Types of Relationships • Social scientists are interested in discovering functional relationships between variables. • In particular, researchers look for: – correlations (association, covariation) among vaariables – differences between groups or conditions The nature of causation • Cause-effect relationships--causation is always inferred, never directly observed • “functional” relationships – one thing correlates with, or is associated with another (correlation) – one thing predicts or explains the amount of variance in another (analysis of variance) – one thing has a direct effect on another (path analysis, multiple regression) Graphic Representations of Relationships (dependent variable) Y-axis (independent variable) X-axis Note: “3/4 rule” the convention is to make the Y axis 3/4 of the length of the x axis Correlations • displaying correlations using a scattergram • linear relationship – can be positive or negative • curvilinear relationship – also known as nonmonotonic relationships, quadratic trends, “u-shaped” or “inverted-u” – requires a minimum of three levels of the variable being investigated • no correlation • spurious effect Do employees who drink a lot of coffee take more bathroom breaks? frequency table employee cups of coffee consumed bathroom trips Fifi 1 2 Rex 2 1 Trudy 3 3 Pedro 3 4 Lulu 4 6 Thaddeus 5 5 Rudolfo 6 5 scatterplot Illustration of Scatterplots • Scatterplots that are closer to a straight line have correlations closer to +1.0 or -1.0 • Must have interval or ratio data • Correlation does not prove causation Linear versus curvilinear relationships Linear relationship Curvilinear relationship Differences Between Groups or Conditions • main effect (changes produced by one independent variable alone) – one-way interaction • interaction effect (changes produces by independent variables acting together, or in concert – two-way interaction – three-way interaction interpersonal touch, social labeling, and the foot-in-the-door effect touch no touch positive FITD .15 .40 negative FITD .45 .25 communicator physical attractiveness and persuasion attractive criminal swindler burglar unattractive criminal 5.45 4.35 2.80 5.20 non-significant-interaction A characteristic feature of non-significant interaction effects is that the lines are parallel, or nearly parallel potentially significant interaction (ordinal) potentially significant interaction (disordinal or “crossed”) potentially significant interaction (ordinal) Illustration of an interaction effect 2 3 3 6 Attitude change 0 1 2 3 4 5 6 7 Source Credibility low high evidence quality low high high quality evidence low quality evidence low high Source Credibility COLD ROOM 500 HOT ROOM 900 HARD TEST 60% 60% EASY TEST 60% Test score 80% 0 10 20 30 40 50 60 70 80 90 100 Illustration of an interaction effect Easy test Hard test 500 900 Overlapping normal distributions • Which distribution has the higher mean? Which has the higher standard deviation? Nonnormal distributions • Positively skewed, negatively skewed , or a normal distribution? What kind of correlation? • Are people in richer nations happier??