PSYCH 280 1st Edition Lecture 4Outline of Last Lecture I. Statistical Abusea. Linear propertiesb. Volume propertiesII. Question Effectsa. Question-Order effectb. Number of Alternatives effectc. Assertion-Agreement effectIII. Random SampleIV. Nature of Statistical DistributionsV. CorrelationsOutline of Current Lecture I. Correlations (recap)II. Experimental Methoda. Independent variableb. Dependent variablec. Random assignmentd. Confounding variablese. Statistical significancef. Subject variableIII. Factorial Designsa. Statistical interactionsCurrent LectureI. Correlations (recap)A correlation is a measure of the linear relationship between two variables. The strength of this relationship is quantified by the correlation coefficient, or r. The stronger the correlation between two variables, the farther from 0 the r value is (for example, when r = .8, there is a pretty strong correlation between the two variables. It doesn’t matter if the correlation coefficient is negative or not—the greater the r value is from 0, the stronger the linear relationship between the two variables).These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.Something to remember about correlations, however, is that correlations does NOT mean causation! A correlation does not mean that variable x causes y (or y causes x)—either relationship is a possibility, along with a third: another variable, z, influences both x and y.II. Experimental MethodThe main components of an experiment are:a. Independent Variable: hypothesized causation variable. This variable is manipulated and measured by scientists.b. Dependent Variable: variable that is hypothesized to be affected by the manipulation of the independent variable. This variable is only measured.c. Random Assignment: random assignment of participants to the different conditions of an experiment. Each participant has an equal chance of being placed in each condition.i. This strategy attempts to lessen the differences between groups.d. Confounding: inadvertently creating a difference between the condition groups that could account for differences found in the results/data. This threatens internal validity.e. Statistical Significance: difference between conditions is NOT due to just chance.f. Subject Variables: variable that is NOT manipulated (ex. gender or age) but is measured or a way to select groups.III. Factorial DesignsA factorial designallows researchers to create experiments where two or more variables, each with two or more “levels,” can be crossed (see figure below). A statistical interaction is defined as: “the effect of one variable changes as a function of the level of the other variable.” So, in theexample below, there is statistical interaction: “insult causes more retaliation from men than from women.” In this example, the magnitude of retaliation (effect of the insult) changes with the level (either male of female) of the other
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