Quasi-Experiment Methods

Question 1

internal validity in quasi-experiments

Answer 1

• quasi experiments lack full experimenter control over the IV
• they can have better internal validity and can use methods to rule out threats to external validity

Question 2

how to prevent selection effects as a threat to internal validity?

Answer 2

pretest-posttest design
- are subjects at one level of IV systematically different from subjects at another level of IV?

Question 3

how to prevent design confound as a threat to internal validity?

Answer 3

measuring second variable can help rule out design confounds

Question 4

how to prevent maturation/history as a threat to internal validity?

Answer 4

pattern of results in comparison group can help rule out these threats

Question 5

characteristics of quasi-experiments

Answer 5

• cannot establish causality
• researchers take precautions to reduce threats to internal validity
• eliminates as many alternative explanations as possible
• lack random assignment to condition

Question 6

non-equivalent groups

Answer 6

participants do not start from the level
they vary systematically from the beginning

experiment:
studying students from public/private schools and their attitudes regarding authority

non-equivalent groups need to be in place because students were not randomly assigned to a school

Question 7

nonequivalent group design

Answer 7

compares 2 non-equivalently groups that are intentionally as similar as possible
1. treatment group
2. control group

group a measure DV -> scores
treatment compare

group b measure DV -> scores
control

Question 8

pretest-posttest nonequivalent groups

Answer 8

comparing before and after treatment that participants were not assigned to

compares 2 non-equivalently groups that are intentionally as similar as possible
1. treatment group
2. control group
* difference between scores before and after treatment is compared across groups

surgery pretest treatment posttest

no surgery pretest treatment posttest
* compare results before and after treatment across groups

Question 9

interrupted time series

Answer 9

treatment in the middle of multiple measurements from DV

take multiple pretest and posttest measurements to asses effects of both treatment and time passing

purpose: eliminate noise before and after data

Question 10

controlled interrupted time series design

Answer 10

observe a non-equivalent control group over the same time period

comparing two sets of data and taking multiple measurements

treatment vs no treatment
*measures multiple DVs before and after treatment

Question 10

structure of interrupted time series design

Answer 10

measure DV - measure DV - measure DV -treatment or intervention - measure DV - measure DV - measure DV
compare
compare average before and after treatment

Question 11

longitudinal developmental design

Answer 11

observe a group of individuals as they age
- time consuming and can be confounded by attrition

looking at maturation itself

measure DV over the course of the participants lifespan

and compare the scores over time

Question 12

cross sectional developmental design

Answer 12

compares different age groups to see how the DV changes with age
internal validity threated by cohort effects (differences due to cohort characteristics rather than development)

group 1 measures dv -> scores
(65+ years old)
group 2 meadures dv -> scores
(18-25 yrs old)

compare scores afterwards