Week 5 : Sampling Strategies

Question 1

Target Populations…

Answer 1

• population parameters, census, sampling frame
• a group about which social scientists attempt to make generalizations about
• do not necessarily refer to groups of individuals, might refer to groups of nations, corporation, written documents or legal cases for example

Question 2

Sample & Sampling

Answer 2

• Sample = a subset of the population selected for a study
• Sampling = the process of deciding what or whom to include in the sample

Question 3

Unit of analysis…

Answer 3

• I will need to collect data from _______ to answer my research question

Question 4

Population Parameter

Answer 4

• represents the ‘true value’ or ‘true measurement’ of the population
• they are often not feasible in social research… why?
• time, resource, frequency (every 5, 6, 10 or 15 years)
• limited number of questions

Question 5

Census

Answer 5

• a study that includes data on every member of a population
• more common in social research when the population in question is not composed of people
• rare cuz they are often not feasible

Question 6

1936 election (Landon vs Roosevelt)

Answer 6

• literary digest survey (2 million completed resources)
• survey (poll) result… Landon wins & Roosevelt loses
• actual result… Roosevelt wins & Landon loses
• What is wrong?… poor sampling strategy, sample did not equal the population

Question 7

Observed value

Answer 7

true value + systematic error + random error = observed value

Question 8

Errors in sampling

Answer 8

1. Systematic error = cannot be estimated, only discuss direction of bias – flaw built into the design of the study
2. Random error = unbiased, can be estimated using statistics

Question 9

Probability samples

Answer 9

• samples that are based on random selection are called probability samples
• a probability sample is one in which (a) random choice is used to select participants for the sample and (b) each individual has a probability of being selected that can be calculated
• removes more systematic errors

Question 10

Probability sample has 2 key advantages over a nonprobability sample

Answer 10

1. estimates based on a probability sample are unbiased = to whatever extent estimates differ from the true population parameter, they are equally likely to overestimate it as underestimate it
2. the only difference between the estimates and the true parameter is due to random chance = this difference is called sampling error (NOT a systematic error)

Question 11

Margin of error

Answer 11

• the amount of uncertainty in an estimate
• equals to the distance between the estimate and the boundary of the confidence interval
• levels of confidence… 95%, 99%, etc.

Question 12

Example of margin of error

Answer 12

• according to a Gallup poll, 43% of Americans approve the job the president is doing. This estimate has a margin of error of 3 percentage points at 95% confidence interval
• Translation… we can be 95% sure that the true level of presidential approval is between 40% and 46%…
• Calculating the confidence interval… Lower bound = mean - margin of error = 43-3=40 … Upper bound = mean + margin of error = 43 + 3=46

Question 13

2 thins to emphasize about margin of error…

Answer 13

1. margin of error pertains only to sampling error (so only applies to probability samples)
2. margin of error has a specific relationship with sample size… as the sample size gets larger, the sampling error gets smaller & so does the margin of error (margin of error is proportional to the square root of the sample size)

Question 14

Example of margin of error & sample size relationship

Answer 14

• Study A has a sample pf 100 ppl & a margin of error being 3%
• If we want to reduce the margin of error to 1%. How many ppl do we need to include in the sample?
  ○ Reduction in margin of errors = 3/1 = 3 times
  ○ Increase in sample size = (3)^2=9 times
  ○ So, in study A we need to have 9 x 100=900 respondents in the sample

Question 15

Simple random sampling

Answer 15

• sampling frame = a list of population members from which a probability sampe is drawn
• the most straightforward type of probability sample
• each individual has the same probability of being selected into the sample
• each pair of individuals has the same probability of being selected (everyone’s chance of being selected into the sample is completely independent of everyone else’s)
• obtain sampling frame then generate a set of random numbers & select individual corresponding to select numbers

Question 16

Systematic simple random sampling

Answer 16

• use a systematic sample to draw a sample that is 1/n the size of the total population…
• first select one of the first n individuals on the list of members in the sampling frame then then every nth member on the list after that
• not all pairs are equally likely !
• important in political exit polls

Question 17

Cluster sampling

Answer 17

• no available sampling frame
• 1) divide target population into clusters (e.g. cities withing Canada, classrooms in a highschool)
• 2) select clusters randonly, get sampling frame for selected clusters
• 3) select individuals randomly from the selected clusters
• enhances feasibility, lower costs and works when a sampling frame doesn’t exist

Question 18

Stratified sampling

Answer 18

• 1) obtain the sampling frame
• 2) divide the target population is divided into strata (e.g. gender, social class)
• 3) select individuals randomly from all strata
• 4) number of selected individuals reflects the proportions from each stratum
• prevents samples from becoming non-representative due to pure chance & can oversample for small groups

Question 19

Weighting

Answer 19

• if a probability sample is conducted in such a way that different people have different possibilities of being selected, then the results must be weighted for estimates to be accurate
• when a sample is weighted, some observations count more than others
• the more a particular group is overrepresented in the sample, the less weight each individual from that group should recieve
• If Person A is x->(A/B) times more likely to be in our sample than Person B , then we give Person A 1/x times as much weight as Person B when computing our estimates

Question 20

Post-survey weighting

Answer 20

• Response rate = number of valid responses/ number of invitations sent x 100%
• Gap between the desired sample & the actual sample
• nonresponse may create systematic difference between the sample and population

Question 21

Example of postsurvey weighting

Answer 21

• based on census, we know there is 20% older adults in the population. However, due to nonreseponse, only 10% of the respondents in our sample are older adults
• the older adults in our sample should have more weight than younger adults
• how much more? population%/sample% = 20/10 = 2 times more

Question 22

Nonprobability sample

Answer 22

• a sample that is not drawn using a method of random sampling
• the key issue here is representativeness
• there may be systematic differences between our sample & the target population

Question 23

nonprobability sample

systematic error

Answer 23

• a flaw build into the design of the study
• impossible to quantify the size of the bias
• only possible to predict the direction of bias

Question 24

nonprobability samples are not representative of the population

Answer 24

• researchers cannot concluded that a hypothesis holds true in the same way throughout all population subgroups (i.e. low generalizability)

Question 25

2 benefits of nonrepresentative samples

Answer 25

• often better for initial tests of hypotheses than representative samples
  1. the diversity of representative samples makes detecting cause-and-effect relationships more difficult (easier to identify cause when cases we study are very similar to one another
  2. we can often gather more/better into on nonrepresentative samples than we can on representative samples

Question 26

nonrepresentative sampling

1 - convenience sampling

Answer 26

• easiest & most convenient
• select any subjects who are willing to participate
• cheapest & easiest method
• systematic errors

Question 27

nonprobability sample

2 - purposive sampling

Answer 27

• selecting cases based on key features…
• access to & quality of data
• typicality
• extremity
• importance
• deviant case
• contrasting outcomes
• key differences
• past experience & intuition

Question 28

nonprobability sample

3 - sequential sampling

Answer 28

• collect additional data based on their findings from data they’ve already collected
• key informants = ppl whom a researcher interviews intensively, typically multiple times, over the course of a fieldwork project
• sampling for range = try to interview ppl who occupy a wide range of different roles within the organization
• saturation = the extent to which new interviews continue to generate new insights about the project

Question 29

nonprobability sample

4 - snowball sampling

Answer 29

• starts with one respondent who meets the requirement for inclusion
• asks him/her to reccoment other ppl to contact
• useful for studying ‘hidden populations’ such as drug dealers & computer hackers

Question 30

Big data

Answer 30

• ‘found data’
• electronic traces
• administratice records
• sample or population? probable sample