Exam 2

Question 1

Internal Validity

Answer 1

the independent variable is what caused the change in the dependent variable

Question 2

Selection effects

Answer 2

sampling effects, possible intentional grouping

Question 3

Order effects

Answer 3

order of the independent variable impacts behaviors on subsequent orders

Question 4

Design confounds

Answer 4

another variable systematically varies and is actually what causes the change to the dependent variable

Question 5

Maturation

Answer 5

participants changed or matured over time
comparison groups as potential solution

Question 6

history threat

Answer 6

Maybe something happened in culture/society/environment to cause the change.
comparison groups over same amount of time as potential solution

Question 7

regression threat

Answer 7

outlier; this one time was really different than the norm, and with time the behavior ‘regresses’ to the meam
comparison group can show how it decreases or increases in comparison to the treatment group
if both groups start in the same place, it is not regression.

Question 8

attrition threat

Answer 8

participants left the study; bad if said participants added variance to your results
potential solution: re-analyze pre-test without the participants who dropped

Question 9

testing threats

Answer 9

A type of order effect where there is a change in participants as a result of experiencing the DV (the test) more than once.

Question 10

instrumentation threat

Answer 10

your measurement changed over time,
potential solution: use post-test design only, with comparison group

Question 11

Preventing testing threat

Answer 11

One way to prevent testing threats is not to use a pretest (posttest-only design). Another way is to use alternative forms of the test at pretest and posttest.
Having a comparison group is also helpful. You can rule out testing threats if both groups take the pretest and the posttest, but the treatment group exhibits a larger change than the comparison group.

Question 12

observer bias

Answer 12

bias caused by researchers’ expectations influencing how they interpret the results. (expecting to see participants improve and they are rated as improving)
potential solution: : blind rater code behavior(bring someone in to code behavior who does not know the purpose of the study); masked design(participants know what condition they are in but the research doesn’t)

Question 13

demand characteristics

Answer 13

participants figure out what the research is about and change their behavior accordingly
potential solutions: double-blind study

Question 14

placebo effects

Answer 14

Participants expect a change and manifest it.
solution: a special comparison group is used that is receiving the placebo therapy or placebo medication, but neither the people working with the participants nor the participants know who is in which group (double-blind placebo control study)

Question 15

why might a study result in null effects?

Answer 15

it might be that the IV really didn’t affect the DV. Other times when there’s a null result, it’s because the study wasn’t designed or conducted properly, so the IV actually did cause the DV, but some obscuring factor got in the way of the researchers detecting the difference.
There are two types of obscuring factors: (1) There might not have been enough difference between groups, or (2) there might have been too much variability within groups. Let’s look at each of these types in detail.

Question 16

Null effects: not enough between group differences

Answer 16

within groups variability may be too high and obscured the differences; weak manipulations, insensitive measures, ceiling and floor effects

Question 17

how to solve not enough between group differences

Answer 17

Weak manipulations: Use a manipulation check; If needed, rerun the study with a stronger manipulation.

Insensitive measures: Use a refined scale.

Ceiling and floor effects: Use DV measures that allow variability; Use a manipulation check for IV.

Question 18

ceiling effects

Answer 18

everybody gets the highest score; questions are too easy

Question 19

floor effects

Answer 19

everybody gets the lowest scores; questions are too hard

Question 20

Manipulation check

Answer 20

a second DV included in a study to make sure the IV manipulation worked

Question 21

Why could there be too much within groups variability?

Answer 21

measurement error, situation noise, individual differences; Having too much noise can get in the way of detecting between-group differences.

Question 22

how to solve for individual differences

Answer 22

1. Change the design: use a within-groups design instead of an independent-groups design When you do this, then each person receives both levels of the IV, and individual differences are controlled for. It’s easier to see the effect of the IV when individual differences aren’t obscuring between-groups differences. You can also use a matched-groups design. Pairs of participants are matched on an individual differences variable, and it’s easier to see the effects of the IV.
2. Add more participants: if it’s not feasible to change the design to a within-groups or matched-groups design, then try adding more participants. This will lessen the effect that any one participant has on the group average.

Question 23

Power

Answer 23

the likelihood that a study will yield a statistically significant result when the IV really has an effect.
Studies with a lot of power are more likely to detect true differences

Question 24

There really is no difference? Not enough variability between levels?

