©2005, Pearson Education/Prentice Hall CHAPTER 5 Experimental Strategies
©2005, Pearson Education/Prentice Hall Characteristics of True Experiments 1.Manipulation of the independent variable to see the effect on the dependent variable. 2.Comparison of the dependent variable results in at least two conditions (experimental and control group) Experimental Group: Receives the manipulation of the independent variable. Control Group: Do not receive the manipulation of the independent variable (everything else is held constant). 3.Randomly assigns (or equates) participants to the experimental and control conditions before the manipulation of independent variable. 4.Control of the unwanted effects of extraneous variables on the dependent variable.
©2005, Pearson Education/Prentice Hall Crown Jewel of Designs The experimental design is the considered the crown jewel of designs because if done properly it allow one to draw a: –Causal Inference: a conclusion where one variable or event causes another. Also called a cause-effect conclusion. Cause-effect conclusion can be made if all of the following criteria are met: 1.Covariation of variables 2.Causal time sequence 3.Elimination of all other plausible causes.
©2005, Pearson Education/Prentice Hall Systematic Variance Since the different levels of the independent variable form the groups in an experiment, it is expected that it should produce variance between the different groups (if it did not there would be no experimental effect) –This intended variance is called the systematic variance.
©2005, Pearson Education/Prentice Hall Unwanted Sources of Variance: Systematic and Nonsystematic Sometimes extraneous variables that the researcher was unaware of can influence the dependent variable. –If the extraneous variable affects the data consistently in one research condition more than another it is considered systematic variance from an extraneous variable. Threatens Internal Validity: Degree to which the changes in the dependent variable were caused by independent variable. May increase separation of mean scores –E.g., Nonequivalent groups. –If the extraneous variable affects the data randomly (increases and decreases scores) in any research condition it is considered to be nonsystematic or “error” variance. Most error variance comes from individual differences among the participants. Measurement error is another source of nonsystematic variance. Does not pull group means apart.
©2005, Pearson Education/Prentice Hall Controlling Unwanted Systematic Error: Random Creating and Maintaining Equal Groups –Random assignment Each participant has an equal chance of being selected to experimental or control condition. There are various methods to achieve this: –Simple Random Assignment (Lottery method, e.g., random number generator) –Block random assignment –Random distribution method Does random assignment always create equivalent groups? Often called randomized group designs.
©2005, Pearson Education/Prentice Hall Matching participants involves selecting participants who are very similar on one or more variables (the matching variable) and then making sure equal numbers of these matched-participants end up in the various conditions. The condition they end up in is usually determined randomly. Although this seems like the ideal way to equate the groups it isn’t. Choosing the matching variable(s) is tough! And explaining why you choose some over others can be complicated. –Because of the above difficulties, matching is usually only done: When there small number of participants When a researcher wants to ensure a particularly important variables is evenly distributed across groups. Controlling Unwanted Systematic Variance: Matching
©2005, Pearson Education/Prentice Hall Repeated measures designs test the same participants in the different conditions of the independent variable. –They are also called within-subjects (or with- participant designs). Within-participant designs eliminate the need for random or match assignment of participants. Why? –The participants are in all levels of the independent variable – thus, they are their own control group and no assignment to groups is required. Controlling Unwanted Systematic Variance: Repeated Measurement
©2005, Pearson Education/Prentice Hall Control Against Differential Attrition Attrition refers to a loss of participants during the course of the study. Differential attrition refers to the situation when participants are leaving one independent condition at a higher rate than another. How can researchers guard against differential attrition? –Use of pretest screening –Equate the groups later – drop a score in the other condition that is similar to the one lost to attrition. Why do these approaches reduce external validity but preserve internal validity?
©2005, Pearson Education/Prentice Hall Preventing Pre- and Posttesting Problems Studies that gather data before the experimental manipulation and then again after the manipulation (pre – posttest method) have unique problems that should be addressed: –History: experiences that occur during a study that may affect the dependent variable but are not part of the independent variable. –Maturation: a principle that states that both physical and psychological changes, due to genetics, occur over time. –Regression to Mean: tendency of individuals who initially scored very well or very poorly to score more toward the middle when reassessed. –Testing Changes: changes in the way assessment is done from the pretest to the posttest. Or changes in the participant from the first to second testing. –Sensitization: the pretest trial changes the participant so they perform differently on the posttest. The best way to control all of the above factors is to include randomly assigned or matched control conditions in the experimental design.
©2005, Pearson Education/Prentice Hall Eliminating Bias There are 2 main sources of bias: 1.Participant Bias or Demand Characteristics Cues or feature in an experiment that may lead a participant to respond in a particular fashion. Note: Participants often behave the way they perceive the experimenter wants them to behave, often called the good- participant effect 2.Experimenter Bias (Rosenthal Effect) The experimenter knows how participants are suppose to respond and may unwittingly coach them to respond in a particular way. The best way to deal with both of the above biases is to utilize a “Double-Blind” procedure. –Neither the participants nor the experimenter knows who is in each experimental condition.