# Research Methods

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1. Analysis of variance (ANOVA):
• Estimate of systematic variance
• Estimate of error variance
• Calculate a ratio
2. Simple, one way analysis of variance (ANOVA)

F= systematic var. + error var. =
Error variance
• F = Between groups var.
• Within groups var.
3. Between groups variance=
Within groups variance=
• Between= variance of group means
• Within= average of variance found within each group
4. Simple analysis of variance (anova)
*Scores almost always vary (differ) from subject to subject... 2 reasons:
• 1. Systematic variance
• 2. Error or unsystematic variance
5. 1. Systematic Variance
Difference due to different levels of I.V.
6. 2. Error or unsystematic variance
Differences due to uncontrolled, random factors (not confounding)
7. Error or unsystematic variance-uncontrolled variance:
• 1. individual differences
• 2. random variations in testing
• 3. measurement error
• 4. experimental treatment not the same for each subject
8. If IV influences DV:
systematic variation of i.v. should cause systematic variation of the d.v. = systematic variance
9. Differences in iv should cause:
differences in group means
10. If IV doesnt have an effect:
group means are not likely to be exactly the same

No systematic var.
11. An effect means:
that there is a real difference in means or that IV and DV are correlated
12. If an effect, variations in the IV:
would not reduce variance within groups variance
13. If an effect:
Assume that differences in group means should be:
greater than differences within any single group
14. If an effect:
Difference within a group :
just due to chance or uncontrolled factors
15. If an effect:
Differences betwen groups due to:
controlled and uncontrolled factors
16. Understanding basics of anova tell you how to design and run experiments:
• 1. large differences between groups
• 2. small differences within groups
17. Would size of sample influence F ratio?
• No, not directly-not in the equation
• Influences whether F ratio is statistically significant
18. For a significant effect:
• 1. large differences among group means
• 2. small differences within each group (not confound idea)
• small individual differences
• reliable tests or measures
• consistent application of each level of iv
19. Effect =
difference (or variability) in means
20. Possible to see an effect if the study includes:
a comparison of the influence of different levels of a predictor or independent variable
21. Factorial design- more than 1 IV:
• Each level of each IV occurs with every other level of all the independent variables
• Uses all possible combinations of levels of 2 or more independent variables
22. Main effect:
the effect (differences in means) of one iv, averaging,ignoring, or collapsing over the other iv's
23. Factorial Designs
• 1. Economy
• 2. Experimental control
• 3. Check generality of the effect of an IV
24. Factorial designs:
1. Experimental control-
reduce error variance
25. Interaction:
the effect of one iv depends on the level of another iv
26. Factorial design:
Look for three things if there are two i.v.:
• main effect of iv A on dv
• main effect of iv B on dv
• Interaction between A and B
27. Describing the results of factorial designs:
• 1. Set up a table of means for each condition
• 2. Draw a figure
• 3. Describe the means for each main effect followed by a description of the results of the ANOVA
28. Statistical Hypothesis Testing:
• 1. Any t, F, U, orr could be due to chance/luck
• 2. If a result (a, t, F...) occurs by chance infrequently decide result is statistically significant
• 3. Typical reasoning
• 4. examples
29. 3. Typical reasoning:
often decide that if an outcome is unusual/rare must be due to more than chance.
30. Null hypothesis:
presumed true unless statistical evidence suggests otherwise
31. The null hypothesis is tested because:
we have information about chance, not how the independent variable works.
32. Type I error:
Rejuect Ho but it is actually true
33. Type II error:
Faile to reject the Ho but Ho is false
34. What evidence must you collect in order to conclude that X is a cause of Y?
• 1. Show that changes in Y didnt occur until change in X (temporal precedence rule)
• 2. Show that X and Y are related (covariation rule)
• 3. Rule out other explanations forthe relationship between X and Y (internal validity rule)
35. Characteristics of a true experiment:
• 1. manipulation of X
• 2. Comparison of the effects of various levels of X on Y
• 3. Subjects begin the experiment equivalent on all levels
• 4. control over all other important variables so that all subjects are treated the same except for X (random assignment)
36. Program Evaluation Basic Strategy:
-To show the program caused a change in client
• a. Change in client occurred after the introduction of the program
• b. participating on not participating in program covaries with client success
• c. Rule out other explanations for the relationship between the program and client success
• d. plausible causal mechanisms linking program to client success
37. If there is an effect: F =
greater than 1
38. If no effect F=
around 1
39. More diversity =
more error variance
40. Interrupted time series design
• often encountered in quasi experimental research
• Have a single experimental group for which we have multiple observations before and after naturally occuring treatment.
• Instead of observing 1 or 2 3rd grade classes we could observe 3rd grade classes over several years
• We need to know when the time series is interrupted by some treatment
• Then we compare observations beforea nd after the treatment to see whether it had any effect.
41. Threat to internal validity in case studies:
• Source of causation;
• baseline condition maturation
• history
• selection bias
42. Ways to enhance internal validity in case studies
• Deviant case analysis (a non equivalent control)
• detective work
43. Threat to internal validity for interrupted time series:
• changes in participants and environment
• delayed effects
44. Ways to enhance internal validity in interrupted time series:
• nonequivalent control group
• detective work
45. Threats to internal validity for subject variables
• dimensions on which to match
• regression artifacts
46. Ways to enhance internal validity for subject variables:
• matching
• include true independent variable
• see interactions
47. Threats to internal validity in Age as a variable:
• Confoundings with time of testing
• generation of birth
48. Ways to enhance internal validity in Age as a variable:
• Cross sequential design
• include a true i.v. & seek interaction
• Converging operations
49. ______ _________ masks true behavior in quasi experiments and matching studies.
regression artifacts
50. All non parallel lines=
interaction
51. p =
• Significance level
• it does notindicate repeatability
52. one way anova- Error Variance (denominator) estimated by:
Calculating within groups variance (variance within each group pooled)
53. Krauter pseudo F ratio=
• range of group means
• (range of scores in gp 1+ gp 2+gp 3 etc..
• # of groups
54. 2 reasons why scores in an experiment almost always vary:
• 1. Systematic variance
• 2. Error or unsystematic variance
55. Mann-Whitney U test
• a simple inferential statistic that can be used in place of a t test.
• It can be used in many instances in which you have tested twoindependent groups of subjects
56. How to do mann whitney u test:
• 1. put the scores in order from smallest to largest
• 2. For each score in the group w/ the lower mean, count the # of scores that are smaller in the other group. Thus you'll have 1 # for each score in the group with the lower mean that tells you how many scores are smaller in the other group than that score\
• 4. For each score in the group with the lower mean, count the # of scores in the higher group that are tied or are the same. Add these ties together and divide by 2
• 5. Add theresults of C and D together to obtain the mann whitney u
57. Program Evaluation:
• Is a particular program actually delivering the services it is designed to deliver?
• Is a law having the desired effect?
58. Program Evaluation
Basic Strategy-
To show program caused change in client;
• a. Change in client occurred after the introduction of the program
• b. Participating or not participating in the program covaries with client success
• c. Rule out other explanations for the relationship between the program and client success
• d. Plausible causal mechanisms linking the program to client success
59. What kind of design is:
After school program and social skills...Every one gets the same treatment and all measured the same way
• One shot case study
• There is no comparison
• Descriptive research
60. If there is success in a one shot case study design, does it show the program caused the success?
• Change in the client occurred after intro to program
• Having or not having program covaries with client success
• Rules out other explanations for relationship between program and client success

