SOAN 2120 Study Guide - Final Guide: Contingency Table, Factorial Experiment, Type I And Type Ii Errors

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Introductory Methods Exam Review Guide
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Textbook Notes:
Chapter 5: Qualitative and Quantitative Sampling page #108-133 (9 Questions):
Non-probability Sampling:
Nonprobability or nonrandom samples means they rarely determine the sample size in
advance and have limited knowledge about the larger group or population from which the
sample is taken
The qualitative researcher selects cases gradually, with the specific context of a case
determining whether it is chose
Types of Non-probability Samples:
Type of Sample
Principle
Haphazard
Get any cases in any manner that is convenient
Quota
Get a present number of cases in each of several predetermined
categories that will reflect the diversity of the population, using haphazard
methods
Purposive
Get all possible cases that fit particular criteria, using various methods
Snowball
Get cases using referrals from one or a few cases and then referrals from
those cases, and so forth
Deviant Case
Get cases that substantially differ from the dominant pattern (a special type
of purposive sample)
Sequential
Get cases until there is no additional information or new characteristics
(often used with other sampling methods)
Haphazard, Accidental, or Convenience Sampling:
Haphazard sampling can produce ineffective, highly unrepresentative samples and is not
recommended when a researcher haphazardly selects cases that are convenient, he or she
can easily get a sample that seriously misrepresents the population
Such samples are cheap and quick; however, the systematic errors that easily occur make
them worse than no sample at all
Television interviewers go out on the street with camera and microphone to talk to a few
people who are convenient to interview the people walking past a television studio in the
middle of the day do not represent everyone; likewise, television interviewers often select
people who look “normal” to them and avoid people who are unattractive, poor, very old, or
inarticulate
Quota Sampling:
In quota sampling, a researcher first identifies relevant categories of people, then decides
how many to get in each category thus, the number of people in various categories of the
sample is fixed
Quota sampling is an improvement because the researcher can ensure that some differences
are in the sample once the quota sampler fixes the categories and number of cases in each
category, he or she uses haphazard sampling
Purposive or Judgmental Sampling:
Purposive sampling is used in situations in which an expert uses judgment in selecting cases
with a specific purpose in mind with purposive sampling, the researcher never knows
whether the cases selected represent the population; it is often used in exploratory research
or in field research
Purposive sampling is appropriate in 3 situations:
1. To select unique cases that are especially informative
a. Ex: a researcher wants to use content analysis to study magazines to find
cultural themes
2. To select members of a difficult-to-reach, specialized population
a. Ex: the researcher wants to study prostitutes
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Introductory Methods Exam Review Guide
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3. When a researcher wants to identify particular cases for an in-depth investigation; the
purpose is less to generalize to a large population than it is to gain a deeper
understanding of types
a. Ex: used in a focus group study of what working-class people think about politics
Snowball Sampling:
Snowball sampling (also called network, chain referral, or reputational sampling) is a method
for identifying and sampling (or selecting) the cases in a network
It is based on an analogy to a snowball, which begins small but becomes larger as it is rolled
on wet snow and picks up additional snow it begins with one or a few people or cases and
spreads out on the basis of links to the initial cases
The crucial feature is that each person or unit is connected with another through a direct or
indirect linkage this does not mean that each person directly knows, interacts with, or is
influenced by every other person in the network, rather it means that, taken as a whole, with
direct and indirect links, they are within an interconnected web of linkages
Researchers represent a network by drawing a sociogram a diagram of circles connected
with lines
Deviant Case Sampling:
A researcher uses deviant case sampling (also called extreme case sampling) when he or
she seeks causes that differ from the dominant pattern or that differ from the predominant
characteristics of other cases
Similar to purposive sampling, a researcher uses a variety of techniques to locate cases with
specific characteristics deviant case sampling differs from purposive sampling in that the
goal is to locate a collection of unusual, different, or peculiar cases that are nonrepresenative
of the whole
Sequential Sampling:
In sequential sampling, a researcher continues to gather cases until the amount of new
information or diversity of cases is filled
In economic terms, information is gathered until the marginal utility, or incremental benefit for
additional cases, level off or drops significantly
Probability Sampling:
Populations, Elements, and Sampling Frames:
