Correlational Designs Causal Modeling Quasi-Experimental Designs

How do quasi-experiments differ from actual experiments? Correlational Designs Quasi, in Latin, means “seeming like.” Quasi- experiments superficially resemble experiments, but lack their required manipulation of antecedent conditions and/or random assignment to conditions.

How do quasi-experiments differ from actual experiments? Correlational Designs They may study the effects of preexisting antecedent conditions—life events(living through 9/11) or subject characteristics (having autism)— on behavior. A quasi-experiment might compare the incidence of Alzheimer’s disease in patients who used ibuprofen since age 50 and those who did not.

How do quasi-experiments differ from actual experiments? Correlational Designs In experiments, researchers randomly assign subjects to antecedent conditions that they create. An experiment might randomly assign subjects to either daily ibuprofen or aspirin use, and then measure their incidence of Alzheimer’s.

When should we use quasi-experiments instead of experiments? Correlational Designs We should use quasi-experiments when we cannot or should not manipulate antecedent conditions. Quasi-experiments could study the effect of spouse abuse on the frequency of child abuse.

Problems with Quasi Cannot establish cause with certainty (cannot be sure that spousal abuse caused the child abuse) Lacks internal validity, that is, the ability to conclude with confidence that the antecedent condition caused the observed differences in behavior. However is higher in external validity, or generalizability, than lab experiments. Are low in manipulation of antecedents, but high in imposition of units.

Correlations Correlations are used to establish relationships among preexisting variables. Can be used to predict one set of behaviors from another, for instance, can predict college grades based on high school grades. Shows the relationship between antecedent conditions and behavioral effects but the antecedents are preexisting, not manipulated. Low manipulation of antecedents, high imposition of units. Cannot be sure of cause. Has poor internal validity but good external validity

Correlations A correlational study is one that is designed to determine the correlation, or degree of relationship, between two traits, behaviors, or events. When two things are correlated, changes in one are associated with changes in the other. In a correlational study, selected traits or behaviors of interest are measured first. Numbers or scores are recorded that represent the measured variable. Next the degree of relationship, or correlation, between the numbers is determined through statistical procedures.

Correlations One a correlation is know it can be used to make predictions. If we know a person’s score on one variable, we can make a better prediction of that person’s score on another measure that is highly related or correlated to it. The higher the correlation, the more accurate the prediction will be.

Correlation example Suppose a researcher wonders if there is a relationship between watching Sesame Street and vocabulary in preschoolers. Might ask parents to list as many words that their preschoolers know and ask how frequently they watch Sesame Street. One variable is hours per week spend watching Sesame Street, other variable is the number of words the child knows. Then you would take these numbers, or data, and run a statistical procedure for all of the children in your study.

Calculating correlations One statistic that is used to calculate a correlation is called the Pearson Product Moment Correlation Coefficient or (r). When r is computed, there are three possible outcomes. The correlation can be positive, negative or no relationship.

Calculating Correlations The values of a correlation coefficient can only range between -1.0 and The sign, + or -, tells us whether the relationship is positive or negative. The absolute value of r tells us the strength of the relationship or +.16 Which is stronger?

Describe the properties of a correlation. Correlational Designs A Pearson correlation coefficient is used to calculate simple correlations (between two variables) and may be expressed as: r (50) = +.70, p =.001. Correlation coefficients have four properties. linearity, sign, magnitude, and probability.

Scatterplot A scatterplot can be created to demonstrate the direction of a correlation. It is often the first step in analyzing the correlation. Each dot stands for a person’s scores. Each person has 2 scores.

Scatterplot Can draw a line through the scatterplot. These lines are called regression lines or lines of best fit. The direction of the line demonstrates the direction of the correlation.

Positive correlation If the r value is positive, then there is a positive correlation between the variables. As one variable increases, the other increases too. Also, as one variable decreases, the other decreases too. As hours of Sesame Street viewing increase, vocabulary increases. As hours of Sesame Street viewing decrease, vocabulary decreases. The absolute value of r tells how strong the relationship is. The closer it is to 1.00, the stronger it is. Strong relationships allow for good prediction.

Negative correlation If the r value is negative, then there is a negative correlation between the variables. This is called an inverse relationship. As one variable increases, the other decreases. As hours of Sesame Street viewing increase, vocabulary decreases. The absolute value of r tells how strong the relationship is. The closer it is to 1.00, the stronger it is. Strong relationships allow for good prediction.

No relationship If the absolute value of correlation is close to 0, then there is no relationship between the variables. Sesame Street viewing has no effect on vocabulary.

Curvilinear relationships Sometimes a correlation coefficient value can be close to zero and it would appear that there is no relationship between the variables. However, there may be a curvilinear relationship which can be demonstrated by the scatterplot.

Curvilinear relationship

Describe the properties of a correlation. Correlational Designs Linearity means how the relationship between x and y can be plotted as a line (linear relationship) or a curve (curvilinear relationship). Sign refers to whether the correlation coefficient is positive or negative.

