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104 Cards in this Set

  • Front
  • Back
Set of all people, objects, or events of interest to the researcher
A variable that divides the population into mutually exclusive segments
e.g., gender, SES, politics
stratum examples
A single member of the population
population element
A subset of the population used in an experiment
A count of all the elements in a population
2 goals of sampling
Maximize external validity, minimize threats to internal validity
If you can specify for each element of the population the probability that it will be included in the sample, you are using a...
...probability sample
Makes representative sampling plans possible
probability sample
Allows investigators to figure out which findings are likely to differ from actual population
probability sample
Can specify size of sample needed if they want a specific degree of certainty
probability sample
A distribution of sample means
sampling distribution
The discrepancy between the sample and the population
sampling error
Specification of the population from which elements are drawn to form a sample
sampling frame
Divide population into strata and take a simple random sample in each subgroup
stratified random sampling
Can oversample for a particular group if you want more statistical precision for that group
stratified random sampling
Representative of both population and key subgroups
stratified random sampling
Divide population into geographic clusters, randomly sample clusters
cluster random sampling
Use when population is spread out
cluster random sampling
Combination of stratified and cluster
multi-stage sampling
Does not involve random selection, there is no way to estimate the probability each element has of being included in the sample
nonprobability sampling
Hard to know whether population is well-represented
nonprobability sampling
e.g., college students, clinical practice samples
examples of convenience sampling
One or more specific groups being sought
purposive sampling
e.g., people in a mall with a clipboard looking for young Caucasian females
purposive sampling
Sampling most frequent or “typical” person
modal instance sampling
Sample of people with known expertise
expert sampling
Select people nonrandomly according to some fixed quota
quota sampling
Represent major characteristics of a population by sampling proportional amount of each characteristic
proportional quota sampling
Specify minimum number of sampled characteristics you want in each category
nonproportional quota sampling
nonproportional quota sampling is similar to...
...stratified sampling
Use when you want to include all views, but it doesn’t matter if they’re presented proportionally
heterogeneity sampling
Opposite of modal instance sampling
heterogeneity sampling
Useful for brainstorming
heterogeneity sampling
Only research that supports causal inferences
randomized experiments
strength of randomized experiments
internal validity
weakness of randomized experiments
lower external validity
People bring them to the study, it’s not possible to manipulate them
individual difference variables
Variables that the experimenter can manipulate or expose people to
experimental variables
e.g., suburban all-boys private school vs. inner city coed public school
examples of confounds
e.g., theft-ice cream sale relationship
example of a third variable
An unintended effect on the DV caused by some feature of the experimental setting, not the IV
Reduces impact of alternative explanations/confounds for effect of IV on DV
random assignment
Used after we have a sample, and before they’re exposed to treatment
random assignment
Compare differences among groups
between-subjects experimental design
Each subject experiences one level of IV
between-subjects experimental design
Both groups get pretest and posttest
Pretest-posttest two group design
Rules out selection and maturation as threats to validity (2 designs)
Randomized two-group design, pretest-posttest two group design
Provides check on history and instrumentation threats (2 designs)
Randomized two-group design, pretest-posttest two group design
Independent measures t-test
Randomized two-group design
Repeated measures t-test
Pretest-posttest two group design
2 controls, 2 experimental groups
Solomon four-group design
One of each gets pretests, one of each does not, all get posttest
Solomon four-group design
Solomon four-group design
2 IVs, presented in combination (X1/Y1, X1/Y2, X2/Y1, X2/Y2)
Between-subjects factorial design
measure differences in subjects over time
Each subject experiences all levels of IV
2 IVs, one within and one between
mixed design
Researcher manipulates something by accident
procedural confounds
Measure does not map onto construct
operational confounds
Preexisting differences between individuals
Selection threat to internal validity
Effects of time on individual
Maturation threat to internal validity
Events that affect the study
History threat to internal validity
Changes in measurement
Instrumentation threat to internal validity
May result from experienced raters, fatigued raters, changes in a survey
Instrumentation threat to internal validity
Participants leave study, maybe at differential rates
Mortality threat to internal validity
Changes in time with the intervention
Selection by maturation threat to internal validity
The degree of resemblance between laboratory operational definitions and some targets/objects outside the lab
mundane realism
The extent to which manipulations or measures are truly perceived in the intended ways by the research participants
experimental realism
What might happen
basic research
controlled setting
basic research
what does happen
applied research
real-life setting
applied research
Concerned with between-treatments variance
experimental research
Derives hypothesis from theoretical premises and tests it
experimental research
Treat everyone the same
experimental research
Try to control for individual difference
experimental research
Goal is to predict variation within a treatment
correlational research
Many factors that may affect DV are free to vary
correlational research
Treat people differently
correlational research
Manipulation “happens” to the subjects
impact studies
e.g., Milgram, Zimbardo
examples of impact studies
Set of conditions is provided and subject makes a judgment
judgment studies
e.g., spousal/family interactions
observational studies
demand characteristics
Personality and situational strength, power of the lab environment
At least one IV is manipulated, but participants are not randomly assigned to all conditions
quasi-experimental design
Nearly impossible to make causal inferences
nonrandomized designs
Groups are nonequivalent before experiment begins
nonrandomized designs
Divide groups by IV, measure each group on DV, control doesn’t have IV
Static-group comparison design
Selection is serious threat to internal validity, temporal precedence hard to establish
Static-group comparison design
Examine several groups at one period
Cross-sectional design
Follow same groups across many measurement periods (longitudinal)
panel design
Examine change over time for same group of people
panel design
Divide on DV, give treatment (IV), measure on DV, control doesn’t get intervention
Pretest-posttest nonequivalent control group design
Selection is a threat, but pretest helps give insight to extent of threat, temporal precedence is clear
Pretest-posttest nonequivalent control group design
Extension of pretest-posttest
Replicated interrupted time-series design
May attempt to match groups to deal with lack of random assignment, makes groups dependent
Pretest matching in quasi-experiments
Doesn’t control for regression toward the mean
Pretest matching in quasi-experiments
Evaluation of process: What is it and how does it work?
Formative evaluation
Evaluation of outcomes: Does it work?
summative evaluation
Do participants find program to be valuable (similar to face validity)
reactions criteria
Do participants learn/understand the information that the intervention is designed to impart?
learning criteria
Do participants change behavior as result of program?
behavioral criteria
Is organization more successful as a result of intervention?
results criteria