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SOC221H5
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Lingqin Feng
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Chapter 7

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Sociology

SOC221H5

Lingqin Feng

Fall

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Chapter7: Qualitative and Quantitative sampling
Introduction
- Quantitative style researchers’ primary goal is to get a representative sample or a small
collection of units or cases from a much larger collection or population
They send to use sampling based on theories of probability from mathematics( called
probability sampling)
2 motives for using probability or random sampling : Saving time and cost , accuracy
Census: Attempt to count everyone in the target population
- Qualitative researchers focus on how the sample or small collection of cases, unites or activities
illuminates
Sampling by collect cases, events or actions that clarify and deepen understanding
Concern to find cases that will enhance what the researchers learn about the process of
social life in a specific context
Second type of sample: non probability sampling
Non-Probability Sampling
- Used by qualitative researchers
- Rarely determine the sample size in advance and have limited knowledge about the larger group
or population from which the sample is taken
- Selects cases gradually with special content of a case determining whether it is chosen
- Some techniques are….
Haphazard, Accidental or convenience sampling: researcher collects anyone that they
happen to come across. Is not recommended. Can get a sample that misrepresents the
population. They are cheap and quick but the systematic errors that easily occur make
them worse than no sample at all. Ex... Person on the street interviews.
Quota sampling: the researcher identifies relevant categories of people ( male
Or female, above 30 or under etc.). Then decide how many in each category and thus
the number of people in various categories of the sample is fixed. Researcher can
ensure that some difference are in the samples , getting a more better representation of
the population
Purposive sampling: expert uses judgment in selecting cases with specific purpose in
mind. Researcher never knows whether the cases selected represent the population-
used in exploratory research or in field research. Appropriate in 3 situations…
1) Uses it to select unique cases that are especially informative
2) To select member of a difficult to reach, socialized population.
3) Researcher wants to identify particular type of cases for in depth investigation. Less
to generalize to a larger population than it is to gain a deeper understanding of types.
o A special case of purposive sampling is known as deviant case sampling in which
researcher seeks cases that differ from the dominant pattern or that differ from
the predominant characteristics of other cases. Goal is to locate collection of unusual cases that are not representative of the whole. Deviant cases selected
inn hopes of learning about the social life by considering cases that fall inside
the general pattern
Snowball sampling: aka network, chain referral or reputational sampling. Is method for
identifying and sampling selecting cases in a network. Based on an analogy to a
snowball. Begins small but then becomes larger as it rolled on wet snow and pick up
additional snow. Similarly this begins with few people or cases and spreads out on the
basis of links to the initial cases. Used to sample a network. Represent network by a
sociogram (a diagram if circles connected with lines. ( shows direct and indirect
connections between the two)( . The circles represent each person or case and the line
represent friendship or other linkages. Also use it in combination with purposive
sampling
Sequential sampling: researcher continues together cases until the amount of new
information of diversity of cases is filled. Requires researcher to continually evaluate all
the collected cases. Related to concept of theoretical sampling which is tied to
grounded theory. Researchers who use grounded theory employ theoretical sampling
which means they continue to collect data until no new information emerges which is
referred to as theoretical saturation
Probability Sampling
-Population, elements, and sampling frames
Element: unit of analysis or case in a population- can be person, group, etc…
Population: the large pool is the population which is important in sampling. Refers to the unit
being samples, locating and boundaries of population.
Target population: the specific poo; of cases that they want to study.
Sampling ratio: the ration of the size of the sample of the sample to the size of the target
population is the ration. Ex… Population is 50 000 people and a sample of 150 is drawn. Thus
sampling ration is 150/50 000+0.003 or 0.3%.
Population is an abstract concept- cannot truly freeze the population to measure it. It’s always
changing thus researcher needs to estimate the population. Must be an operational definition.
Does this by developing a specific list that closely approximates all the elements in the
population. This list is a sampling frame.
Sampling frame: A list of cases in a population or the best approximation of it. Ex… Want to see
population of Canada thus looks at everyone’s driving license (sampling frame). But it may
include some of those out the target population and omit those inside of it thus its almost
always inaccurate.
Any characteristic of a population (ex… % of city residents who smoke, height of women over
21) is a population parameter. The true characteristic of the population. Determined when all
elements in a population are measured. Never absolute for large populations, must estimate on basis of samples by using information from the sample called statistics to estimate the
population parameter.
Why random?
- Probability theory relies on random process
- Random refers to a process that generates a mathematically random result. The selection
process operates in random method and researcher can calculate the probability of outcomes.
- Each elements have an equal chance of being selected
- Researchers must identify specific elements to include in the sample
- Most likely to yield results that truly represents the population.
- Can measure sampling error: How much a sample deviates from being representative of the
population. Related to idea called margin of error.
- Margin of error: estimates of amount of error that exist in a surveys results
- Random sample: Based on sophisticated mathematics. Uses mathematical random process so
that each sampling element in the population has an equal chance of being selected.
Types of probability samples
-Simple random:
Researcher develops accurate sampling frame, selects elements from the sampling frame
according to a mathematically random procedure then locates the exact element that was
selected for inclusion in the sample.
After numbering all elements in a sampling frame, a researcher uses a list of random numbers
to

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