midterms

• Quasi


  Seeming like
• Quasi-experiments


  • Superficially resemble experiments, but lack their required manipulation of antecedent conditions and/or random assignment to conditions
  • May study the effects of preexisting antecedent conditions-life events or subjects characteristics-on behavior
• We should use quasi-experiments when we cannot or should not manipulate antecedent conditions
• Pearson correlation coefficient


  Used to calculate simple correlations (between two variables) and may be expressed as: r(50) = +.70, p = .001
• Properties of correlation coefficients

  • Linearity
  • Sign
  • Magnitude
  • Probability
• Linearity


  How the relationship between x and y can be plotted as a line (linear relationship) or a curve (curvilinear relationship)
• Sign


  Whether the correlation coefficient is positive or negative
• Magnitude


  The strength of the correlation coefficient, ranging from -1 to +1
• Probability


  The likelihood of obtaining a correlation coefficient of this magnitude due to chance
• Scatterplots


  Graphic display of pairs of data points on the x and y axes
• A scatterplot illustrates the linearity, sign, magnitude, and probability (indirectly) of a correlation
• Range truncation


  An artificial restriction of the range of X and Y that can reduce the strength of a correlation coefficient
• Outliers


  Extreme scores that usually affect correlations by disturbing the trends in the data
• Coefficient of determination (r^2)


  Estimates the amount of variability that can be explained by a predictor variable
• Chaplin et al. (2000) showed that handshake firmness accounted for 31% of the variability of first impression positivist
• Correlation studies do not create multiple levels of an independent variable and randomly assign subjects to conditions, so they cannot establish causal relationships
• Reasons correlations cannot prove causation

  • Causal direction
  • Bidirectional causation
  • The third variable problem
• Causal direction


  Since correlations are symmetrical, A could cause B just as readily as B could cause A
• Bidirectional causation


  Two variables-insomnia and depression-may affect each other
• Third variable problem


  A third variable-family conflict-may create the appearance that insomnia and depression are related to each other
• Multiple correlation (R)


  Researchers use it when they want to know whether there is a relationship among three or more variables
• Partial correlation


  We should compute it when we want to hold one variable (age) constant to measure its influence on a correlation between two other variables (television watching and vocabulary)
• Multiple regression


  Used to predict behavior measured by one variable based on scores of two or more other variables
• We could estimate vocabulary size using age and television watching as predictor variables
• Causal modeling


  The creation and testing of models that suggest cause-and-effect relationships between behaviors
• Forms of causal modeling

  • Path analysis
  • Cross-lagged panel designs
• Path analysis


  A researcher creates and tests models of possible causal sequences using multiple regression analysis where two or more variables are used to predict behavior on a third variable
• Cross-lagged panel design


  A researcher measures relationships over time and these are used to suggest a causal path
• Ex post facto


  Means "after the fact" - a researcher examines the effect of already existing subject variables (like gender or personality type), but does not manipulate them
• Nonequivalent groups design


  Compares the effects of treatments on preexisting groups of subjects
• Longitudinal designs


  The same group of subjects is measured at different points of time to determine the effect of time on behavior
• Cross-sectional studies


  Subjects at different developmental stages (classes) are compared at the same point in time
• Pretest/posttest design


  Researcher measures behavior before and after an event. This is quasi-experimental because there is no control condition
• There is no control group which receives a different level of the IV (no preparation course)
• The results may be confounded by practice effects (also called pretest sensitization) due to less anxiety during the post-test and learning caused by review of pretest answers</b>
• Variation on pretest/posttest design

  Includes four conditions: 1) Pretest, treatment, posttest 2) Pretest, posttest only 3) Treatment, posttest 4) Posttest only
• Hypothesis


  An explanation of a relationship between two or more variables
• Types of hypotheses


  • Experimental hypothesis
  • Nonexperimental hypothesis
• Experimental hypothesis


  A tentative explanation of an event or a behavior. It is a statement that predicts the effect of an independent variable on a dependent variable
• Nonexperimental hypothesis


  Predicts how variables (events, traits, or behaviors) might be correlated, but not causally related