A sampling element is the unit of analysis or case in a population it can be a person, a
group, or organization, a written document or symbolic message, or even a social action that
is being measured
The large pool is the population, which has an important role in sampling to define the
population, a researcher specifies the unit being samples, the geographical location, and the
temporal boundaries of populations
The term target population refers to the specific pool of cases that he or she wants to study
the ratio of the size of the sample to the size of the target population is the sampling ratio
A researcher operationalizes a population by developing a specific list that closely
approximates all the elements in the population; this list is a sampling frame he or she can
choose from many types of sampling frames: telephone directories, tax records, driver’s
license records, and so on
A good sampling frame is crucial to good sampling a mismatch between the sampling
frame and the conceptually defined population can be a major source of error
Any characteristic of a population is a population parameter it is the true characteristic of a
population; parameters are determined when all elements in a population are measured; the
parameter is never known with absolute accuracy for large populations, so researchers must
estimate it on the basis of samples; they use information from the sample, called a statistic, to
estimate population parameters
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Why Random?
The area of applied mathematics called probability theory relies on random process the
word random refers to a process that generates a mathematically random result; that is, the
selection process operates in a truly random method, and a researcher can calculate the
probability of outcomes
Random samples are most likely to yield a sample that truly represents the population
In addition, random sampling lets a researcher statistically calculate the relationship between
the sample and the population that is, the size of the sampling error a nonstatistical
definition of the sampling error is the deviation between sample results and a population
parameter due to random process
Types of Probability Samples:
Simple Random:
In simple random sampling, a researcher develops an accurate sampling frame, selects
elements from the sampling frame according to a mathematically random procedure, then
located the exact element that was selected for inclusion in the sample
The researcher can get random umbers from a random-number table, a table of numbers
chosen in a mathematically random way the numbers are generated by a pure random
process so that any number has an equal probability of appearing in any position
Unrestricted random sampling with random sampling with replacement that is, replacing an
element after sampling it so it can be selected again; in simple random sampling without
replacement, the researcher ignores elements already selected into the sample
Sampling distribution is a distribution of different samples that shows the frequency of
different sample outcomes from many separate random samples the pattern will become
clearer as more and more independent random samples are drawn from the population
The central limit theorem from mathematics tells us that as the number of different random
samples in a sampling distribution increased toward infinity, the patter of samples and the
population parameter becomes more predictable with huge random samples, the sampling
distribution forms a normal curve, and the midpoint of the curve approaches the population
parameter as the number of samples increases
Random sampling means that the most random samples will be close to the population most
of the time, and that one can calculate the probability of a particular sample being inaccurate
A confidence interval is a range around a specific point used to estimate a population
parameter a range is used because the statistics of random processes do not let a
researcher predict an exact point, but they let the researcher say with a high level of
confidence that the true population parameter lies within a certain range
Systematic Sampling:
Systematic sampling is simple random sampling with a shortcut for random selection again,
the first step is to number each element in the sampling frame
A researcher calculates a sampling interval, and the interval becomes his or her quasi-
random selection method the sampling interval tells the researcher how to select elements
from a sampling frame by skipping elements in the frame before selecting one for the sample
In most cases, a simple random sample and a systematic sample yield virtually equivalent
results one important situation in which systematic sampling cannot be substituted for
simple random sampling occurs when the elements in a sample are organized in some kind
of cycle or pattern
Stratified Sampling:
In stratified sampling, a researcher first divides the population into subpopulations (strata) on
the basis of supplementary information
After this, the researcher draws a random sample from each subpopulation he or she can
sample randomly within strata using sample random or systematic sampling
The researcher controls the relative size of each stratum this guarantees
representativeness or fixes the proportion of different strata within a sample
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