©2005, Pearson Education/Prentice Hall CHAPTER 6 Nonexperimental Strategies

©2005, Pearson Education/Prentice Hall Types of NonExperimental Strategies There are 3 types of nonexperimental designs or strategies: 1.Quasi-Experimental Strategy 2.Correlation Strategy 3.Descriptive Strategy Let’s consider some of the unique aspects of each strategy.

©2005, Pearson Education/Prentice Hall 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.

©2005, Pearson Education/Prentice Hall 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.

©2005, Pearson Education/Prentice Hall 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.

©2005, Pearson Education/Prentice Hall 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

©2005, Pearson Education/Prentice Hall 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

©2005, Pearson Education/Prentice Hall 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 = is stronger than r = r = is stronger than r = r = is the same strength as r = + 0.7

©2005, Pearson Education/Prentice Hall 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.

©2005, Pearson Education/Prentice Hall Coefficient of Determination The coefficient of determination (r 2 ) 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.

©2005, Pearson Education/Prentice Hall 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

©2005, Pearson Education/Prentice Hall 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

©2005, Pearson Education/Prentice Hall 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.

©2005, Pearson Education/Prentice Hall 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.

©2005, Pearson Education/Prentice Hall 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.