Chapter 7- sampling

Question 1

sampling frame

Answer 1

list of who you are asking/selecting

Question 2

quota sampling

Answer 2

based on knowledge of the characteristics of the population being sampled: men/women, age,, incomes

Question 3

non-probability sampling

Answer 3

based on targetting (purposive selection), NOT ALL elements population have chance to be selected, population is unknown, representativeness often problem

Question 4

convenience sample

Answer 4

non probability, take what you can find (certain location), no control over representativeness

Question 5

snowball sample

Answer 5

non-probability, ask people+ let them ask their friends etc

Question 6

purposive sample

Answer 6

non-probability, ask respondents with particular characteristics, usually specific population (fans), researcher decides how useful the sample will be

Question 7

quota sample

Answer 7

non-probability, set target number of respondents; ask people till you reach target

Question 8

volunteer sample

Answer 8

non-probability, let people come to u (place advertisement, experiment)

Question 9

probability sampling

Answer 9

everybody has chance to be selected; if you select sufficient units, you’ll get representative group

Question 10

simple random sampling

Answer 10

define population (pick names from hat) when working from a list of subjects or a data base

Question 11

systematic random sampling

Answer 11

define population, think of ‘system’ to select (every 10th person, need to know size population) when working from list of subjects

Question 12

stratified random sampling

Answer 12

define population, divide in subgroups/strata with same trait (age, income), randomly sample from each subgroup, improves representativeness of a sample

Question 13

cluster random sampling

Answer 13

define population, divide into clusters->different traits in each sample, mini-representation of whole population, randomly select one or more clusters

Question 14

multi-stage cluster sampling

Answer 14

several stages (Shanghai -> urban, suburban, rural -> blocks or towns from each district->residential areas randomly selected from each block or town) errors: when there is an error in the first sample, the other samples will have that same error

Question 15

stratified

Answer 15

population divided into subgroups or strata by researcher, and then samples from each strata (age, men/women), subgroups well represented, but more difficult

Question 16

cluster

Answer 16

samples are randomly chosen from naturally formed groups (clusters) (neighbourhoods, school classes), easy, efficient if you dont have info whole population, maybe not fully representative

Question 17

PPS

Answer 17

probability proportionate to size, multistage cluster sample in which clusters are selected, not with equal probabilities but with probabilities proportionate to their sizes as measured by the number of units to be subsample

Question 18

EPSEM

Answer 18

equal probability of selection method, each member of a population has the same change of being selected into the sample