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Who are the participants?Creating a Quality Sample
47:269: Research Methods I
Dr. Leonard
March 22, 2010
The importance of a research sample
It is rarely possible to study an entire population, or all people the research is focused on, so to be more efficient we draw a sample
We use findings from the sample to infer conclusions about the population
Therefore, the quality of our conclusions about the population depends on how good, or representative our sample is
Selecting an unbiased sample
Ideally, all samples would be without bias, meaning any individual in the population has an equal chance of being in the study
To do this a researcher needs to have access to every single member of the population, which is unlikely
Therefore, most of our samples have built in biases
Bias in sampling
When a sample does not reflect some of the similarities or differences present in the population
Chance (by accident) OR Selection Bias (due to your sampling methods)
Example: I want to test whether or not a new pedestrian assist device will aid blind individuals’ in navigating busy intersections• What is my target population?• What is my accessible population? • How might I select participants to be in the study?
Gold standard: Random Sampling
Increases representativeness of population using PROBABILITY Characteristics of population are known so likelihood of getting
each type of individual can be estimated Each member of the population has an equal chance of being
selected into the sample Each possible sample of a given size (e.g., n=10) has an equal
chance of being selected from the population (N=100)
We want our samples to reflect all of the similarities and differences found in our target populations
Age Gender Ethnicity Political or Religious Affiliation Others specific variables of interest The range of characteristics or variety in the sample is almost
never as big as in the population but it should be close
Simple Random sampling: Each individual has an equal & independent chance of being
selected (like drawing names out of a hat) Stratified Random Sampling:
Divide population into strata, or sub-groups, before randomly selecting participants and then draw representative percentage from each strata
Systematic Sampling: Line up the population, randomly select a starting point, and take
every nth (lets say 10th) person Cluster Sampling:
Imagine being interested if learning about high-school students’ attitudes towards military service. You are interested in collecting a sample of 500 students.
Instead of randomly sampling to get 500 students, list the 30 schools and randomly select 5 schools.
Then test 100 students from each of those schools.
Types of Random/Probability sampling)
Stratified Random SamplingIf we wanted sample to represent the SES
breakdown of a given population…
Consider need for different recruiting methods
Nonprobability Sampling Methods Characteristics of entire population not known Therefore, probability of selecting certain types of individuals
can not be estimated Sampling is nonrandom, but with an effort to maintain
representativeness and avoid bias.
Types of nonprobability sampling Convenience (e.g., Use the next ten people through the
door) Quota (e.g., need a certain number of individuals with a
specific trait in each group) Volunteer (e.g., undergraduate psychology majors) Purposive (e.g., looking for expert informants) Snowball (e.g., hard time finding participants so ask them
to recruit others they know)
Which kind of research is more likely to use random (probability) sampling? Experimental research (Any scientific study in which the
researcher systematically varies one or more variables, holding all others constant, to see if another variable is affected)
Why? Exerts more control than non-experimental Seeks to rule out extraneous variables & confounds More manipulation of independent variable More likely to use groups of participants
Reminders about experimental research
Any scientific study in which the researcher systematically varies one or more variables, holding all others constant, to see if another variable is affected
Intervention made or treatment given to observe effects (causal -- does X cause changes in Y?)
Independent variable must have two or more levels for comparison Most often accomplished by having experimental group(s) and
control group
True experiment if assignment of participants to groups is random
Random Sampling vs. Random Assignment
Random sampling is concerned with every individual in the population having an equal chance of being in the study
Random assignment is concerned with every participant in the sample having an equal chance of being in an experimental group (rules out bias)
True experimental designs (1) Pretest-posttest randomized control group design
Participants randomly assigned to groups Results in two “equal” or equivalent groups
Experimental and control group Both groups tested, or observed, before treatment (pretest) Treatment given (independent variable being manipulated) Both groups tested, or observed, again (posttest) Any pre-post gains or differences between groups are likely
attributable to a) the treatment or b) random error
R O X OR O O
True experimental designs (2) Beware pretest sensitization Sometimes the pretest can
cause problems if it also affects the groups in addition to the treatment
Posttest only randomized control group design Participants randomly assigned to groups Results in two “equal” or equivalent groups Treatment given (independent variable being manipulated) Both groups tested, or observed, again (posttest) Any posttest differences between groups are more likely attributable
to a) the treatment or b) random error
R X OR O
True experimental designs (3) Best of both designs… Solomon randomized four-group design
Still have participants randomly assigned to groups (may need more participants to get equivalent groups)
Like running two experiments at the same time Can test for pre-posttest gains and control for pretest
sensitization
R O X OR O OR X OR O
Problems in True Experimental Designs
What if the observed change is NOT due to the treatment but due to random error? We may find a design lacks validity (just like measures can lack validity)
Experimental validity can be…
Internal - the degree to which an experiment’s methods are controlled and free from confounds
External - the degree to which findings from research can be generalized to the real world context (other populations, other settings, other times???)
Threats to internal validity of experiment Any factors that lessens the degree of control
Pre- and post-test problems History - events that occur outside of the study that may affect
the outcome Maturation - changes within individual during the study that
may affect the outcome Instrumentation - changes in the measures between pre- and
post-tests; could be capturing different construct Testing - any feature of the test or task that could change the
responses the second time; practice effect Statistical regression to the mean - over time, scores tend to
move toward the average
Threats to internal validity of experiment
Participant problems - any problems related to how the individuals participating in the study may challenge the validity of the findings; most common:
Selection effects - participants in samples should be equivalent to each other except for the independent variable but human beings are not clones
Also, selection into the study should be totally random AND each participant should have an equal chance of being assigned to groups
Morality/Attrition - every single participant may not complete the study, which can be especially problematic if a certain type of participant is more likely to drop out