Answer 24

your IV simply does not have a causal effect on your DV

Question 25

How to solve for measurement error

Answer 25

any factor that can inflate or deflate a person’s true score on the DV.The goal is to keep measurement error as small as possible.

Use reliable, precise measurements: measurement errors are reduced when researchers use measurement tools that are reliable (internal, interrater, and test/retest) and that are valid (i.e., have good construct validity).

Measure more instances: if researcher can’t find a measurement tool that’s reliable and valid, then the best alternative is to measure a larger sample of participants. Random errors will cancel each other out with more people in the sample.

Question 26

how to solve for situation noise

Answer 26

external distractions of any kind that obscure between-groups differences and cause variability within groups.
Example: This includes smells, sights, and sounds that might distract participants and increase within-groups variability; it adds unsystematic variability to each group situation by controlling the surroundings of an experiment that might affect the DV
solve by testing participants in a quiet room with no outside odors, and so on

Question 27

One way Anova

Answer 27

used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups
compares the means between the groups you are interested in and determines whether any of those means are statistically significantly different from each other.

Question 28

t-test versus one way anova

Answer 28

if there are two groups, use a t-test. if there are three or more groups, use a one-way anova

Question 29

Within Samples Variance in one way ANOVA

Answer 29

the variance within one condition

Question 30

Between samples variance

Answer 30

variance between ALL conditions

Question 31

three main assumptions that must be met in order for your ANOVA test to be valid and meaningful

Answer 31

The dependent variable is normally distributed in each group that is being compared in the one-way ANOVA
There is homogeneity of variances
There is independence of observations

Question 32

The dependent variable is normally distributed in each group that is being compared in the one-way ANOVA

Answer 32

So, for example, if we were comparing three groups (e.g., amateur, semi-professional and professional rugby players) on their leg strength, their leg strength values (dependent variable) would have to be normally distributed for the amateur group of players, normally distributed for the semi-professionals and normally distributed for the professional players.

Question 33

There is a homogeneity of variances

Answer 33

This means that the population variances in each group are equal. If you use SPSS Statistics, Levene’s Test for Homogeneity of Variances is included in the output when you run a one-way ANOVA in SPSS Statistics

Question 34

there is an independence of observations

Answer 34

This is mostly a study design issue and, as such, you will need to determine whether you believe it is possible that your observations are not independent based on your study design (e.g., group work/families/etc).

Question 35

Explain why ANOVAs are needed (why not just run multiple t-tests?)

Answer 35

Every time you conduct a t-test there is a chance that you will make a Type I error (false positive). This error is usually 5% (if your alpha is .05 – it is 1% if your alpha is .01).
- By running two t-tests on the same data you will have increased your chance of “making a mistake” to 10% (essentially doubling your chances of making a Type I error.)

Question 36

total variance ANOVA

Answer 36

How much an individual score differs from the grand mean
-Grand mean (Gm) = overall mean of all scores

Question 37

Within group variance

Answer 37

How much an individual score differs from
its own group mean

Question 38

Between group variance

Answer 38

How much a group mean differs from the
grand mean

Question 39

Understand the impact of within group variance on an F- Value

Answer 39

the f statistic is the comparison of the variance between the groups (your levels of the IV) from the variance within the groups.

Question 40

Repeated measures ANOVA

Answer 40

a one- way ANOVA used to determine whether there are any statistically significant differences between different levels of your independent variable across one group of participants

Question 41

when to use RM Anova

Answer 41

(1) when participants are measured multiple times to see changes to an intervention or something over time; or
(2) when participants are subjected to more than one condition/trial and the response to each of these conditions wants to be compared.

Question 42

Between Treatments/ Between Conditions Variance(RM ANOVA)

Answer 42

the variance between different levels of the IV

Question 43

Within Treatments/ Within Groups Variance

Answer 43

variance within each level of the IV

Question 44

advantage of a repeated measures ANOVA

Answer 44

whereas within-group variability (SSw) expresses the error variability (SSerror) in a between-subjects ANOVA, a repeated measures ANOVA can further partition this error term, reducing its size.
- This has the effect of increasing the value of the F-statistic due to the reduction of the denominator and leading to an increase in the power of the test to detect significant differences between means

Question 45

Between-Subjects Variability

Answer 45

variability
that comes individual participant score
variability across treatments
individual differences