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

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9 Aug 2016

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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

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

Introductory Methods Exam Review Guide

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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