chap 5

quasi

quasi

means “seeming like”

quasi-experiments

• superficially resemble experiments, but lack their required manipulation of antecedent conditions and/or random assignment to conditions.

• They may study the effects of preexisting antecedent conditions on behavior

pearson correlation coefficient

is used to calculate simple correlations (between two variables) and may be expressed as: r (50) =+.70, p = .001

1. linearity

2. sign

3. magnitude

4. probability

correlation coefficients have four properties

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

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.

scatterplots

• are a graphic display of pairs of data points on the x and y axes.

• illustrates the linearity, sign, magnitude, and probability (indirectly) of a correlation.

range truncation

is an artificial restriction of the range of X and Y that can reduce the strength of a correlation coefficient

outliers

• are extreme scores.

• They usually affect correlations by disturbing the trends in the data.

• Range truncation removes ——

coefficient of determination (r2)

estimates the amount of variability that can be explained by a predictor variable

1. causal direction

2. bidirectional causation

3. the third variable problem

There are three additional reasons that correlations cannot prove causation:

causal direction

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

bidirectional causation

Two variables (for example—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 —- when they want to know whether there is a relationship among three or more variables.

partial correlation

We should compute a —- 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

• Researchers use — to predict behavior measured by one variable based on
  scores on two or more other variables.

• ex. We could estimate vocabulary size using age and television watching as predictor variables

causal modeling

is the creation and testing of models that suggest cause-and-effect relationships between behaviors

path analysis and cross-lagged panel designs

two forms of causal modeling

path analysis

in —, 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

in —-, 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 effects 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.

• ex. A researcher could install fluorescent lighting in Company A and incandescent lighting in Company B and then assess productivity

longitudinal designs

in —-, the same group of subjects is measured at different points of time to determine the effect of time on behavior

cross-sectional studies

in —-, subjects at different developmental stages (classes) are compared at the same point in time.

pretest/post test 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.

practice effects

The results may be confounded by —- (also called pretest sensitization) due
to less anxiety during the posttest and learning caused by review of pretest answers.

Solomon 4-group design

This variation on a pretest/posttest design include these conditions:

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