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PSYCH 250 > EXAM 2 > Flashcards

EXAM 2 Flashcards

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1
Q

Quasi-experimental designs

A

No true IV
Looks like an experimental variable but
Can’t be randomly assigned
Can’t be interpreted as causing changes in the DV or outcome variable
ex– Studying the impact of a new traffic safety law by comparing accident rates before and after the law’s implementation

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2
Q

Extraneous variables

A

Variable environmental conditions over the course of the study
Individual differences among participants
Fluctuations in the physical/mental state of an individual participant

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3
Q

Confounding variables

A

an extraneous variable that covaries with your IV
It provides an alternative explanation for your findings
Threat to the internal validity of your study

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4
Q

Between-subjects design

A

Different participants are assigned to each group or condition.
Each group is exposed to a different level of the independent variable, allowing for comparisons between groups.
Random assignment/ Randomized designs
Comparing the test scores of two different groups of students, one that received the tutoring program and another that did not, to determine if the tutoring had a significant impact on math skills.

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5
Q

Within-subjects design

A

Same participants in all conditions
Repeated testing of individuals

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6
Q

Factor

A

independent variable
IV takes on different values called
Levels
Conditions
Groups
Treatments

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7
Q

Single factor

A

manipulating one independent variable with more than two levels or conditions to observe its impact on a dependent variable

ex– Experiment: Testing the Effect of Fertilizer Types on Plant Growth
Independent Variable (Single Factor):
Three different types of fertilizers:
Fertilizer A
Fertilizer B
Fertilizer C

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8
Q

Two group design

A

Simplest design
1 IV with two levels/groups

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9
Q

Multigroup design

A

1 IV with 3+ groups
Power to detect nonlinear relationships
Ex– caffeine consumption and speed of encoding

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10
Q

Randomizes

A

Random assignment to hopefully make groups equivalent on extraneous variables
Relies on chance– not guarantee that groups will be equivalent

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11
Q

Matched

A

Matched Design: Ensures groups are equal on matched characteristics.
Matched-Pairs: 2 conditions, 2 participants per match
Matched Multi-Group: 3+ conditions, 1 participant per match.
If no match, no study participation.

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12
Q

Solution for matched groups design

A

Recruit a sample of participants
Measure the participants on the extraneous variable you want to control
Group participants who scored the same (or close) on the matching variable
Randomly assign participants in the match-groups to each of the conditions

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13
Q

Matching vs. random assignment

A

Matching– guarantee
Random assignment– chance
The more participants you have, the more likely it is that random assignment will be successful

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14
Q

Within subjects design

A

The same participants are used in all conditions
Logic is similar to the matched-groups design
Threats to validity
Purpose is to control individual difference extraneous variables
ex– testing the same group of students’ math skills before and after a tutoring program to see if there is an improvement in their scores.

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15
Q

Does a pre-test lead to a post-test within subject-design

A

No
Pre test and post test are not levels of IV
pre test is a baseline and post test assesses the effects after the experiment

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16
Q

Disadvantages for within subjects design

A

More demanding on participants
Especially if your IV has a lot of levels
Drop-out
Participants failing to complete the study
Carryover effects
Exposure to one condition affects performance in a subsequent condition
Testing affects
Performance changes over time, unrelated to IV

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17
Q

Sources of carryover

A

Learning, contrast, habituation, dishabituation, sensitization

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18
Q

Learning

A

Participants don’t just unlearn what you’ve taught them in a previous condition

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19
Q

Contrast

A

Participants may compare treatments, affecting behavior

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20
Q

Habituation

A

Repeated exposure to a stimulus that may decrease responsiveness to that stimulus

ex– traffic noise outside of your apartment becoming very unnoticeable because you are so used to it

21
Q

Dishabituation

A

when an organism responds to a previously familiar stimulus as if it were new again.

ex– background music: you get used to it but you would notice if it stopped or changed in a noticeable way

22
Q

Sensitization

A

when a person or organism becomes more responsive or reactive to a stimulus after being exposed to it, often due to a previous intense or disturbing experience

23
Q

Testing affects

A

Fatigue– Participating in repeated trials can result in poorer performance in later conditions
Exhaustion, frustration, boredom, etc
Practice– Participating in repeated trials can result in better performance in later conditions

24
Q

Attrition

A

random dropout
Ex– people move away from area
Unrelated to the topic of study

25
Q

Participant mortality

A

unequal rates of dropout related to topic of study
Ex– results in changes to the sample
Can result in confounding
Worse than attrition

26
Q

Threats to validity

A

History– historical/cultural change occurs during study in between your measurements
Maturation– Development occurs between measurements
If you are already studying development this is not an issue

27
Q

Counterbalancing

A

Equal numbers of participants experience different orders of conditions.
For example, half start with A, then V, while the other half start with V, then A.
Order is assigned randomly.
Full counterbalancing ensures fairness and minimizes order effects in experiments.

