# Chapter Summaries - Test 2

Chapter 7 – Logic of Sampling

Nonprobability Sampling

Social research is often conducted in situations that do not permit the kinds of probability samples used in

large-scale social surveys (i.e. homelessness)

Although appropriate to some research purposes (qualitative research), nonprobability sampling methods

cannot guarantee that the sample being observed is representative of the whole population

There are four types of nonprobability sampling:

-Reliance on Available Subjects: i.e. stopping people at a street corner or some other location

-Purposive or Judgemental Sampling: sometimes it’s appropriate to select a sample on the basis of

knowledge of a population, its elements, and the purpose of the study

-Snowball Sampling: whereby each person interviewed may be asked to suggest additional people

for interviewing

-Quota Sampling: units are selected into a sample on the basis of prespecified characteristics, so

that the total sample will have the same distribution of characteristics assumed to exist in the

population being studied

Probability Sampling

EPSEM (equal probability of selection method) – a sample design in which each member of a population

has the same chance of being selected into the sample

Probability sampling does offer two advantages:

-Typically more representative than other types of sample because biases are avoided

-Probability theory permits us to estimate the accuracy or representativeness of the sample

Element – that unit of which a population is composed and which is selected in a sample

Parameter – the summary description of a given variable in a population

Statistic – the summary description of a variable in a sample, used to estimate a population parameter

Sampling error – the degree of error to be expected in probability sampling

Confidence level – the estimated probability that a population parameter lies within a given confidence

interval (i.e. we are 95% confident that between 35%-45% of all kids like smarties)

Confidence interval – the range of values within which a population parameter is estimated to lie

There are four types of probability sampling:

-Systematic sampling: involves the selection of every kth member from a sampling frame; this

method is more practical than simple random sampling and is almost functionally equivalent

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-Stratification: the process of grouping the members of a population into relatively homogeneous

strata before sampling, improves the representativeness of a sample by reducing the degree of

sampling error

-Multistage Cluster Sampling: complex but is frequently used when a list of all the members of a

population does not exist. Typically, researchers must balance the number of clusters and the size

of each cluster to achieve a given sample size. Stratification can be used to reduce the sampling

error involved in multistage cluster sampling

oProbability proportionate to size (PPS) is a special efficient method for multistage cluster

sampling

oIf the members of a population have unequal probabilities of selection into the sample,

researchers must assign weights to the different observations made, in order to provide a

representative picture of the total population. The weight assigned to a particular sample

member should be the inverse of its probability of selection

Ethics of Sampling

Probability sampling always carries a risk of error; researchers must inform readers of any errors that

might make results misleading

When nonprobability sampling methods are used to obtain the breadth of variations in a population,

researchers must take care not to mislead readers into confusing variations with what’s typical in the

population

Chapter 8 – Experiments

The Classical Experiment

-Tests the effect of an experimental stimulus (independent variable) on a dependent variable

through the pretesting and posttesting of experimental and control groups

oPretesting: the measurement of a dependent variable among subjects before they are

exposed to a stimulus represent an independent variable

oPosttesting: the remeasurement of a dependent variable among subjects after they’ve been

exposed to a stimulus representing an independent variable

- It is generally less important that a group of experimental subjects be representative of some

larger population than that experimental and control groups be similar to each other

oExperimental group: a group of subjects to whom an experimental stimulus is

administered

oControl group: a group of subjects to whom no experimental stimulus is administered

and who resemble the experimental group in all other respects

-A double-blind experiment guards against experimenter bias because neither the experimenter nor

the subject knows which subjects are in control and experimental groups

Selecting Subjects

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