• Extremely low internal validity
• Cant even show that the client changed during the program
61. Ongoing flow of events interrupted by the introduction of treatment-
interrupted time series
62. Pre or non experimental design
• pretest post test design (before-after) o x o
• Potentially suffer from all the threats of within subjects design
• change occurred after intro to program
• Program/no program varies with client success
• rule out other explanations for the correlation b/w program/noprogram and client success
63. X O
one shot case study
64. O X O
Pret test post test
65. X O
O
Static Group Comparison (ex post facto)
66. r O X O
r O - O
Pretest posttest control
67. Quasi Experimental Design
Might manipulate X, but dont compare groups that are formed based on random assignment

More opportunity to discredit alternative interpretations of data
68. Quasi experimental designs: To change non-experimental (pretest-posttest) to quasi exp. design-
• 1. Additional times before and after program introduced-time series studies
• OXO -> OOOOOOOXOOOOOO
• 2. Additional people who havent received the program (non equivalent control group - no random assignment - STATIC GROUP COMPARISON
• OXO -> OXO
• O-O (non equivalent control group
69. Time Series Design
Get many measures before and after some natural or planned intervention
70. Pretest-posttest design also becomes a quasi experimental design if:
71. To improve a non experimental design:
Increase number of observations, add comparison group or both
72. Program evaluation-quasi experimental designs

Can distinguish between effect of program and many other variables:
• 1. comparison and "program" group have same amount of time to mature
• 2. history should influence both groups equally
• 3. testing should influence both groups equally
73. problem with program evaluation-quasi experimental designs:
• Finding a good comparison group...
• Cant randomly assign-between groups variable: ex post facto
74. Possible selection bias problem:
groups different because of the way they were selected or assigned to groupss
75. Program evaluation:
Quasi experiments summary
• 1. no random assignment
• 2. quasi-comparison using additional measures before and after or non equivalent control groups
• 3. moderate control
• 4. often field based
• 5. may never be able to eliminate confounding variables

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 Author: faulkebr ID: 117294 Filename: Research Methods Updated: 2011-12-05 22:15:18 Tags: Final Folders: Description: Final-Slides Show Answers:

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