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Chapter 7

# Chapter 7: Qualitative and Quantitative Sampling.docx

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University of Toronto Mississauga

Sociology

SOC221H5

Jayne Baker

Winter

Description

Chapter 7: Qualitative and Quantitative Sampling
Introduction
Quantitative researchers more concerned with sampling; primary goal to get a representative
sample (smaller set of cases a researcher selects from large pool and generalizes to population) and
tend to use sampling based on theories of probability (called probability sampling)
Using probability/random sampling has two motivations: 1. Save time and cost and 2. Accuracy
Census: an attempt to count everyone in a target population (takes place in Canada every 5 years)
Qualitative researchers focus on how the sample or small collection of cases illuminates key
features of social life; purpose of sampling is to collect cases, events or actions that clarify and
deepen understanding
o Focus on finding cases that will enhance what the researchers learn about processes of
social life in specific context and use nonprobability sampling
Nonprobability Sampling
Non-random sample: type of sample in which the sampling elements are selected using something
other than a mathematically random process
Rarely determine sample size in advance and have limited knowledge about large
group/population from which sample is taken
Select cases gradually with specific context of case determining whether it is chosen
Types of Nonprobability samples:
Haphazard Get any cases in any manner that is convenient
Quota Get a pre-set number of cases in each of several predetermined categories
that will reflect diversity of population, using haphazard methods
Purposive Get all possible cases that fit particular criteria, using various methods
Snowball Get cases using referrals from one or few cases, and then referrals from those
cases, and so on
Sequential Get cases until there is no additional information/new characteristics (*often
used with other sampling methods)
Haphazard, Accidental, or Convenience Sampling
Haphazard sampling: a type of non-random sample in which the researcher selects anyone he
happens to come across
Can produce ineffective, unrepresentative samples and not recommended
Cheap and quick but many systematic errors
I.e. person on the street interviews seen on TV
Quota Sampling
Defn: type of non-random sample in which the researcher first identifies general categories into
which cases or people will be selected, then he selects predetermined number of cases in each
category
Researcher can ensure that some differences are in the sample (i.e. age)
Researchers use haphazard sampling once the quota samples fixes the categories and number of
cases in each category
Purposive Sampling
Defn: researcher uses wide range of methods to locate all possible cases of a highly specific and
difficult-to-reach population
Used in situations in which expert uses judgment in selecting cases with specific purpose in mind
Researcher never knows whether the cases selected represent the population
Appropriate in three situations:
1. Researcher uses it to select unique cases that are especially informative
2. To select members of difficult-to-reach, specialized population; i.e. researcher wants to study
prostitutes so he finds different ways to find as many to include in his study as possible (places
where they solicit, social groups they interact with or police who work with prostitutes) 3. When a researcher wants to identify particular types of cases for in-depth investigation; purpose
less to generalize to larger population than to gain deeper understanding of types
Deviant case sampling: type of non-random sample, especially used by qualitative researchers, in
which a researcher selects unusual or nonconforming cases purposely as a way to provide greater
insight into social processes or a setting
o Seek cases that differ from dominant pattern or that differ from predominant characteristics
of other cases
o Goal is to locate collection of unusual, different, or peculiar cases that are not representative
of the whole
o I.e.. Researcher studying high school dropouts
Snowball Sampling
Defn: type of non-random sample in which the researcher begins with one case, then, based on
information about interrelationships form that case, identifies other cases, and then repeats the
process again and again
Also called network, chain referral or reputational sampling
Method of identifying and sampling the cases in a network
Social researchers often interested in interconnected network of people or organizations
Crucial feature is that each person or unit connected with another through direct/indirect linkage
Sociogram: diagram or map that shows the network of social relationships, influence patterns or
communication paths among group of people or units
Also use snowball sampling in combination with purposive sampling as in case of Albanese (2006)
in qualitative study of women in Quebec whose children were in provincial childcare
Sequential Sampling
Defn: type of non-random sample in which a researcher tries to find as many relevant cases as
possible, until time, financial resources, or his energy are exhausted, and there is no new
information or diversity from the cases
Information is gathered until marginal utility, or incremental benefit for additional cases, levels off
or drops significantly
Theoretical sampling: an iterative sampling technique associated with the grounded theory
approach in which the sample size is determined when the data reach theoretical saturation;
continue to collect data until no new information emerges
Theoretical saturation: a term associated with grounded theory approach that refers to the point at
which no new themes emerge from the data and sampling is considered complete
Probability Sampling
Populations, Elements, and Sampling Frames
Researcher draws sample from larger pool of cases, or elements
Sampling element: name for a case or single unit to be selected; unit of analysis in population
o Can be a person, group or organization
Large pool is the population (name for large general group of many cases from which researcher
draws sample and which is usually stated in theoretical terms); can also be called universe
Target population: name for large general group of many cases from which a sample is drawn and
which is specified in very concrete terms; specific pool of cases that he wants to study
Sampling ratio: number of cases in the sample divided by the number of cases in the population or
the sampling frame, or the proportion of the population in the sample; ratio of the size of the
sample to the size of the target population
Population is an abstract concept, cant be frozen at any time to measure it accurately
o Therefore, the researcher needs to estimate the population; researcher operationalizes a
population by developing specific list that closely approximates all the elements in
population
o Sampling frame: list of cases in a population, or the best approximation of it (i.e. telephone
directories, tax records) o Good sampling frame crucial to good sampling
Population parameter: characteristic of the entire population that is estimated from a sample;
determined when all elements in population are measured
o Never known with absolute accuracy for large populations
o Statistic: numerical estimate of population parameter computer from a sample
Why Random?
Probability relies on random processes
Random: refers to process that generates mathematically random result; selection process operates
in truly random method and researcher can calculate probability of outcomes
o Each element has equal probability of being selected
Sampling error: how much a sample deviates from being representative of the population;
deviation between sample results and a population parameter due to random processes
Margin of error: estimate about the a

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