Final Exam 2

Question 1

notation system for factorials

Answer 1

of IV1 X # of IV2, multiply to find total number of conditions

Question 2

main effect

Answer 2

overall effect of Independent Variable, can have as many main effects as independent variables

Question 3

interaction

Answer 3

effect of one factor depends on the level of another factor. If one level of IV is higher in both conditions, no interaction occurred. Also can plot onto graph, parallel lines=no interaction

Question 4

mixed factor design

Answer 4

at least one between subjects factor, at least one within subjects factor

Question 5

PXE design

Answer 5

factorials with subject and manipulated variables, P=person, E=environmental

Question 6

Pearson’s R

Answer 6

ranges from -1 to +1, 0=no correlation

Question 7

correlations with scatterplots

Answer 7

Weak correlations are more spread out

Question 8

Pearson’s R squared

Answer 8

helps determine variability

Question 9

criterion variable

Answer 9

in regression analysis, variable being predicted

Question 10

predictor variable

Answer 10

in regression analysis, variable doing predicting

Question 11

how to solve problem of directionality

Answer 11

can use cross-lagged correlation, longitudinal design

Question 12

third variable problem

Answer 12

what are correlations with other variables? Make chart

Question 13

problems with correlational research

Answer 13

hard to establish cause

Question 14

reasons to use correlational research

Answer 14

• make predictions
• when variables cannot be manipulated (subject)
• test/retest reliability and criterion validity of psychological tests
• assessing relationships between variables in personality and abnormal psych
• twin studies

Question 15

bivariate analysis

Answer 15

correlational research, two variables

Question 16

multivariate analysis

Answer 16

correlational research, more than two variables, only one criterion variable

Question 17

factor analysis

Answer 17

examines all possible correlations among each of several scores, identifies clusters of intercorrelated scores

Question 18

quasi-experimental research

Answer 18

research with non equivalent groups: examples=

• nonequivalent group factorial design
• P X E factorial design
• correlational design
• control group pre-test and post-test design

Question 19

Quasi-experimental non-equivalent control group design

Answer 19

Experimental: 01 treatment 02
non-experimental: 01 no treatment 02
Example of control: Arizona in nightmare study, did not experience earthquakes
baseball coach study: control group=baseball coach from different league who was not trained

Question 20

Possible confounds of quasi-experimental design

Answer 20

• history
• subject selection
• knowledge of participating
• ceiling/floor effects-too difficult/easy
• regression to mean-extreme scores at pretest move toward mean at posttest, looks like there was no effect in treatment

Question 21

Best outcome in quasi-experimental pretest posttest design

Answer 21

experimental below control in prettest, end=experimental above control. This outcome rules out regression and ceiling/floor effects

Question 22

Time-series design

Answer 22

measure at different moments, say 10 moments. Pretest at O1 and posttest at O8. Measure past posttest. Treatment happens at O5. Helps to evaluate longer term trends

Question 23

Problems with time-series design

Answer 23

attrition, history (must rule out these confounds)

Question 24

Time-series switching replication

Answer 24

Give treatment at different time to rule out effect of history

Question 25

second dependent variable

Answer 25

measure a different variable, which should not be influenced by program. If both DVs change, there could be a different trend or extraneous variable influencing both variables.

Question 26

Stages of program evaluation

Answer 26

1. planning - research - informants/focus groups 2. formative evaluation - -evaluating program while in process - is program being implemented as planned? 3. summative evaluation - program effectiveness

Question 27

Program evaluation, failure to reject null hypothesis

Answer 27

can be useful in this case because new programs have to prove themselves. Might find program is not cost effective.

Question 28

Naturalistic vs. participant observation

Answer 28

In participant observation, experimenter is involved in group being observed -cult study (Festinger)

Question 29

Problems with participant observation

Answer 29

- ethical issues | - reactivity of group, changing their behavior

Question 30

Challenges with observation research

Answer 30

-absence of control, cannot prove hypothesis (can falsify) -observer bias -how to counteract: checklist, interobserver reliability, time and event sampling

Question 31

survey: types

Answer 31

- probability sampling | - representative sampling: sample reflects attributes of target population

Question 32

sampling procedures

Answer 32

- random - stratified: same strata as actual population - cluster sampling: group of people all have some feature in common like living on the same floor or taking core classes

Question 33

interviews: benefits and costs

Answer 33

benefits: comprehensive, can follow up costs: cost, logistics, interview bias, representative samples

Question 34

phone surveys

Answer 34

benefits: cost, efficiency drawbacks: brief, low response rate

Question 35

electronic survey

Answer 35

benefits: cost, efficiency drawbacks: sampling issues

Question 36

written surveys

Answer 36

plus: ease of scoring (vs. interviews) drawbacks: cost, non-response rate, social desirability bias

Question 37

creating an effective surveys

Answer 37

- open-ended questions when first starting - balance favorable and unfavorable statements (avoids response acquiescence) - "most important problem" questions - use scales (Likert scale) - moderate use of I don't know alternative - place demographic info at end of survey - avoid ambiguity with a pilot study - avoid biased and leading questions - don't ask for two things in one question - "do you support or oppose"

Question 38

Small N-design definition

Answer 38

- data reported one participant at a time | - small group or one individual studied

Question 39

Why use small-N design

Answer 39

- practical reason: rare attribute, rare species | - issues with statistical summaries: grouping data can be misleading, poor individual-subject validity

Question 40

Operant conditioning, steps of experimental analysis of behavior

Answer 40

``` Behaviors result from learning history Steps 1.Define behavior 2.Understand conditions leading to behavior 3.Understand reinforcing consequences ```

Question 41

Applied behavior analysis process

Answer 41

1) Baseline A 2) Treatment 3) B

Question 42

Withdrawal designs

Answer 42

ABA or ABAB is better

Question 43

Benefits of case study

Answer 43

- level of detail | - can serve falsification

Question 44

Weaknesses of case study

Answer 44

- limited control - external validity-generalization - faulty memory

Question 45

Alternating treatment design

Answer 45

alternate treatments to see which is more effective example: AOC vs. no AOC in autistic girl

Question 46

weaknesses of small N design

Answer 46

- external validity - no statistical analysis - interactive effects hard to test - overreliance on rate or response

Question 47

changing criterion design

Answer 47

Shaping behavior, criterion is changed until goal is reached, example: diet studies

Question 48

Multiple baseline design

Answer 48

One treatment introduced at three different times, each baseline should improve after treatment is introduced, not before. Example: posting scores for each behavior with football players. This solves problem of ABAB design

Question 49

Types of multiple baseline studies

Answer 49

- treatment introduced in different settings - treatment introduced with different subjects, all have different baselines but same general behavior - multiple behaviors, one treatment (like football player study)