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Chapter 5

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University of Toronto Scarborough

Psychology

PSYB01H3

Nussbaum D

Fall

Description

Chapter 5
Sample: a group of units selected from a larger group that is known as the population
Survey research: research in which information is obtained from a sample of individuals
through their responses to questions about themselves or others
Selecting Research Participants
You can study an entire population of interest by conducting a census: research in
which information is obtained through responses that all available members of an entire
population give to questions
Unfortunately, this is very time consuming and takes a lot of effort
Sample Planning
Define the Population
In behavioural research, it has been found that:
Students, disabled persons, the elderly, and adult samples tend to report similar
(not identical) levels of happiness.
The same is true of men and women, as well as white and African Americans
However, in countries around the world, happiness levels are very different.
Satisfaction with life is also different across cultures.
This means cannot generalize happiness finding from population to population.
Cross-population generalizability: exists when findings about one group, population, or
setting hold true for other groups, populations, or settings
Also called external validity
Define Sample Components
Population: entire set of individuals or other entities to which study findings are to be
generalized
Elements: the individual members of the population whose characteristics are to be
measured
Sampling frame: a list of all elements in a population
Representative sample: a sample that “looks like” the population from which it was
selected in all respects potentially relevant to the study.
In an unrepresentative sample, some characteristics are overrepresented or
underrepresentative. Random selection of elements maximizes sample
representativeness. Sample representativeness depends on the amount of sampling error: the difference
between the characteristics of a sample and the characteristics of the population from
which it was selected.
The larger the sampling error, the less representative the sample and the less
generalizable the findings obtained from that sample.
Estimating Sampling Error
Inferential statistics: mathematical tool for estimating how likely it is that a statistical
result based on data from a random sample is representative of the population from
which the sample is assumed to have been selected.
Sampling distribution: hypothetical distribution of a statistic across all the random
samples that could be drawn from a population
For many statistics, including the mean, the graph has a „normal shape‟
Random sampling error: differences between the population and the sample that are
due only to chance factors (random error) not to systematic sampling error.
May or may not result in an unrepresentative sample
Sample statistic: the value of a statistic, such as a mean, computed from sample data
Population parameter: the value of a statistic, such as a mean, computed using the data
for the entire population; a sample statistic is an estimate of a population parameter
In a normal distribution, a predictable proportion of cases falls within certain ranges.
Inferential statistics takes advantages of this feature and allows researchers to estimate
how likely it is that, given a particular sample, the true population value will be within
some range of statistics.
Sampling methods
Probability sampling methods: allow us to know in advance how likely it is that any
element of a population will be selected for the sample
Nonprobability sampling methods: do not allow us to know in advance the likelihood of
selecting each element
Probability of selection: the likelihood that an element will be selected from the
population for inclusion in the sample
In a census of all elements of a population, the probability that any particular
element will be selected is 1.0. If half of the elements in the population are
sampled on the basis of chance, the probability of selection for each element is
one half, or 0.5. As the size of the sample decreases as a proportion of the
population, so does the probability of selection. Random sampling (cases are selected only on the basis of chance) is not totally
haphazard. Researchers actually have to be very methodical to ensure a completely
random selection.
Probability Sampling Methods
Probability sampling methods are those in which the probability of selection is known
and is not zero (to make sure there is some chance of selecting element). These
methods randomly select elements, meaning that they have no systematic bias (),
nothing but chance determines which elements are selected for the study.
This makes them much more desirable than nonprobability samples when the
goal is to generalize to a larger population.
Simple Random Sampling
Def.: every sample element is selected only on the basis of chance, through a random
process
Requires some procedure that generates numbers or otherwise identifies cases
strictly on the basis of chance
o One type of procedure is random-digit dialing: random dialing by a
machine of numbers within designated phone prefixes, creating a random
sample
Systematic Random Sampling
Def.: Variant of simple random sampling. The first element is selected randomly from a
list or from sequential files, and then every nth element is selected.
Have to be cautious of periodicity: the sequence varies in some regular, periodic
pattern.
If the sampling interval (number of cases from one sampled case to another) is a
number, the same as the periodic pattern, all the cases selected will be in the
same position.
o In reality, periodicity and the sampling interval are rarely the same.
Stratified Random Sampling
Def.: uses information known about the total population prior to sampling to make the
sampling process more efficient.
All elements in the population (i.e. the sampling frame) are distinguished
according to their value on some relevant characteristic. That characteristic forms
the sampling strata. Then the elements are sampled randomly from within these
strata. Using this method requires more information prior to sampling than is the case with
simple random sampling. All of the elements must be able to be categorized in only one
stratum so the size of each stratum must be known.
This method is more efficient than drawing a simple random sample because it ensures
appropriate representation of elements across strata. It is commonly used in national
surveys.
Proportionate stratified sampling: ensures that the sample is selected so that the
distribution of characteristics in the sample matches the distribution of the
corresponding characteristics in the population.
Disproportionate stratified sampling: sampling in which elements are selected from
strata in different proportions from those that appear in the population
Cluster Sampling
Def.: sampling in which elements are selected in two or more stages
Useful when a sampling frame of elements is not available, as often is the case
for large populations across a wide geographic area.
Cluster: naturally occurring, mixed aggregate of elements of the population, with each
element appearing in one and only one cluster.
Schools can serve as clusters for sampling students
Drawing a cluster sample is two stepped:
1. The researcher draws a random sample of naturally occurring clusters. A list of
clusters should be much easier to obtain than a list of all individuals in each
cluster in the population.
2. The researcher draws a random sample of elements from each selected cluster
a. This is a fraction of the total clusters are involved so getting a sampling
frame should be easier
Nonprobability Sampling Methods
Availability sampling: sampling in which elements are selected on the basis of
convenience
Also known as haphazard, accidental, or convenience sampling
Quota Sampling
Def.: quotas are set to ensure that the sample re

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