Describe the properties of a correlation. Correlational Designs Magnitude is the strength of the correlation coefficient, ranging from -1 to +1. Probability is the likelihood of obtaining a correlation coefficient of this magnitude due to chance.

What does a scatterplot show? Correlational Designs Scatterplots are a 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.

How do outliers affect correlations? Correlational Designs Outliers are extreme scores. They usually affect correlations by disturbing the trends in the data.

Causation Correlation does not imply causation. Even a perfect correlation, if it exited, does not indicate a causal relationship. Even though a strong relationship exists between two variables, we cannot say that one cause the other. There are other possible variables that were not measured, that could have caused the effects.

Causation Research on firmness of handshakes and positivity of first impressions found a positive correlation. However, isn’t it possible that people who shake firmly are very extraverted and it is their extraversion that creates the good impression? A positive correlation exists between the number of cars built and the numbers of airplanes built, but one doesn’t cause the other.

Why should we compute the coefficient of determination? Correlational Designs Once we calculate r, we can then calculate the coefficient of determination. The coefficient of determination (r 2 ) estimates the amount of variability that can be explained by a predictor variable. It is an estimate of strength. If r is.56 then r2 is.31. For example, Chaplin et al. (2000) showed that handshake firmness accounted for 31% of the variability of first impression positivity.

Why doesn't correlation prove causation? Correlational Designs Since correlational studies do not create multiple levels of an independent variable and randomly assign subjects to conditions, they cannot establish causal relationships.

Why doesn't correlation prove causation? Correlational Designs There are three additional reasons that correlations cannot prove causation: (1) casual direction- in a correlation, we cannot be sure which variable is the cause and which is the effect (2) bidirectional causation- both variables could cause the other variable (3) the third variable problem- there could be some other variable that is the cause that we have not measured

Why doesn't correlation prove causation? Correlational Designs Causal direction Since correlations are symmetrical, A could cause B just as readily as B could cause A. Does insomnia cause depression or does depression cause insomnia?

Why doesn't correlation prove causation? Correlational Designs Bidirectional causation Two variables—insomnia and depression— may affect each other.

Why doesn't correlation prove causation? Correlational Designs Third variable problem A third variable—family conflict—may create the appearance that insomnia and depression are related to each other.

Linear regression analysis When two behaviors are strongly related, the researcher can estimate a score on one of the measured behaviors from a score on the other. This technique is called linear regression analysis. If we knew that time watching tv was correlated strongly with scores on vocabulary test, we could substitute someone’s viewing time into an equation for the regression line which would give us an estimate of what that person’s performance should be on the vocabulary test.

When do researchers use multiple regression? Correlational Designs When more than two related variables are correlated, a multiple regression can be used. Researchers use multiple regression to predict behavior measured by one variable based on scores on two or more other variables. We could estimate vocabulary size using age and television watching as predictor variables.

Quasi Experimental Designs Lacks important elements of experiments, such as manipulation of antecedents or random assignment to treatment conditions. Quasi experimental designs don’t look for relationships between variables like correlations; instead we are comparing different groups of subjects looking for differences between them, or looking for changes over time in the same group of subjects. With quasi experimental designs we never know for sure what causes the effects that we see. Therefore, they are low in internal validity.

What is an ex post facto design? Quasi-Experimental Designs Ex post facto means “after the fact.” A researcher examines the effects of already existing subject variables (like gender or personality type), but does not manipulate them. For example, a researcher wants to study women who are divorced to see if they are more pessimistic about marriage than women who are not divorced and who are single. The divorce is the preexisting variable or subject variable.

Ex post facto Like correlations, there is no cause. So cannot say that divorce causes changes in attitudes. There could have been a third variable which caused the result. Ex post facto studies are low in internal validity.

What is a nonequivalent groups design? Causal Modeling A nonequivalent groups design compares the effects of treatments on preexisting groups of subjects. A researcher could install fluorescent lighting in Company A and incandescent lighting in Company B and then assess productivity. Cannot be sure that the lighting made the difference; it could be that one company is threatening layoffs, so workers are being more diligent. Also has low internal validity.

Describe the longitudinal and cross-sectional approaches. Causal Modeling In longitudinal designs, the same group of subjects is measured at different points of time to determine the effect of time on behavior. In cross-sectional studies, subjects at different developmental stages (classes) are compared at the same point in time.

What is a pretest/posttest design? Causal Modeling In pretest/posttest designs, a researcher measures behavior before and after an event. This is quasi-experimental because there is no control condition. For example: Practice GRE test 1  six-week preparation course  Practice GRE test 2.

Which problems reduce its internal validity? Causal Modeling 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 posttest and learning caused by review of pretest answers. Practice effects – people do better the second time they take an intelligence test, even when there is no special training in between.

What is a Solomon 4-group design? Causal Modeling This variation on a pretest/posttest design includes four conditions: (1) a group that received the pretest, treatment and posttest (2) a nonequivalent control group that received only the pretest and posttest

What is a Solomon 4-group design? Causal Modeling (3) a group that received the treatment and a posttest (4) a group that only received the posttest