewing. Each interview leads to a new subject, and thus one's sample "snowballs" over time, becoming larger and larger. Snowball samples are appropriate for studying difficult to identify or difficult to locate populations (e.g. prostitutes, gang members, drug dealers). Issues: 1. It can result in samples with questionable representativeness. 2. Initial contacts may shape the entire sample and foreclose access to some members of the population of interest.

Probability proportionate to size (PPS) sampling

A type of multistage cluster sample in which clusters are selected not with equal probabilities, but with probabilities proportionate to their sizes—as measured by the number of units to be sub-sampled. PPS is a more sophisticated form of cluster sampling.

Quota sampling

A type of non probability sampling in which units are selected into a sample on the basis of pre-specified characteristics, so that the total sample will have the same distribution of characteristics assumed to exist in the population being studied. Quota sampling begins with a matrix that describes characteristics of a target population. Issues ▪ Proportions that different cells in a matrix represent must be accurate. ▪ It is often difficult to get up-to-date information for this purpose. Selection of sample elements may be biased ▪ E.g. an interviewer may be instructed to interview 5 people who meet a given set of characteristics, yet still avoid certain people that are representative of the issue at hand.

Systematic sampling

A type of probability sampling in which every kth unit in a list is selected for inclusion in a sample. Generally, systematic sampling is more accurate than simple random sampling. 1. Sampling interval; A sampling interval is the standard distance between elements selected from a population in the sample. 2. Sampling ratio; A sampling ratio is the proportion of elements in a population that are selected to be in a sample.

Simple random sampling

A type of probability sampling in which the units composing a population are assigned numbers. A set of random numbers is generated and the units having those numbers are included in the sample. Simple random sampling is beneficial in many ways, but it is not necessarily the most accurate sampling method.

Sampling Error

Any individual sample probably will not be identical to the population. Therefore, sampling error exists when sampling. As sample size increases, sampling (standard) error decreases. As sample size decreases, sampling (standard) error increases.

Stratification in Multistage Cluster Sample

In multistage cluster sampling, stratification techniques can refine and improve a sample being selected. By stratifying a sample in multistage cluster sampling, sampling error can be reduced.

Multistage Culturing Sample...

Natural groups are sampled initially with the members of each selected group being sub-sampled afterward. Cluster sampling is used when it is not practical or possible to create a list of all elements that compose a target population. Cluster sampling is efficient, but less accurate.

Sampling bias

Occurs when subjects selected for a study are not typical nor representative of a larger population. Bias is often unintentional. When one selects a sample based on some background characteristic, they ALWAYS introduce some sort of bias into their sample. Some degrees of bias are more acceptable than others (depending on one's research design).

Probability theory

Provides the basis for estimating parameters of a population. A parameter is the summary description of a given variable in a population. ▪ E.g. the mean unemployment rate or age distribution in L.A. are both parameters of the L.A. population. When researchers generalize from a sample, they use sample statistics to estimate population parameters.

Sampling (Social research rarely addresses a complete population of people)

Researchers sample from a population to discover and understand social phenomena.

Representative and Probability of Selection.

Samples must be representative of the population from which they are selected. Representativeness refers to the quality of a sample of having the same distribution of characteristics as the population from which it was selected. A sample is representative of the population from which it is selected if all members of the population have an equal chance of being selected in the sample.

Purposive/ Judgemental Sampling

Units are selected on the basis of a researcher's judgment about which ones will be most useful or representative. One may want to interview the entire population of some limited group (e.g. directors of shelters for homeless adults). One may want to sample a subset of a population (e.g. mid-level manages with a reputation for efficiency). Guidelines when selecting a purposive sample: 1. Informants should be knowledgeable about the cultural arena, situation, or experience being studied. 2. Informants should be willing to talk. 3. Informants should be representative of the range of points of view. In purposive sampling, interviews should be selected until two criteria are met: 1. Completeness; Interviews should continue until a researcher is confident subjects have provided an overall sense of the meaning of a concept, theme, or process. 2. Saturation; Interviews should continue until one is confident that they are learning little that is new from subsequent interviews