Experimental Data Collection
When conducting an experiment, one needs to take into account many elements: the design (variables, conditions, manipulations), pretesting and pilot testing (to avoid costly errors), and data interpretation (e.g. power statistics, experimenter effects). Laboratory experiments are useful for investigating predictions derived from explicit theories. A lab experiment provides researchers with a more controlled environment that allows to isolate variables and understand cause-and-effect relationships. When collecting data, the researcher mainly controls which subjects are assigned to which groups, which treatments (or non-treatment) each groups receives, and in which order. Random allocation and counterbalancing are key features of experimental designs, and are used to avoid problems of endogeneity, experimenter effects, or participant effects. Other elements that need to be taken into account when designing an experimental data collection are situational variables or demand characteristics, as well as whether you will have pre-test and post-test measures of the dependent variables, or whether participants will go through the experimental manipulation (i.e. independent variable) more than once (between subjects or within subjects design). If you are thinking about conducting an experiment and are looking for advice in this area, check out the website of our lab or contact us at [email protected].