Textbook Notes
(363,094)

Canada
(158,187)

University of Toronto Scarborough
(18,333)

Psychology
(9,565)

PSYB01H3
(585)

David Nussbaum
(77)

Chapter 5

# PSYB01 Chapter 5.doc

Unlock Document

University of Toronto Scarborough

Psychology

PSYB01H3

David Nussbaum

Fall

Description

Chapter 5: Sampling and Survey Research
← Substantive Theme: Happiness
← Ed Diener’s Satisfaction With Life Style, a widely used measure of happiness
← 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
← When studying happiness, we should be concerned with who we study and when
we study them
• Sampling populations and Census – study the entire population of interest BUT
it’s difficult to account for all people
← Sample Planning
← To plan a sample or assess a sample we must answer 2 questions:
• From what population will you select cases?
• What method will you use to select cases from this population?
← Define the Population
← US population studies found:
• Students, disabled persons, elderly and adults samples report similar levels of
happiness, as do men and women, African Americans and whites.
• Happiness varies in different countries around the world
• Satisfaction with different life domains varies across cultures
• We CAN’T generalize happiness findings from one population in a country to
another
← Cross Population Generalizability – extent to which result of a population can
be applied to another country; tested by comparing results from samples of the different
populations. ← Westerners perceive scenes by focusing on distinctive objects and Asian cultures
view scenes holistically.
← Define Sample Components
← Population – the entire set of individuals of other entities to with 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.
• The distribution of characteristics among the elements of a representative
sample is the same as the distribution of those characteristics among the total
population.
• In an unrepresentative sample, some characteristics are overrepresented or
underrepresented.
• Random selection of elements maximizes sample representativeness.
← Sampling generalizability depends on Sampling Error – difference between
characteristics of sample and that of the population
• The larger the errorless representative of the population
← Estimating Sampling Error:
• Inferential Statistics – tool for calculating sampling error; a 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 – represent all possible samples that we could have
drawn; many are ‘normal’ or in the mean, while others are deviant creating the
normal shape; most frequent value from sample statistic – statistic from the
sample (mean) or population estimate – Is identical to corresponding
population parameter – statistic from the population
o Normal shape created by Random Sampling Error – variation owing
purely to chance; differences between the population and the sample that
are due to chance factors not to systematic sampling error. It may or may
not result in an unrepresentative sample. The magnitude of sampling
error due to these factors can be estimated statistically ← Sampling Methods
← Probability Sampling Methods – allow us to know in advance the likelihood of
selecting each element; use random selection and has no systematic bias = Probability
of selection is known.
← Non-Probability Sampling Methods – sampling methods that don’t let us 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 (by tossing a coin), the
probability of selection for each element is .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; BUT there is
much control in this sampling to ensure it’s only chance that is working on their
sampling.
← Probability Sampling Methods
← Probability sampling methods are those in which the probability of
selection is known and is not zero (so there is some chance of selecting each
element).
← Systematic Bias – having NONE means nothing but chance determines which
elements are included in the sample.
• Good for generalization whereas non-probability samples aren’t good for it.
← Sampling representativeness (or randomness) is more important than the size of
the sample.
← There are 4 methods for drawing random samples:
1. Simple Random Sampling – every sample element is selected only on the basis of
chance, through a random process
a. Generates numbers or identifies cases on the basis
of chance
b. Random-Digit Dialing – a procedure in which a
machine dials random numbers within the phone
prefixes corresponding to the area in which the
survey is to be conducted
c. The probability of selection in a true simple random
sample is equal for each element
d. Eg. flipping a coin or rolling a dice 2. Systematic Random Sampling – sample elements are selected from a list or from
sequential files, with every nth element being selected after the first element is
selected randomly within the first interval
a. Periodicity – sequence varies in some regular, periodic pattern; this is problem
to watch out for Eg. house on corner of every block different
b. Sampling Interval
3. Stratified Random Sampling – sample elements are selected separately from
population strata that are identified in advance by the
researcher
a. All elements are distinguished according to
value on relevant characteristic which forms
sampling strata Eg. race
b. Then elements are sampled randomly from
within strata; ensures right representation Eg.
randomly sampling within a race
c. Stratifies sampling can be either:
i. Proportionate Stratified Sampling –
ensures sample is selected so that the
distribution of characteristic in the
sample matches the population
ii. Disproportionate Stratified Sampling – elements are selected from
strata in different proportions from those that appear in population to study
them
4. Cluster Sampling – useful when sampling frame is not available or for large
populations spread out across a wide area or among many organizations; elements
are selected in two or more stages, with the first stage being the random selection of
naturally occurring clusters and the last stage being the random selection of elements
within clusters
a. Cluster – a naturally occurring, mixed aggregate of elements of the popula

More
Less
Related notes for PSYB01H3