EXPE Chapter 5: Alternatives to Experimentation Correlational and Quasi-Experimental Designs Flashcards
“seeming like”
Quasi
superficially resemble experiments, but lack their required manipulation of antecedent conditions and/or random assignment to conditions.
Quasi-experiments
is used to calculate simple correlations (between two variables)
Pearson correlation coefficient
how the relationship between x and y can be plotted as a line (linear relationship) or a curve (curvilinear relationship).
Linearity
refers to whether the correlation coefficient is positive or negative.
Sign
the strength of the correlation coefficient, ranging from -1 to +1.
Magnitude
the likelihood of obtaining a correlation coefficient of this magnitude due to chance.
Probability
a graphic display of pairs of data points on the x and y axes
Scatterplots
an artificial restriction of the range of X and Y that can reduce the strength of a correlation coefficient.
Range truncation
are extreme scores. They usually affect correlations by disturbing the trends in the data.
Outliers
estimates the amount of variability that can be explained by a predictor variable.
coefficient of determination (r2)
when we want to hold one variable (age) constant to measure its influence on a correlation between two other variables (television watching and vocabulary).
partial correlation
when they want to know whether there is a relationship among three or more variables
multiple correlation (R)
predict behavior measured by one variable based on scores on two or more other variables.
multiple regression
the creation and testing of models that suggest cause-and-effect relationships between behaviors
Causal modeling
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.
path analysis
a researcher measures relationships over time and these are used to suggest a causal path.
cross-lagged panel design
“after the fact.” A researcher examines the effects of already existing subject variables (like gender or personality type), but does not manipulate them.
Ex post facto
design compares the effects of treatments on preexisting groups of subjects.
nonequivalent groups
a researcher measures behavior before and after an event. This is quasi-experimental because there is no control condition
pretest/posttest designs
receives a different level of the IV (no preparation course).
control group
(also called pretest sensitization) due to less anxiety during the posttest and learning caused by review of pretest answers
practice effects
Solomon 4-group design
(1) a group that received the pretest, treatment and posttest
(2) a nonequivalent control group that received only the pretest and posttest
(3) a group that received the treatment and a posttest
(4) a group that only received the posttest
