Relational Research

1. Relational Research

2. Examines whether some X variable is systematically related to some Y variable. relational research involves measuring both IV and DV variables. one cannot make causal inferences However, can still identify relationships. Two types of relational research: Contingency Table (relation between two or more qualitative/categorical variables at the nominal level) Correlational research (relation between two or more quantitative variables at interval or ratio level.

3. Contingency table 2 or more rows and 2 or more columns, each represent nominal categories. Examine whether observations distribute themselves "evenly" across the rows/columns. If not, variables are not independent--i.e., significantly related). Examine the frequencies/percentages in each of the cells of the contingency table. example, frequency of people voting for Obama vs. McCain in relation to the individuals' political affiliation:

4. If voting is independent of political affiliation, frequencies should distribute themselves evenly across the four cells (called the "Expected Frequency"). 40 Democrats and 40 Republicans: Expected Frequency is compared to the "Observed frequency". The appropriate analysis is chi square. If p < .05, there is a significant relationship.

5. CORRELATIONAL RESEARCH Powerful tool for examining relations among variables. Pearson's product-moment correlation coefficient Most Common Measure Requires that both variables are quantitative and interval-level measurement. However, there are statistics for computing correlations among qualitative variables, such as Spearman's rho.

6. The Correlation Coefficient Pearson r 1.0" (perfect negative correlation) through "0" (no relation), to "+ 1.0" (perfect positive correlation) Direction is indicated by the sign of r. Degree is indicated by the magnitude/size of the coefficient. Scatter plots show direction and degree of relationship between two variables. also useful for determining curvilinear relationships.

7. Reasons one cannot infer causation from correlation 1. "Direction of Causation" Problem: Can't tell whether X caused Y, or Y caused X. Basic "chicken-egg" question. e.g., amount of time spent reading is positively correlated with scores on vocabulary tests. 2. �Third Variable" Problem: X and Y may simply share a common cause, some third variable, Z. Thus, the correlation is "spurious�. e.g., caviar and heart disease, optimism and healthy aginge.g., caviar and heart disease, optimism and healthy aging

8. 1. Heart Disease Red Wine ? less heart disease Green Tea ? less heart disease 2. Obesity and� Being obese ?more bullied Families eating together ? less obese 3. Aging and� Optimism ? more healthy aging 4. Alzheimer�s and� Blueberries ? less Alzheimer�s Crossword Puzzles ? less Alzheimer�s

9. If small r, it may be that the variables are unrelated. Or� 1. "truncated range" of one or both variables: The full possible range of scores is not represented. e.g., a low correlation between GRE and grad school GPA. 2. "curvilinear relationship". Positive and negative relationships "cancel each other out," e.g., arousal and performance. Grad schools do not accept applicants with low GRE scores, nor do grad programs often give grades lower than a "B.�Grad schools do not accept applicants with low GRE scores, nor do grad programs often give grades lower than a "B.�

10. Multiple Regression Correlating several predictor variables with one or more criterion variables.

11. Increase ability to predict grad school GPA by including measures of other predictor variables: y (predicted grad GPA) = a + bX1 (GRE) + bX2 (Undergrad GPA) + bX3 (Prestige of Undergrad school) + bX4 (Recommendation letters) + bX5 (Amount of Research Experience) + ... The more predictor variables in the equation, the greater % of variance that can be "captured.� The relative importance of each predictor variables can be discovered by comparing the "b" values.

12. "Cross-lagged-panel" procedure can provide a tentative answer to the "direction of causation" problem. e.g., Which came first: viewing violent TV, or an aggressive personality? Third variable problem still rampant!!!!!Third variable problem still rampant!!!!!

13. Experimentation

14. ADVANTAGES OF EXPERIMENTATION Ability to infer cause-effect i.e., experimental research has high internal validity due to control of variables.

15. simplest "true experiment" includes at least two groups. an experimental/treatment group, and a baseline/control group. Groups provide the basis for the comparison of scores on the DV. Control the occurrence/nonoccurrence of a variable (the IV) and measure the outcome (the DV). underlying logic is Mill's joint method of agreement and difference To demonstrate cause-effect between a causal event (A) and a resulting effect (X), need 2 conditions: (1) if A occurs, then X results; this is the "agreement" part and (2) if A does not occur, then X also does not occur.

16. Cause-effect statements do not demonstrate absolute causality seldom can show that A causes X in every single instance. We settle for probabilistic causality. X is more likely to occur when A is present than when A is not present. Example.

17. Experimental Control Control over the IV: Ability to "produce" the IV by directly manipulating its occurrence to produce the comparison (i.e., "A," vs. "~A") A "Control" Group: Withhold experimental treatment from a group to serve as the baseline of comparison. �controls� any change in behavior due to the absence of the treatment. Control over Extraneous Variables: Hold extraneous variables constant across two groups, so they don�t become confounds.

18. VARIABLES IN EXPERIMENTATION

19. Independent Variables Avoid a "weak" manipulation of the IV. May not observe any differences on the DV. e.g., half of you a dime for doing well on Exam 1 (the "experimental group"), vs. nothing (the "control group"). Instead, "stack the deck" in favor of finding a significant difference between the levels of the I.V. Journal editors are reluctant to publish null results possibility that the lack of difference was due to a poor/weak manipulation of the I.V.

20. Dependent Variables Unreliability/invalidity of measurement creates static/noise that masks the effects of the IV. Poor operational definitions of the DV result in random error, rather than varying systematically as a function of the IV. This creates too much noise to allow us to detect even a real signal (IV effect). (scores that "bounce all over the place�),(scores that "bounce all over the place�),

21. Other D.V. Issues We need a DV that is sensitive to changes produced by the IV. "Floor Effect" most scores (regardless of experimental condition) fall at the lower end of the measurement scale. e.g., if the Exam was too difficult, then everyone would do poorly in both conditions, thereby masking any real effects of incentive. "Ceiling Effect" most scores on the high end of the scale. e.g., if the Exam was too easy, everyone would do well, so once again any real differences due to incentive would be masked.

22. Control Variables Extraneous variables that are held constant. holding them constant across levels of the IV renders them "nonvariables" (since they have one level). How do we know which variables to control? Control for extraneous variables that could exert an effect on performance. study past research, we learn from other researchers which are the important variables should be controlled. Ex. Eye color.Ex. Eye color.

23. Adding IV's  Including two or more IV's in the experiment allows greater: Efficiency. Easier to do one experiment with two IV's than two separate experiments. Control over extraneous variables, More likely to be constant across conditions in a single experiment conducted at one time. Generality of results to multiple conditions/situations. Interest-value interaction effects among variables.

24. Reactivity in Experiments Experiments may not only examine behavior, they may produce it! Performance affected by knowledge they are in an experiment. (Hawthorne effect). Apprehensive subject Uncomfortable about being evaluated. Performs in socially desirable way. Tricks to minimize: Deception: omissions & cover stories Simulations Example of door sign at suny.Example of door sign at suny.

25. External Validity Generalization of your results can be enhanced by using: Different Participants Different Variables Different Settings Experimental control (internal validity) vs. generalize to �realistic setting� (external validity). Internal more important. If you can�t establish cause-effect, then you have nothing to generalize.