Research Methods Experimental Method only method that can establish “cause and effect” important components include: random sampling random assignment hypothesis control and experimental groups independent variable and dependent variable control of subject and experimenter bias

Control The ability to establish cause and effect with experiments is due to the degree of control possible in true experiments. There are three major means by which control is achieved: Manipulation of the independent variable - altering a condition of the experiment believed to influence the dependent variable. Constancy of conditions - maintaining conditions that may influence behavior at levels we choose or believe them to be (e.g., all Ss tested at or about the same time of day). Elimination of extraneous variables - identifying those variables which may influence behavior and exclude them from the research situation (e.g., environmental distractions).

Independent Variables An independent variable is one under the direct control of the experimenter (e.g., level of background white noise) or, if not under direct control, has been selected for inclusion in the experiment (e.g., gender) and is believed to influence behavior in a predictable manner. Independent variables are manipulated in a variety of ways: present different stimuli (e.g., complex vs. simple designs, drug dose, highlighter vs. no highlighter) vary content in scenarios (e.g., three vs. five “friends”) vary characteristics of “paper people” (e.g., male vs. female) etc.

Dependent Variables A dependent variable is a direct or indirect measure of behavior or mental process. There a several categories of dependent variables: frequency of response length of response strength of response number of correct responses/number of errors reaction time (RT) rating scales test scores

Threats to Internal Validity We have seen how manipulation of the independent variable functions in research. The other two means of control are directed toward establishing internal validity - ensuring all plausible rival hypotheses are eliminated (i.e., “confounding”). There are a number of common threats to internal validity: history - specific events (extraneous variables) occurring between presentation of the independent variable and measurement of the dependent variable or between a first and second measurement (i.e., pretest-posttest), other than the independent variable. maturation - processes within the Ss that change as a function of the passage of time (e.g., growing older, more hungry, tired, etc.).

Threats to Internal Validity testing - the effects of taking a test upon the scores of a second test. instrumentation - changes in calibration of measuring instruments or changes in scorers/observers. differential selection - groups selected in such a way that may result in group biases (e.g., one group is more intelligent, more motivated, better educated, etc.). experimental mortality - differential loss of Ss from treatment groups (e.g., illness, time constraints, death, etc.).

Pre-Experimental Designs There are many instances in which true experiments are not possible. In those cases, pre-experimental or quasi-experimental designs are sometimes used. There are, however, threats to internal validity in each that must be acknowledged. The following are three pre-experimental designs: One-Group Pretest- Posttest Design O X O Static-Group Comparison X O O One-Shot Case Study X O

Basic True Experimental Design Ideally, we would like to have a small group or “sample” to study which represents the entire population. That can be accomplished if we use “random sampling” to select our sample. Population Random Sampling Random Sample

Basic True Experimental Design Unfortunately, we are seldom able to obtain such a sample. We must, therefore, often rely on using subjects who are readily available -- a “convenience sample” -- and split them into a minimum of two groups using a technique called “random assignment.” Convenience Sample Random Assignment Experimental Group Control Group

Basic True Experimental Design Present Independent Variable Control Group Experimental Group Measure Dependent Variable Compare Groups

Basic Experimental Design Have you ever wondered whether those “Highlighters” help you study? Let’s see how we could develop an experiment to test the following hypothesis: Highlighters facilitate memory of facts read from textbooks. All subjects will be given several pages to read. After they have done so, they will be dismissed and asked to return to the experimental lab the next day. Convenience Sample: Students in Gen. Psych. class Random Assignment Control Group Experimental Group

Basic Experimental Design Present Independent Variable Availability of Highlighter No Highlighter Highlighter Available Control Group Experimental Group Measure Dependent Variable Number of correctly recalled facts on quiz Each subject is given five pages from an Intro Psych text and told to read the pages carefully because they will be tested on the material. The subjects are dismissed after they finish reading and asked to return the next day. Compare Groups

Expanding the Basic Design The basic experimental design is limited to comparing two groups. We can, however, increase its versatility by using “factorial designs.” Factorial designs use a slightly different terminology than what we have been using: Factor - an independent variable Levels - different values of the independent variable Treatment condition/group - a group exposed to one of the levels of the independent variable

Simple-Randomized Design Factor A a1 a2 a3 Levels S1 . n1 n2 n3

Between- vs. Within-Subjects A a1 a2 a3 S1 . n1 n2 n3 A a1 a2 a3 S1 . n

Two-Factor Designs (completely crossed designs) a1 a2 a3b1 a3b2 a3 b1 B b2 a1b1 a2b1 a1b2 a2b2 This is simply a combination of two simple-randomized designs.

Mixed Designs Within-subject Factor A a1 a2 a3 Between-subject Factor . n b1 B b2 S1 . n

Higher-Order Factorial Designs a1b1c2 b1 B b2 a3b2c1 c2 c1 C a1 a2 a3 A

Main Effects and Interactions In addition to investigating several variables at one time to test for main effects, factorial designs permit us to investigate interactions as well. Main effect - a significant influence of a factor on the dependent variable. Interaction - the simultaneous effects of two factors on the dependent variable that may be different than the effects of the factors individually.

Graphing Main Effects A a1 a2 b1 B b2 Main effects for Factors A and B Main effect for Factor B Main effect for Factor A a1 a2 = b1 = b2

Graphing Interactions a1 a2 b1 B b2 Disordinal interaction Ordinal interaction a1 a2