Chapter 11 Confounding and Obscuring Variables

Question 1

why is the one-group pretest/posttest design a bad design?

Answer 1

there is no comparison group/there is only 1 IV level

Question 2

3 threats to internal validity

Answer 2

design confounds
selection effects
order effects

Question 3

6 main potential threats to internal validity

Answer 3

maturation
history
regression to the mean
attrition
testing
instrumentation
***combined threats

Question 4

3 additional general threats to internal validity

Answer 4

observer bias
demand characteristics
placebo effects

Question 5

maturation threats and examples

Answer 5

a change in behaviour that emerges spontaneously over time
ex: gaining experience, developmental changes, fatigue, boredom, hunger

Question 6

how can maturation threats be prevented/detected?

Answer 6

comparison groups

Question 7

history threats

Answer 7

an external event affects most members of the treatment group at the same time as the treatment (systematically)

Question 8

how can history threats be prevented/detected?

Answer 8

comparison group

Question 9

regression to the mean

Answer 9

statistical concept in which extremely low/high performance at time 1 is likely to be less extreme at time 2 (closer to the mean)

Question 10

regression threats only occur:

Answer 10

-in pre/posttest design AND
-when a group has an extreme pretest score

Question 11

why can regression to the mean occur?

Answer 11

-random error in measurement
-when measures have low reliability
-doesnt happen all the time

Question 12

attrition

Answer 12

a reduction in participants from pretest to posttest

Question 13

attrition is only a threat if it’s…

Answer 13

systematic

Question 14

how can attrition be prevented/detected?

Answer 14

-remove the pretest scores of the participants who drop out
-inspect if pretest scores of those who dropped out are extreme

Question 15

testing threats

Answer 15

when the very act of completing a pretest influences responses on the posttest

Question 16

why testing threats may occur

Answer 16

-participants are aware of the hypothesis
-re-evaluate the DV
-practice causes improvement (order effect)
-consistency pressures: people want to give off the impression that they’re consistent/on all the time

Question 17

how can testing threats be prevented/detected?

Answer 17

-avoid pre/posttest design (harsh)
-use alternative equivalent forms of test for DV at pretest and posttest
-comparison groups show smaller pre/posttest fx than treatment group

Question 18

instrumentation threats/instrumentation decay

Answer 18

when a measuring instrument changes over time

Question 19

in what 2 ways can measuring instruments change over time?

Answer 19

-different observers or changed criteria by the same observers
-non-equivalent forms of test to measure the DV

Question 20

how can instrumentation threats be prevented?

Answer 20

-keep the same observers
-highly structured coding standards
-posttest only design
-equivalent forms of test
-counterbalancing pretest/posttest

Question 21

2 combined threats

Answer 21

selection-history threats: an outside event/factor systematically affects participants at 1 level of the IV
selection-attrition threats: participants in 1 experimental group experience attrition

Question 22

observer bias

Answer 22

when observers expectations influence either the interpretation of participants behaviours or the outcome of the study

Question 23

when may observer bias occur?

Answer 23

when the DV is behavioural

Question 24

adding a comparison group cannot fully solve the issue of this threat to internal validity and may even increase its threat

Answer 24

observer bias; may increase the threat if the observers know who’s in which group

Question 25

observer bias threatens which validities?

Answer 25

internal and construct

Question 26

demand characteristics

Answer 26

when participants discover what a research study is about, it may change their behavior in the "expected" direction

Question 27

true or false: adding a comparison group can fully solve the demand characteristic issue

Answer 27

false

Question 28

what does Kihlstrom say about human participants

Answer 28

they are curious creatures who constantly think about what is happening to them, evaluating the proceedings, figuring out what they're supposed to do and planning their response

Question 29

solutions to demand characteristics

Answer 29

double-blind design single-blind/masked design: observers don't know participants conditions

Question 30

placebo effects

Answer 30

when people receive a treatment and improve but only because they believe they are receiving an effective treatment -it's not imaginary. there's an actual physical or psychological improvement

Question 31

double-blind placebo control study goal

Answer 31

to find out if the treatment is effective

Question 32

what else can the double-blind placebo control study suggest other than placebo fx?

Answer 32

may instead suggest maturation, history threats, regression to the mean, testing threats or instrumentation threats

Question 33

"to assess whether an IV is a cause of variation in the DV, we assess..."

Answer 33

how much of the total variability in the DV is due to the IV

Question 34

total variability in the DV =

Answer 34

b/w groups variability and w/in groups variability

Question 35

how much of each variability do we want (in total variability in the DV)

Answer 35

large b/w groups and minimal w/in groups

Question 36

null effect

Answer 36

no significant covariance, causal effect or correlation b/w IV and DV

Question 37

why were null effects rarely seen in peer-reviewed/popular articles in the past?

Answer 37

because science used to be biased in only reporting results w/ significant results

Question 38

power in research

Answer 38

the likelihood that a study will yield a statistically significant result when the IV really has an effect the chance that a study will produce a statistically significant result when the IV actually affects the DV

Question 39

solutions to increasing b/w groups variability and reducing w/in groups variability are attempts to increase the _______

Answer 39

power of a study

Question 40

define sunk cost

Answer 40

money, time, effort, etc spent (wasted) on non-sig/null studies

Question 41

null effects should be reported ________

Answer 41

transparently

Question 42

publication bias past vs present

Answer 42

past: null results weren't likely to be published present: publish results regardless of significance