What to Measure Sampling and generalizability  Population vs. sample  Sampling techniques – procedures for deciding which examples of the population you will measure  Generalization and representative  To generalize from sample to population, need to know if sample is representative of the population

What to Measure, con’t Types of samples  Non-probability sampling  Haphazard or convenience sampling  Self-selected sample  Quota sampling  Probability sampling  Sometimes called random sampling  Simple random sampling  Systematic sampling  Stratified sampling  Cluster sampling

What to Measure, con’t Sample size  Probability sampling might not be representative if the sample size is not large enough  How large is large enough?

Goals of Experimental Research Causation  What is causation?  To show causation, you must have three things:  Effect did not come before cause  Change in 1 st thing related to change in 2 nd thing  Can be shown by a correlation  Nothing else could have caused change in 2 nd thing  Cannot be shown by a correlation  Eliminate rival hypotheses

Goals of Experimental Research Experimental overview  1 st stage  Sampling  2 nd stage  Divide samples into groups  3 rd stage  Manipulate groups according to experimental design  4 th stage  Measure results in each group

Type of variables Independent variables  Two different groups  Control group  Experimental group Dependent variables Extraneous variables  Subject variables  Experimenter variables  Situational variables  Confounding variables

Complex Experimental Designs Three or more groups Factorial designs  Main effects  Interaction effects Multivariate designs

Developmental Designs Longitudinal designs  Test the same sample at least twice across some time period  When does repeated tested become longitudinal?  Problems with longitudinal designs  Test obsolescence  Issues related to sampling  Cohort effects  Subject attrition  Repeated psychological testing  Advantages of longitudinal designs  Sampling: Age diffs vs. age changes  Examine any cross-age pattern  Trace transformations underlying behavior

Developmental Designs Cross-sectional designs  Test different people at different ages  Tests age changes as opposed to age differences  Problems with cross-sectional designs  Selection bias  Subject attrition  Confounding of age and generation of cohort  Measurement equivalence