SOC200 - Logic of Sampling (Chapter 6)

Question 1

Sample

Answer 1

selection of observations from a pop

Question 2

Population

Answer 2

all possible data values that could be observed

Question 3

Sampling

Answer 3

process of selecting observations from target pop

Question 4

NONPROBABILITY SAMPLING

Answer 4

Selecting observations in way that doesn’t ensure results generalizable to target pop
•if members of pop don’t have an equal chance to be selected - can’t be sure that it represents whole pop
•complex social phenomena

Question 5

NONPROBABILITY SAMPLING

Answer 5

Used in situations where it virtually impossible/unnecessary to ensure generalizability of results
hard to find people or groups with unique conditions
Homeless, Deviant cases: not usual cases, unique to the norm (gay military), Complex social phenomena: (initiation into a frat, cult)

Question 6

APPROACHES to NON- PROBABILITY SAMPLING:

1. Relying on Available Subjects

Answer 6

Sample limited to available subjects.

Good for pretesting questionnaire/providing info for pilot study

Question 7

APPROACHES to NON- PROBABILITY SAMPLING:

1. Relying on Available Subjects

Answer 7

Undergraduate students common source of data for this sampling approach

Question 8

FIVE MAIN APPROACHES to NON- PROBABILITY SAMPLING: 2. Purposive Sampling

Answer 8

Selecting sample based on own knowledge of sample + purposes of study.
widely used for studying deviant cases to improve understanding of general pattern (Gays in the military, Male midwives)
zeroing in deviation, selecting samples based on what you want to study

Question 9

FIVE MAIN APPROACHES to NON- PROBABILITY SAMPLING: 3. Snowball Sampling

Answer 9

network based - researcher interviews few members of target pop + asks to be referred to other members of they know
Good for locating members of unique possibly marginal pop
•might be easier to do this than having a blanket survey
•Used primarily for exploratory purposes

Question 10

FIVE MAIN APPROACHES to NON-

PROBABILITY SAMPLING: 4. Quota Sampling

Answer 10

methodical - knowing the demographic breakdown of target pop (52% female)
select people fitting this combo of characteristics (quota frame)
•stratify pop based on demographic breakdown

Question 11

FIVE MAIN APPROACHES to NON-

PROBABILITY SAMPLING: 4. Quota Sampling

Answer 11

E.g. If you decide to interview 100 people in Toronto, then you should have 52 females in your sample
•problem when demographic breakdown is not accurate (not up to date)

Question 12

FIVE MAIN APPROACHES to NON-

PROBABILITY SAMPLING: 4. Quota Sampling

Answer 12

Good when time + sampling budgets limited/high level of accuracy is not needed
•Selection of sample elements within given cell may be biased even though proportion of population is accurately estimated

Question 13

FIVE MAIN APPROACHES to NON- PROBABILITY SAMPLING: 5. Informers

Answer 13

–Collaborating with member “inside” group want to study
–Beware of reliability + validity of the info: can’t randomly select, often don’t have a choice, only someone who is willing to give you that info
•there might be something about that informer that make them unreliable - filter everything they tell you, often marginalized by group, biased view/maybe unhonest, cencorsed view

Question 14

Major Limitation of Non-Probability Samples

Answer 14

• assuming relationship beyond what sample can support
• not necessarily giving everyone a chance to get selected
• while some may be necessary, reliability + generalizability issues - can’t claim they represent characteristics of pop
• exploratory
• puposive +snowball would be valid because they focus on target pop

Question 15

PROBABILITY SAMPLING

Answer 15

•Aim: provide reliable + valid description of pop
equal chance – better probability of generalizable results
•by collecting observations with method that ensures sample data have same variations + consistent characteristics in pop data

Question 16

PROBABILITY SAMPLING

Answer 16

• EPSEM: based on probability theory - equal chance of being selected
• minimizes sampling bias, ensuring variations in sample reflect variations in pop within a narrow margin of error

Question 17

Benefits of Randomly Selecting Observations

Answer 17

1. minimizes sampling bias
   •If you don’t return ball, then you are changing probability of being selected
   •Constant 1/10 chance if you put it back
   •Anything that increases/decreases chance of being selected – bias

