Reading...
Play button
Play button
Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
104 Cards in this Set
- Front
- Back
|
Set of all people, objects, or events of interest to the researcher
|
population
|
|
|
A variable that divides the population into mutually exclusive segments
|
stratum
|
|
|
e.g., gender, SES, politics
|
stratum examples
|
|
|
A single member of the population
|
population element
|
|
|
A subset of the population used in an experiment
|
sample
|
|
|
A count of all the elements in a population
|
census
|
|
|
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
|
artifact
|
|
|
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
|
|
|
ANOVA
|
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
|
within-subjects
|
|
|
Each subject experiences all levels of IV
|
within-subjects
|
|
|
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
|
