2005, Pearson Education/Prentice Hall CHAPTER 6 Nonexperimental Strategies

©2005, Pearson Education/Prentice Hall

CHAPTER 6

Nonexperimental Strategies


Types of NonExperimental Strategies

• There are 3 types of nonexperimental designs or strategies:1. Quasi-Experimental Strategy2. Correlation Strategy3. Descriptive Strategy

• Let’s consider some of the unique aspects of each strategy.


Quasi-Experimental Strategies

• As the word quasi implies, quasi-experimental strategies are almost true experiments. They only lack one of the following:– They do not manipulate an independent

variable– They do not have equivalent control and

experimental groups.


Quasi-Experiments: Manipulate Independent Variables

• Nonequivalent Control Group Design– Experimental and control groups exist but they are

created without random assignment or matching.– Often these designs use pretest and posttest

strategies.• Time-Series Design

– Multiple assessment are made over time.– In an interrupted time-series design measures are

many measures are gathered before and after some event or experimental condition.

– Often no control group exists with this design.


Quasi-Experiments: No Manipulation of I.V.

• Natural Groups Design– Divides participants into groups on the basis of

some physical or psychological feature (called a subject variable) and then compares the groups.

• Thus, no random assignment or matching into groups. • The subject variable is the independent variable.• E.g., age, gender, personality, twin, SES.


• Some common age-related natural groups designs include:– Cross-sectional design: different individuals

of different ages are measured at rough the same time.

– Longitudinal design: the same individuals are measured multiple times over a long period of time (usually years).

Age-related Natural Groups


• A correlation is a measure of the relationship between two variables.

• Correlations are use when a researcher’s goal is to predict one variable from another.

• Researchers are interested in 2 aspects of the correlation:– Its size or magnitude– Its direction

Correlational Strategies


Correlation Coefficient

• The correlation coefficient (symbolized as r) can range from -1 to +1.

• Values closer to either extreme indicate stronger relationships. Values closer to zero indicate no relationship.– E.g.,

• r = + 0.9 is stronger than r = + 0.8• r = - 0.4 is stronger than r = - 0.2• r = - 0.7 is the same strength as r = + 0.7


The Direction of the Correlation

• The plus (+) or minus (–) sign in front of the r value tells you the direction of the relationship.

• + means that if one variable is increasing in size so too is the other variable.

• - means that if one variable is increasing in size the other variable is decreasing in size.

• It is always a good idea to graph your relationship to see if it represents a positive or negative relationship. This graph is called a scatter plot.

• The scatter plot will also give you an idea about the strength of the relationship. Less scatter = higher r values.


Coefficient of Determination

• The coefficient of determination (r2) is calculated by simply squaring r.

• It represents the proportion of the variance of one variable that can be accounted for by variation in the other variable.– For example, suppose you get a r = 0.9. Thus,

81% of the variance in one variable is accounted for by the other variable.


Interpreting Correlations

• Correlation designs do not allow for cause and effect conclusions. Why?– Directionality: Does A cause B or does B cause A?– Third variable: Maybe a third variable that you did

not measure – that is related to both variables you measured – is responsible for the correlation you observe?

• Low correlation can result from many factors so don’t get upset with your results to quickly.– Some factors leading to low correlations include:

• Curvilinear relationship between the variables• Restricted range of scores• Outliers


Linear Regression

• Linear regression involves predicting a score on one variable from the score on another variable.

• Regression is used to predict future outcome on some variable (the criterion variable or Y) from some variable you currently know (the predictor variable or X).

– E.g., predicting how well you will do in university from your high school grad point average.

• The mathematical equation that is used to make the prediction is in the form:

– Y = a + bX

• And was derived from numerous similar situations. E.g., 1000s of people who finished university and their high school GPAs were known.

• Multiple regression involves predicting a criterion score from two or more predictor variables


Correlation: Reliability

• Reliability is the consistency of a test.• Correlation is often used as a measure of

reliability in a test.– Test-retest reliability: correlate the scores

of people who take the test twice.– Split-half reliability: dividing the test into 2

halves and correlation the scores of people on the 2 halves.• Cronbach’s alpha.


Correlation: Criterion Validity

• Validity means that a test is actually measuring what it is suppose to measure.

• Correlation is also used to measure various types of validity.– Criterion validity: refers to how well a test predicts

some future event or behavior? There are two types of criterion validity:

• Predictive: Test scores are kept for a period of time. These scores are then correlated with some future behavior (the criterion).

• Concurrent: Test scores are correlated with an established – already validated – measurement.

– High correlations suggest valid tests.


Correlation: Construct Validity

• Construct validity is the extent to which a test measures some theoretical construct.

• There are two types of construct validity:– Convergent Validity: Measured by correlating score

of test with other tests that measure the same thing. High correlations indicate convergent validity.

– Discriminant Validity: Correlate test scores with test that do not measure the same thing. Low correlations provide discriminant validity.

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2005, Pearson Education/Prentice Hall CHAPTER 6 Nonexperimental Strategies