Question 18

Benefits of Randomly Selecting Observations

Answer 18

probability theory to estimate:

a) characteristics of pop based on much smaller sample
b) how closely characteristics in random sample represent the pop characteristics

Question 19

Estimating Sample Representativeness using Probability Theory

Answer 19

•using observations in sample to estimate summary value of characteristic in whole population (the parameter)

Question 20

Estimating Sample Representativeness using Probability Theory

Answer 20

•sample more representative if summary of values of characteristic in sample is closer to summary value of characteristic in pop

Question 21

Estimating Sample Representativeness using Probability Theory

Answer 21

Increasing Samples
•More combinations possible within samples of 2 as we move closer to true mean
•Most frequent estimate would be the true mean

Question 22

Estimating Sample Representativeness via Sampling Error

Answer 22

• Problem: don’t know exact pop value of characteristic
• Probability Theory as more and/or larger samples obtained from the pop, statistics begin to cluster around the unknown population parameter in a predictable way (The Central Limit Theorem)

Question 23

Estimating Sample Representativeness via Sampling Error

Answer 23

• allows researchers to estimate how close sample value is to pop value through concept of Standard Errors
• Standard Error: how close values cluster

Question 24

STANDARD ERROR INCREMENTS

Answer 24

certain proportions of sample estimates will fall within certain distance from pop parameter
•34% will fall 1 standard deviation above + below
•68% of sample estimate will fall within range of standard error
•2 standard errors – 47.5%
•95% of samples might fall within 2 standard errors
•99.9% will fall within 3 standard errors

Question 25

STANDARD ERROR

Answer 25

• Natural tendency that most cluster around central value – normal distribution
• standard error distance will change based on value of mean + range of data, but % will stay the same

Question 26

STANDARD ERROR SIZE

Answer 26

size of the SE changes with:
a) estimated pop parameter
b) sample size
•as sample size increases, standard error size decreases
•understanding parameter + how much precision you want, then can calculate how big sample should be

Question 27

Confidence Levels and Confidence Intervals

Answer 27

• inference about how confident researchers are sample statistics fall within specified interval (the calculated SE) from parameter
• diff way of understanding standard error
• based on probability theory – as long as sample is random + representative – you can say estimate statistic will fall in between 1 standard error

Question 28

Real World of Sampling: sampling frame

Answer 28

list of units representing all potential observations that will be selected from pop
▫Lists of members or employees in an organization, Phone books

Question 29

Probability Sampling Methods 1. Simple Random Sampling

Answer 29

seldom used in practice

1. Assign diff number to each element in sampling frame
2. After deciding on sample size, select the units randomly using table of random numbers/random number generator

Question 30

Probability Sampling Methods 2. Systematic Sampling

Answer 30

Every nth element chosen from frame, but with random start
1.Calculate sampling interval: pop. size/sample size
2.Select random start using number table
3.Select every nth unit based on calculated sampling interval
•if list is ordered – biased sample

Question 31

Probability Sampling Methods 3. Stratified Sampling

Answer 31

Significantly reduces sampling error
a) grouping population on key variables
b) randomly sampling from each group
c) ensuring random sample of each group proportional to size of group
•Grouping on key variables makes units more homogeneous, sampling error lower

Question 32

Probability Sampling Methods 3. Stratified Sampling

Answer 32

-choosing sample proportionate to diff parts of pop

•make sure 30% female, 70% female

Question 33

Probability Sampling Methods 4. Cluster Sampling

Answer 33

Preferable when entire list of pop is impractical/impossible to compile, but is already grouped into subpopulations (students in schools; officers in police stations; individuals on city blocks)
Involves random sampling at each cluster

Question 34

Probability Sampling Methods 4. Cluster Sampling

Answer 34

must sample in manner that makes the cases selected in each cluster proportionate to total number of cases found in cluster
stratifying sample by each level
natural groups sampled initially, members of each selected group being subsampled afterward

Question 35

Probability proportionate to size (PPS) sampling

Answer 35

•Type of multistage cluster sample in which clusters selected, not with equal probabilities, but with probabilities proportionate to their sizes – as measured by number of units to be subsampled

Question 36

Weighting

Answer 36

sampling whereby unit selected an unequal probabilities assigned weights in such matters to make sample representative of population from which it was selected