U9

Question 1

Population

Answer 1

Entire aggregation of cases that meet specified criteria

Question 2

Eligibility Criteria

Inclusion Criteria

Answer 2

Specified criteria that delimit the study

Question 3

Target Population

Answer 3

The entire population which a researcher is interested

Question 4

Accessible Population

Answer 4

Comprises cases from the target population which are most accessible

Question 5

Sample

Answer 5

A subset of a population

Question 6

Elements

Answer 6

Entities that make up the samples and populations

Question 7

Sampling bias

Answer 7

systematic overrepresentation or underrepresentation of some segment of the population in terms of characteristics relevant to the research question

Question 8

Strata

Answer 8

mutually exclusive segments of a population based on a specific trait

Question 9

Sampling Designs

Quant vs Qual

Answer 9

Quant plan ahead, Qual plan on the go

Question 10

Qualitative Sampling Designs

Answer 10

1 - Nonprobability Sampling -selection of elements by non-random methods. can’t estimate probability of including each element and every element does not have a chance for inclusion.

2 - Probability Sampling - random selection of elements from a population, where the random assignment is after population selection.

Question 11

Nonprobability Sampling

Answer 11

selection of elements by non-random methods. can’t estimate probability of including each element and every element does not have a chance for inclusion.

CONVENIENCE Sampling risks sampling bias, available subjects may be atypical of the population.

SNOWBALL Sampling - sample members are asked to refer others with the eligibility criteria.

QUOTA Sampling - researchers identify strata of the population and then determine how many participants are needed from each stratum to meet a quota

PUPOSIVE Sampling - based on belief that researchers knowledge about the population can be used to hand pick the cases to be included in the sample

Question 12

Evaluation of Probability Sampling

Answer 12

avoids conscious/unconscious biases

• allows for estimation of sampling error
• expensive and demanding
• usually beyond scope of most researchers to draw a probability sample without a narrowly defined sample.

Question 13

Probability Sampling

Answer 13

SIMPLE RANDOM Sampling - most basic probability sampling design. Population is defined, then researchers establish a sampling frame (actual list of population elements), the elements in the frame are numbered consecutively, table of random numbers or computer is used to randomly draw numbers till desired sample size is reached.
No researcher bias, no guarantee sample is representative of population, but guarantee it is by chance. larger the sample the better.
Laborious process

STRATIFIED RANDOM Sampling - population divided into homogenous sub sets from which elements are randomly selected.
-Aims to enhance samples representativeness, usually group the elements of the startup and randomly select the desired number of elements, may select proportionate samples or disproportionate (weighting can be used to analyze results)

CLUSTER Sampling - most common for national surveys.

SYSTEMATIC Sampling - involves selection of every Kth case from a list or group.

Question 14

Evaluation of Probability Sampling

Answer 14

avoids conscious/unconscious biases

• allows for estimation of sampling error
• expensive and demanding
• usually beyond scope of most researchers to draw a probability sample without a narrowly defined sample.

Question 15

Sample Size in Qualitative Studies

Answer 15

Larger sample always = more representative of the population

Question 16

Power Analysis

Answer 16

aids researchers to estimate how large their sample should be to test their research hypotheses adequately.

a large sample can not correct for a faulty sampling plan

Question 17

Sampling in Qualitative Sampling

Answer 17

typically small and nonrandom
also an emergent design
Convenience and Snowball Sampling
Purposive Sampling
Theoretical Sampling

Question 18

Sampling in the 3 main Qualitative Traditions (Ethnographic, Phenomenological, and Grounded Theory)

Answer 18

Ethno- big net approach, mile with as many members of the culture as possible, yet often rely on few key informants

Phen- typically less than or equal to 10 participants, all must have experienced the phenomenon with ability to articulate it

Grounded Theory - 20-30 participants with theoretical sampling to develop and refine categories and theory.

Question 19

Critiquing Sampling Plan

Answer 19

Particular scrutiny with Quantitative as can lead to bias and decrease confidence in results

1-did researcher describe sampling strategy adequately?
2- did researcher make good sampling decisions?

Quantitative - response rates, number of participants relative to number samples, nonresponse bias - difference btw participants those who declined. if target population defined broadly, there may be missed opportunities to control extraneous variables and gap btw accessible population and the target population too great

Qualitative - adequacy and appropriateness (sufficient and quality data and the methods to gain the data)
-good to see participants with unusual or negative cases
fittingness - degree of congruence btw study sample and ppl at another site
-transferability