– Easy to answer and analyse Why use probability sampling techniques? • Less subjective (hence more “scientific”) • Effective in reducing sampling error • Results conducive to statistical analysis • Findings easily generalizable to the whole population 4/4/16 4
When to use non-probability sampling techniques? • Researcher is constrained in time or resources • For exploratory research • Target population is small or difficult to identify • Sampling error is a major concern one-shot case study - A pre-experimental design in which a single group of test units is exposed to a treatment X, and then a single measurement on the dependent variable is taken. one-group pretest–posttest design - A pre-experimental design in which a group of test units is measured twice, once before and once after the treatment.
static group - A pre-experimental design in which there are two groups: the experimental group (EG), which is exposed to the treatment, and the control group (CG). Measurements on both groups are made only after the treatment, and test units are not assigned at random. pretest–posttest control group design - A true experimental design in which the experimental group is exposed to the treatment but the control group is not. Pretest and posttest measures are taken on both groups. Test units are randomly assigned. posttest-only control group design - A true experimental design in which the experimental group is exposed to the treatment but the control group is not and no pretest measure is taken. Posttest measures are taken on both groups. Test units are randomly assigned. time series design - A quasi-experimental design that involves periodic measurements on the dependent variable for a group of test units. Then, the treatment is administered by the researcher or occurs naturally. After the treatment, periodic measurements are continued in order to determine the treatment effect. multiple time series design - A time series design that includes another group of test units to serve as a control group factorial design - A statistical experimental design used to measure the effects of two or more independent variables at various levels and to allow for interactions between variables.
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- Fall '16