Flashcards in Lecture 5 - Sampling Procedures Deck (42):

1

## Define target population

### It is the totality of the cases that conform to some designated specifications such as demographics or psychographics, or say the population of a country

2

## What is a census?

### Surveying the entire target population, which is costly, time consuming, and sometimes impossible

3

## Instead of implementing a census, it's best to what?

### take a sample from the target population

4

## What is the 6 step sampling procedure?

###
1. Define target population

2. identify the sampling frame

3. Select a sampling procedure

4. Determine sample size

5. Select sample elements

6. Collect data from selected elements

5

## What is a sampling frame?

### It is the list of people/units who are eligible in the target, and the sample is drawn from them

6

## What are the 3 types of sampling?

###
1. Random Sample

2. Stratified sample

3. Cluster sample

7

## What is unit of analysis?

### It is what you are studying, so could be students, store, households etc.

8

##
What are

non-probability samples?

Probability samples?

###
Non-probability samples

(This is when you don't know probability of being included in sample)

Probability sample

(Each population element has a known chance of being included in the sample)

9

## With sampling, what is incidence rate?

###
# target pop / # whole pop

So if you are only targeting small amount of pop the incidence rate will be tiny

10

## What are 3 types of non-probability samples?

###
1. Convenience Samples

(elements are chosen because they're convenient and available easily)

2. Judgement Samples

(elements handpicked by researcher)

3. Quota Samples

(selecting sample that looks like population by using some criteria)

11

## What are judgement samples suitable for and method?

###
Suitable for exploratory research, subjects that can be experts selected.

Snowballing is a method, which is a special group of subjects identified, then these people help you find more people like them

12

## What kind of sample are most of the projects in this class going to use?

### Convenience samples, because a lot of people are going to be putting their surveys on Facebook and whatnot because we don't have money/resources for random sampling

13

## What is an example criteria for quota samples?

###
So you could select sample by using an observable criteria such as gender, or using unobservable such as personality

Or if you wanted something like

n=100

30% marketers

40% Finance

30% HR

Than you could do survey with screening question to make sure you fulfill quota of percentages. So once you get 30 marketers the quota is filled and no more from marketing is needed/wanted

14

## If you were randomly talking to people in the Rowe using quota sampling, do you need a sampling frame?

### Nope, you don't need a sampling frame because you don't have the list before hand or anything, you're just going around asking people stuff

15

## What is a bad part of quota samples?

### Well you might choose quota on some criteria, but you might miss criterias. So choosing to select based on Uni majors criteria, but ignore intl students criteria.

16

## Explain what happens with a simple random sample?

### Every population element is equally likely to be selected. So every sample with size n is equally likely to occur as any other sample with size n

17

## What is a super simple way to draw a random sample?

###
1. Give id numbers to each population element

2. Find id numbers from a list of random numbers

18

## What is a parameter?

###
It is a characteristic or measure of a population.

So things like Canada pop average income level. So it's a population parameter that could be compared to say, the US.

19

##
Define

N

u

o (sigma)

###
N: size of population

u: population mean

o (sigma): population standard deviation

sample:

n= sample size

x (with bar onto): sample mean

s = sample standard deviation

20

## Define sampling distribution?

### So this is all the possible samples of size n that can be selected from a population of size N

21

## What is the central limit theorem?

###
So when n is large, the sampling distribution will be NORMAL with mean equal to u (pop mean) and standard deviation equal to (formula)

So it shows a nice normally distribute curve for a sampling distribution, that the farther away from the pop mean, the less frequency of samples.

22

## What are the three z for confidence interval estimates?

###
z = 1, 68.3% of all sample means are within range

z = 2, 95.5% of all sample means are within range

z = 3, 99.7% of all sample means are within range

So z = 1 would be most precise out of these, but you would also only be confident 68.3% of the time

23

## If the standard deviation of the population is unknown, what are the two things you can do?

###
1. Use standard dev or population from other research projects that measured the same variable

2. Assume standard deviation of population equals sample standard deviation

24

## What is stratified sampling?

### Random sample is selected from each stratum

25

## What is a stratum (plural is strata)

### it is mutually exclusive and collectively exhaustive subsets of a population

26

## Give an example of a mutually exclusive and exhaustive stratified sampling

###
so for age

55

27

## For stratified samples, how are they weighted? (2 of them)

###
proportionate stratified sampling

(So each stratum size is proportionate to size in population)

Disproportionate stratified sampling

(Size of sample from stratum is determined also according to the variation within that stratum)

28

## How is disproportionate stratified sampling done with regards to variation within stratums?

### So the greater variation within a stratum, the more sampling from it.

29

## What are 3 reasons to use stratified sampling?

###
1. Lower variability within strata increases precision of the sample mean

2. Investigate some characteristics of particular subgroups

3. Ensure representativeness of important segments

30

## What are the two steps wit cluster samples?

###
1. divide target population into clusters that are mutually exclusive and collectively exhaustive

2. Randomly select a subset of clusters

31

## What must clusters be?

### Representative of target population

32

## What are one and two stage cluster sampling?

###
One-stage cluster sampling:

use all elements in the selected cluster

two-stage cluster sampling:

randomly draw a sample from each cluster

33

## Give example of one-stage cluster sampling with wine consumption in Chicago

###
So we randomly select say 100 blocks out of 10,000 blocks in Chicago.

Then we measure wine consumption fro all households in the selected 100 blocks

34

## What is a pro and con with one-stage cluster sampling?

###
1. Less costly, because easier after clustering

2. Less representative, because households in say a cluster could be homogenous, and thus subsets are not representative

35

## What are the two stages of two-stage cluster sampling?

###
1. Randomly select n clusters out of N population clusters available

2. Randomly select sample k from each cluster

36

##
With two-stage cluster sampling:

n*k = desired houlseholds to survey with Chicago wine example

Greater n and smaller k means:

Smaller n and greater k means:

###
Greater n and smaller k means:

(more costly and more representative)

Smaller n and greater k means:

(less costly, and less representative)

37

## For the objective factor, what is the difference between stratified and cluster sampling?

###
stratified:

increase precision

Cluster Sampling:

decrease cost

38

## For the subpopulations factor, what is the difference between stratified and cluster sampling?

###
stratified:

all strata included

Cluster Sampling:

sample of clusters chosen

39

## For the within subpopulations factor, what is the difference between stratified and cluster sampling?

###
stratified:

each stratum should be homogeneous

Cluster Sampling:

each cluster should be heterogeneous

40

## For the across populations factor, what is the difference between stratified and cluster sampling?

###
stratified:

strata should be heterogenous

Cluster Sampling:

cluster should be homogeneous

41

## For the sampling frame factor, what is the difference between stratified and cluster sampling?

###
stratified:

needed for entire population

Cluster Sampling:

needed only for the selected clusters

42