sampling & research design

Question 1

population

Answer 1

The entire set of individuals (or units) of interest.

Question 2

target population

Answer 2

The specific population to which you want to generalize.

Question 3

sample

Answer 3

The subset of the target population actually studied

Question 4

probability sampling

Answer 4

Every individual in the population has a known, nonzero chance of being selected
(e.g., simple random sampling, systematic sampling, stratified sampling).

Question 5

nonprobability sampling

Answer 5

The likelihood of selection is not known (e.g., convenience sampling, self-
selection, snowball sampling)

Question 6

sampling frame

Answer 6

The list or mechanism from which a sample is actually drawn

Question 7

selection interval / systematic sampling

Answer 7

a fixed interval used to select participants from a list (e.g., every 7th name).

Question 8

What is the difference between a population, a target population, and a sample?

Answer 8

population -> target population -> sample

Question 9

advantages or disadvantages of probability sampling

Answer 9

Advantages:
Minimizes Bias
Generalizability
Statistical Accuracy
Reproducibility

Disadvantages:
Time-Consuming
Expensive
Difficult to Implement

Question 10

advantages or disadvantages of nonprobability sampling

Answer 10

Advantages:
Quick and Cost-Effective
Useful for Exploratory Research
Practical for Hard-to-Reach Groups

Disadvantages:
Higher Risk of Bias
Limited Generalizability
Lack of Statistical Accuracy

Question 11

how is selection interval determined

Answer 11

by choosing a specific range within a dataset based on factors like the desired sample size, distribution of data, and the research question

Question 12

why is having a complete or accurate sampling frame important
for reducing sampling error?

Answer 12

it ensures that every member of the population has an equal chance of being selected for the sample

Question 13

quantitative methods

Answer 13

○ Result in data that can be represented by and condensed into numbers
Less depth, more breadth, focusing on a larger number of cases
ex. survey

Question 14

qualitative methods

Answer 14

○ Ways of collecting data that yield results such as words or text
○ Gain in-depth understanding of a relatively small number of cases
Potential for greater richness in meaning than quantified data

Question 15

mixed method

Answer 15

○ They are complementary
○ Researchers may prefer one because they’re trained in one or favor one

Question 16

units of analysis

Answer 16

• A person, collective, or object that we want to learn more about
  
  • This encompasses a larger population that we are interested in making claims about

Question 17

margin of sampling error

Answer 17

Describes how close we can reasonably expect a survey result (sample statistic) to fall relative to the true population value (parameter)

Question 18

sampling error

Answer 18

• The difference between the statistics obtains from a sample and the actual parameters of a population
  
  • Probability sampling allows for the calculation of the sampling error that is expected given the size of the sample being used
    Because surveys only select a sample of the population, the result probably won’t exactly match the ‘true’ result that we would get if we interviewed everyone in the population

Question 19

simple random sampling

Answer 19

A sampling technique where the researcher gives all members of a population (more accurately, of a sampling frame) an equal probability of being selected

Question 20

, systematic sampling

Answer 20

• Systematically choosing sampling units from a list by defining an interval (k) and then selecting every kth element in our list
  A probability sampling method in which a random sample from a larger population is selected

Question 21

stratified sampling

Answer 21

• A sampling technique where researchers divide the study population into two or more mutually exclusive subgroups (strata) and then draw a sample from each subgroup
  
  • Used to ensure that the sample adequately represents the identified subgroups
    To make sure that a small subgroup is adequately represented in our sample

Question 22

Proportionate stratified sampling

Answer 22

Carve up our sampling frame into strata then choose cases from each stratum in line with its numbers in the larger population

Question 23

disProportionate stratified sampling

Answer 23

An approach to stratified sampling in which the sizes of the subgroup samples do not match their relative sizes within the population

Question 24

oversampling

Answer 24

When a subgroup represents a greater share of a sample than the same subgroup represents in the larger population

Question 25

weighting

Answer 25

Adjusting for how much particular cases contribute to the statistics for a sample

Question 26

generalizable

Answer 26

- When a study's results can reasonably tell us something about the larger population from which its sample was drawn "representative sample"

Question 27

convenience sampling

Answer 27

units are selected for inclusion in the sample because they are the easiest for the researcher to access (ex. class)

Question 28

self-selection

Answer 28

people volunteering to do a study

Question 29

snowball sampling

Answer 29

people selected telling their family + friends