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### 104 Cards in this Set

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 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