1. 12 Experimental Control and Internal Validity What are the potential threats to the validity of research? What is experimental control? What effect do extraneous variables have on the validity of research? What is meant by confounding? Why does confounding reduce an experiment’s internal validity? What are some methods of controlling for extraneous variables in experimental designs? What are some methods for increasing the validity of experimental manipulation? What are manipulation checks and confound checks, and what can they tell us? What are some common artifacts in experimental research, and how can they produce confounding?

2. Threats to the Validity of Research Threats to construct validity Threats to statistical conclusion validity Threats to internal validity Threats to external validity Although it is claimed that the measured variables and the experimental manipulations relate to the conceptual variables of interests, they actually may not. Conclusions regarding the research may be incorrect because a Type 1 or Type 2 error was made. Although it is claimed that the independent variable caused change in the dependent variable, the changes in the dependent variable may have actually been caused by a confounding variable. Although it is claimed that the results are more general, the observed effects may actually only be found under limited conditions or for specific groups of people.

3. Experimental Control Extraneous Variables Confounding Variables Variables other than the independent variable that cause changes in the dependent variable. Example: participants’ ability, mood, motivation, and differences in how the experimenter treats the participants or how they react to the experimental setting. Variables other than the independent variable on which the participants in one experimental condition differ systematically or on average from those in other conditions. Example: Room sizes, Speed of computers, and number of participants.

4. Control of Extraneous Variables Limited-Population Designs Before-After Designs Matched-Group Designs Standardization of Conditions Experimental manipulation in which all participants in all levels of the independent variable are treated in exactly the same way. Examples: Script, Protocol, and Automated Experiments Experimental designs in which participants are sampled from a limited population so as to standardize participants’ background. Example: College Students. Experimental designs in which participants are at first asked to complete baseline measure and then are assigned to a experimental condition. Example: Word Memory Experimental designs in which participants are measured on the variable of interest before the experiment begins and then are assigned to conditions on the basis of their scores on that variable. Example: IQ test.

5. Creation of Valid Manipulations Impact: The manipulation which creates the hoped-for changes in the conceptual variables. Experimental Realism: The extent to which the experimental Manipulation involves the participants in the research. Manipulation Checks Confound Checks Measures that determine whether the experimental manipulation has had the intended impact on the conceptual variables of interest. Measures that determine whether the manipulation has unwittingly Caused differences on confounding variables. Pilot Testing

6. Threats to Internal Validity Placebo Effects Demand Characteristics Participant’s expectations about experiments, which influence the dependent measure independently of the actual effect of manipulation. <>Giving the same instruction to participants in control and in experiment conditions. Aspect of the research that allows participants to guess the research hypothesis. <> Cover stories, Unrelated-experiments technique, Non reactive measures. Experimenter Bias Experimenters influence on the dependent measures when they know the research hypotheses and which condition the participants they are running are in. <> Naïve Experimenters and Blind Experimenters