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research methods > exam 3 > Flashcards

exam 3 Flashcards

1
Q

Criteria for cause

A
  1. Covariance - looking at changes in DV after manipulating IV
  2. Temporal precedence - manipulation of IV comes before change in DV
  3. Internal validity - manipulating IV while holding all other factors constant
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2
Q

Characteristics of experiments

A
  1. Empirical/objective approach - gathering information through the use of your five senses during systematic hypothesis testing
  2. Manipulation of variables - value of interest is manipulated and outcome is recorded
  3. Keeping other factors constant - consistent variation in all conditions
  4. Determining causal relationships
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3
Q

Independent variable MUST have…

A

-One IV in each experiment
-2 levels
-Can have more than one IV
-Can be discrete or continuous

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4
Q

Between subjects independent variable

A

different groups of subjects, different subjects in each level

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5
Q

Within subjects independent variable

A

Same subjects experience each level of IV

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6
Q

Types of manipulations

A

-Environmental - changing social or physical environment
-Instructional - changing instruction given to one group over another
-Invasive - create physical changes in participants

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7
Q

How to pick levels of IV

A
  1. trial and error - look at past research and choose ones that seem appropriate and hope for the best
  2. pilot study - pick levels that seem appropriate and try them out on a handful of participants
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8
Q

Manipulation check

A

institute measures within the study to determine if you actually manipulated what you think you did

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9
Q

Dependent variables must be

A
  1. reliable
  2. valid
  3. sensitive
  4. no ceiling/floor effects
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10
Q

Simple random assignment

A

every participant has an equal chance of being in each group. on average, groups should end up being equal in most characteristics

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11
Q

matched random assignment

A

matching people on a characteristic that is relevant to the experiment. requires a pretest and used when there is a possibility that random assignment will still cause inequalities between groups

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12
Q

advantages of within subject designs

A

-fewer participants are needed to detect differences
-more powerful

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13
Q

disadvantages of within-subjects designs

A

-order effects - practice effects- people improve b/c they have already done it before
-carryover effects - effects of condition A are still lingering when condition B begins

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14
Q

counterbalancing

A

presenting levels of the IV in different orders to different participants

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15
Q

threats to internal validity

A
  1. misc. design confounds - type of extraneous variable that varies systematically with the independent variable
  2. biased assignment to conditions
  3. differential attrition - people in one condition drop out at higher rate, no longer have random assignment
  4. pretest sensitization
  5. history - behavior is affected by a person’s past experiences
  6. maturation - developmental changes may take place and subjects behavior changes
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16
Q

one way experimental design

A
  • contains only one IV
  • simplest one way design: one IV two levels
  • can be within or between subjects
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17
Q

factorial designs

A

-contains 2 or more IV
- designs can be between subjects, within subjects, or mixed

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18
Q

inferential statistics

A

we can specify the probability that differences are due to error variance but cannot be 100%

19
Q

H0 (null hypothesis)

A

group/conditions will NOT differ. the independent variable will NOT have impact on behavior

20
Q

Ha (alternative hypothesis)

A

group condition WILL differ. the independent variable WILL have impact on behavior

21
Q

rejecting the null hypothesis

A

high probability that the null hypothesis is false, saying group conditions will differ

22
Q

fail to reject null

A

low probability that null is false. null is correct.

23
Q

Type I error

A

rejecting null when it is true. you should have failed to reject it

24
Q

Type II error

A

Failing to reject the null when it is false. Should have rejected

25
Q

Alpha level (p value)

A

probability of making type I error. the smaller the alpha, the smaller chance of type I error

26
Q

Beta level

A

probability of making type II error

27
Q

Power

A

ability to detect true differences between groups. ability to correctly reject the null. power is opposite of beta

28
Q

Power analysis

A

tells you how many subjects you need in your study, given your alpha level and desired amount of power

29
Q

T

A

actual difference between group means/expected difference between means if only due to error

30
Q

Unpaired t test

A

one way with two levels; IV is between subjects and using random assignment

31
Q

Paired T test

A

One way with two levels; IV is within or between using matched assignment

32
Q

Equation for significant results

A

t(df) = t value, p<,05

33
Q

Equation for nonsignificant results

A

t(df) = t value, p >.05

34
Q

test when you have one IV two levels

A

t test (paired or unpaired)

35
Q

test when you have one IV > 2 levels

A

one way ANOVA

36
Q

test when you have more than one IV

A

factorial ANOVA

37
Q

ANOVA evaluates..

A

how much systematic variance there is in your study, how much error variance there is in your study

38
Q

systematic variance

A

variability due to your manipulations
between groups

39
Q

error variance

A

variability due to individual differences and mistakes
within groups

40
Q

Post-hoc tests

A

used to pinpoint exactly which conditions differ from each other

41
Q

Larger F value indicates

A

differences likely due to IV

42
Q

Smaller F value indicates

A

differences are likely due to error

43
Q

F formula

A

F = MSbg/MSwg

research methods (3 decks)

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