28
Q

of different orders= number of conditions

A

2 conditions = 2 X 1 = 2
3 conditions= 3 X 2 X 1 = 6
4 conditions = 4 X 3 X 2 X 1 = 24

29
Q

Latin square design

A

When you have 3 conditions, you create 3 orders.
It alternates the order of conditions, like A before B, or B before A, and similarly for A and C, and B and C.
Helps prevent order effects in experiments with multiple conditions.

30
Q

Why use different orders

A

Constant Order: When you always present conditions in the same sequence.
Impact on Internal Validity: Can mix up the effects of conditions with the order they’re presented in.
Balancing Effects: Helps distribute any order-related impacts evenly, making them easier to ignore unless they’re unusual (e.g., contrast effects).
Detecting Carryover: You can spot carryover effects by looking at how performance varies with different orders, making the order itself a factor in your study (included in statistical analysis).

31
Q

When to use a within-subject design

A

Best to use when the extraneous variable is related to the independent variable
Best to use when it is difficult to find/recruit participants
You need less people

32
Q

2X2 factorial design

A

IV #1 has two levels and IV #2 also has two levels
2X2 = 4– four different conditions
Each condition is a combination of the levels of the two different IV’s
Example–
Rehearsal, US words
Rehearsal, UK words
Rehearsal and imagery, US words
Rehearsal and imagery, UK words

33
Q

3X2 factorial design

A

IV #1 has three levels and IV #2 has two levels
six different conditions
Example–
Rehearsal, US words
Rehearsal, UK words
Rehearsal and imagery, US words
Rehearsal and imagery, UK words
Elaboration, US words
Elaboration, UK words

34
Q

2X2X2 factorial design

A

Adding another independent variable– 3 IV’s
IV 1 has two levels, IV 2 has two levels, and IV 3 has two levels
Eight different groups
Easiest to do two different tables

35
Q

Factorial designs

A

Can have factorial designs for either between subjects or within subjects designs
Can have factorial designs for randomized or matched designs

36
Q

Purpose of factorial designs

A

Manipulation two variables in the same experiment is more efficient than conducting two studies
Can look at the interaction or joint influence of the two IV’s

37
Q

Why do scores vary?

A

Independent variable
- Quasi IV (not manipulated, preexisting difference)
- Extraneous variables
o Testing conditions, individual differences

38
Q

Population and Sample

A

Every sample you take will have a different mean
- Population has only one mean (Population mean)
- Each sample has a mean of its worm (Sample Mean)

39
Q

The difference between a sample mean and the population mean = Sampling Error

A

farther away the sample mean is from the population mean, the larger the error

40
Q

Standard Error (of the mean)

A

Tells us how close or far sample means are from the population mean

“How much do sample averages differ from the true average in the entire population?”

41
Q

Std dev

A

the more variability there is in the scores themselves, the more variability there will be in the means you obtain from different samples

understanding the spread or consistency of scores or results from your experiment

42
Q

N – sample size

A

the smaller your sample is, the greater the likelihood that the sample mean will differ from the population mean

43
Q

P value

A

P < .05 is good – it means we trust that observed differences weren’t just due to luck

44
Q

Degrees of Freedom

A

Used to find the critical value (p= .05) of a statistic

45
Q

Null Hypothesis (H0)

A
  • No difference in means
  • Mu = Population mean

ex– caffeine consumption and ability to code has no correlation

46
Q

Alternative hypothesis (H1)

A
  • Samples came from different populations
  • Means are different
  • same as research hypothesis
47
Q

If the probability is less than or equal to .05

A
  • The difference is statistically significant
  • Reject the null hypothesis
48
Q

If probability greater than .05

A
  • Fail to reject the null hypothesis
  • Difference is nonsignificant
49
Q

We never accept the null hypothesis

A

We say that the groups were not significantly different, not that they’re the same
Results are nonsignificant, not insignificant
A result that’s significant at p < .01 isn’t more significant than one at p < .05

PSYCH 250 (3 decks)

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