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Population

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entire aggregation of cases in which a researcher is interested.

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Eligibility Criteria (inclusion criteria)

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researchers specify the characteristics that delimit the study population (very important in quantitative studies)

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Target Population

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entire group of people or objects to which the researcher wishes to generalize findings of a study

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Accessible Population

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available group researcher needs to describe clearly to determine to whom research results can be generalized

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sampling

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

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representative sample

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one whose main characteristics closely approximate those of the population ~never a guarantee of this~

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Sampling Bias

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systematic over representation or under representation of some segment of the population in terms of a characteristic relevant to the research question

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Sampling in Quantitative Studies

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researcher select participants for the sample, so that results can be GENERALIZED to other people similar to study samlple

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Sampling in Qualitative research

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researchers select participants for the VARIETY and RICHNESS of their experience to learn more about the "whole" participants may be selected for how different they are or for what new information might be contributed

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strata

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sub-populations of the population, exclusive segments of a population based on a specific characteristics

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non probability sampling

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sample elements chosen deliberately likely to have a biased sample and unlikely to have a representative sample limits generalization of findings due to convenience, available subjects, or certain characteristics. non random methods

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types of non probability sampling are?

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convenience sampling quota sampling consecutive sampling purposive sampling

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convenience sampling

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entails using the most conveniently available people as participants worst type

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snowball sampling (network sampling or chain sampling)

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variant of convenience sampling. early sample members are asked to refer other people who meet the eligibility criteria

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quota sampling

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identify population strata and determine how many participants are needed from each stratum. want to ensure diverse segments are adequately represented in the sample

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consecutive sampling

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involves recruiting all of the people from an accessible population who meet the eligibility criteria over a specific time interval, or for a specified sample size

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Purposive Sampling

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"judgement sampling" is based on the belief that researchers' knowledge about the population can be used to hand-pick sample members *maximum variation sampling *extreme deviant case sampling *typical case sampling *criterion sampling

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Probability Sampling

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involves the random selection of elements from a population random selection way different than random assignment systematic and scientific process, without bias ~researcher has no prior knowledge of which specific subjects will be selected

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random selection

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process is one in which each element in the population has an equal, independent chance of being selected

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simple random sampling

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most basic probability sampling design

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sampling frame

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technical name for the list of population elements ex: if the nursing students at UT were the accessible pop then a roster would be an example of the sampling frame

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types of probability sampling

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simple random sampling stratified random sampling cluster random sampling systematic random sampling

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stratified random sampling

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population is first divided into two or more strata researchers might sample proportionately or non proportionately

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cluster sampling

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successive random sampling of units example: drawing a sample of nursing schools and from those schools draw a random sample of students many times called multistage sampling

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systematic sampling

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involves the selection of every kth case from a list has a sampling interval

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sampling interval

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standard distance between the selected elements

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sampling error

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refers to differences between population values and sample values eg: average age of the population vs average age of the sample

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sample size

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number of subjects in a sample major issue in conducting and evaluating quantitative research

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power analysis

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a way in which researchers can estimate how large their samples should be to adequately test their research hypotheses

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sample size in Quantitative studies

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generally, with tighter control fewer subjects needed how representative is the sample with small sample sizes, even greater need for replication

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many uncontrolled variables are present small differences are expected on variable of interest population must be divided into sub groups dropout rate among subjects is expected to be high statistical tests are used that require minimum sample sizes

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when should you use larger sample sizes with quantitative research?

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1. description:target pop, eligibility criteria, selection procedures? 2. type of sampling plan? best? 3. method of recruitment and potential bias 4. influencing factors? 5. possible sampling biases identified? 6. key characteristics described? 7. size sufficiently large? justified from power analysis or other rationale? 8. size support external validity?

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to whom can results be generalized?critiquing quantitative sampling plans

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critiquing Qualitative sampling plans

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1. description of setting and or context? appropriate? 2. selection procedures...clear..type? 3. eligibility criteria? recruitment? information-rich? 4. appropriate approach? phenomenon well represented from participants? 5. size: adequate? data saturation indicated? data rich? 6. key characteristics of sample described? to whom might results fit?

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sample size in Qualitative studies

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1. researchers look for good informants 2. no rules about numbers need. 3. typically use small, nonrandom samples 4. convenience and snowball sampling 5.Purposive sampling-often depends on what is learned from the first participants *maximum variation sampling *extreme deviant case sampling *typical case sampling *criterion sampling 6. Theoretical sampling 7. purpose to discover meaning and uncover multiple diverse perspectives. 8. once the researcher has collected data and it begins to repeat itself then the sample is "sufficient" (data saturation)

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data saturation (usually descriptive word for qual studies)

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collected data repeats itself *informants who share and reflect more may reduce the size, or number needed *a narrower interest area requires fewer informants

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maximum variation sampling (qual)

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involves deliberately selection cases with a wide range of variation on dimensions of interest

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extreme (deviant) case sampling (qual)

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provides opportunities for learning from the most unusual and extreme informants (both successful and noted failures)

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typical case sampling (qual)

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involves the selection of participants who illustrate or highlight what is typical or average

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criterion sampling (qual)

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involves studying cases who meet a predetermining criterion of importance

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theoretical sampling (qual)

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method of sampling that is most often used in grounded theory studies. involves decisions about what data to collect next and where to find those data to develop emergent theory optimally

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ethnography sample sizes (qual)

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(culture) -learn about the culture from many -select smaller number of "key informant" once researcher is more knowledgeable about major areas of interest -selects people and thins that help inform researcher about the culture

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Phenomenological sample sizes (qual)

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-very small samples (typical 10 or fewer) -participants who can talk about what it is like to have "lived the experience"

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Grounded theory sample sizes (qual)

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-select informants who can best contribute to the emerging theory (helping to reveal, through reflection, how variables relate to each other and which are important -usually 20-40 people -uses theoretical sampling

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