SAMPLING DESIGNS

Question 1

population

Answer 1

entire aggregation/groups of people that meet a set of criteria

Question 2

accessible population

Answer 2

aggregation meet criteria and people are actually accessible

Question 3

target population

Answer 3

aggregate cases about which you want to make generalizations

Question 4

sampling

Answer 4

process of selecting a portion of a population to represent an entire population

Question 5

sample

Answer 5

actual subset of units that compose the population

Question 6

representativeness

Answer 6

key characteristics of your sample are the same as the population

Question 7

strata

Answer 7

mutually exclusive segments of population established by 1 or more characteristics

Question 8

sampling bias

Answer 8

systematic over representation or under representation of some segment of the population with respect to a characteristic that’s relevant to the research

Question 9

probability sampling

Answer 9

• make sure people in sample have equal chance of being picked to be in study
• some form of random selection in choosing the elements
• researcher is in a position to specify probability that each element of population will be included in sample.

Question 10

non-probability sampling

Answer 10

• elements are selected by nonrandom methods
• no way to estimate probability that each element has of being included and every element usually does not have a chance for inclusion

Question 11

non-probability sampling methods

Answer 11

NOT RANDOM

• convenience sampling (snowball/network)
• quota sampling
• purposive or judgmental sampling
• theoretical sampling

Question 12

convenience sampling

Answer 12

don’t have access to people so you use what is available

Question 13

quota sampling

Answer 13

research identifies strata of population and determines portion of elements needed from various segments

Question 14

snowball/network sampling

Answer 14

• someone know someone that knows someone

- building

Question 15

purposive/ judgmental sampling

Answer 15

based on belief that researcher knowledge of population can be used to hand pick cases that are to be included in sample

Question 16

theoretical sampling

Answer 16

pick people that we know use instrument

Question 17

probability sampling method

Answer 17

• simple random sampling (stratified rand
• cluster sampling
• systematic sampling

Question 18

simple random sampling

Answer 18

researcher establishes sampling frame

Question 19

sampling frame

Answer 19

actual list of elements from which sample will be chosen

Question 20

stratified random sampling

Answer 20

use strata characteristics and make sure you have equal amount of people from each group

Question 21

proportionate stratified sampling

Answer 21

size of sample strata is proportional to the size of population strata

Question 22

disproportionate sampling

Answer 22

if there are not enough people, use a portion to represent the large population

Question 23

cluster sampling

Answer 23

typically send in a large scale of surveys when all other methods become expensive

Question 24

systematic sampling

Answer 24

selection of every k case from some list/group

can be both non/probability

Question 25

sampling error

Answer 25

difference between population values and sample values

Question 26

use power analysis

Answer 26

statistical procedure

Question 27

need info on alpha

Answer 27

risk you want to take on a type I error (wrongly rejecting true null hypotheses) usually 0.05

Question 28

need info on 1-beta which is standardly 0.80 = power

Answer 28

willing to take a 20% chance of comminting a type II error (wrongly accepting false hypothesis)

Question 29

need info on gamma = effect size =

Answer 29

estimate of magnitude of relationship between research variables if relationship between independent and dependent variables is strong, then need small sample if weak need larger sample

Question 30

use previous research to estimate

Answer 30

effect size

Question 31

if no effect size then

Answer 31

0. 20= small effect size 0. 50= medium effect size 0. 80= large effects size

Question 32

attrition

Answer 32

drop out rates/dying

Question 33

murphy's law

Answer 33

if anything can go wrong, it will

Question 34

of variables

Answer 34

larger # of variables, larger sample size, more # you need

Question 35

sensitivity of measures

Answer 35

different measures vary in their ability to measure precisely concepts under study. if measure is vague, larger sample.

Question 36

subgroup analysis

Answer 36

if sample is divided to test for effects in specific group -sample must be large

Question 37

steps in drawing a sample

Answer 37

1. identify target population 2. identify accessible population 3. specify eligibility criteria (specific characteristics) 4. specifiy sampling plan 5. recruit sample