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research methods > RM Introduction > Flashcards

RM Introduction Flashcards

1
Q

What order do you plan an experiment

A

Order:
Theory
Research Questions
Research Design
Hypotheses
Data Collection

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2
Q

What is the experimental design process

A

Create hypothesis
Translate hyp into treatment conditions or levels
Administer treatment groups to P
Measure performance on response

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3
Q

Treatment conditions
(the variables being manipulated)
are commonly known as…

A

Independent variables

eg. drug or placebo
amount of caffeine

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4
Q

Response measures are commonly known as…

A

Dependant variables

eg. blood sugar levels
performance on eye test

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5
Q

Name the 3 types of IV

A

Quantitative
Qualitative
Classification

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6
Q

Quantitative

A

Variables represent variation in amount

eg. amount of drug, loudness of noise, difficulty of test

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7
Q

Qualitative

A

Variables represent variations in kind or type

eg. teaching strategy, type of psychotherapy

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8
Q

Classification

A

Variables represent characteristics that are intrinsic to the subjects/participants

eg. ex, species, age group

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9
Q

What are Nuisance variables

A

potential independent variables
if left uncontrolled, could exert a systematic influence on the different treatment conditions

eg. different researchers may produce an “experimenter effect”,
time of day, individual differences

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10
Q

Uncontrolled nuisance variables are also known as…

A

Confounding variables
– they confound any inference derived from the experiment

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11
Q

What should a good dependent variable should capture

A

The hypothesised differences

The observed data should be somehow dependent on the independent variable

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12
Q

SCENARIO:
Two labs are practically identical, except that temperature cannot be controlled
Temperature variations may lead to…

A

Systematic differences in task performance

Solution - random allocation of treatment conditions
(many 0.5, 1.0, 1.5ml caffeine per P) each lab gives an equally likely “chance” that different random temperatures will be associated with the different treatment conditions

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13
Q

A completely randomised design involves…

Random assignment helps to prevent…

Also known as a between subjects or independant-groups design

A

Each P is randomly assigned to one of the treatment conditions

non-manipulated systematic differences (confounds) from occurring between treatment groups

Observed differences are observed between groups of participants

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14
Q

Randomized block design uses…

Referred to as a repeated measures design or a within-subjects design

A

Blocks of subjects who are matched closely on some relevant characteristic

A common procedure is to treat a subject as a “block”, wherein the subject serves in all the treatment conditions of an independent variable

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15
Q

A research hypothesis

A

A fairly general statement about the presumed nature of the world inspiring a specific experiment

eg.
“Physical exercise decreases dementia symptoms”

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16
Q

A statistical hypothesis

A

A precise statement about the parameters of distributions for different treatment populations

eg. Mean dementia scores will be lower for the Exercise group than for the No-Exercise group
(more than what we expect to observe by chance!)

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17
Q

What 2 types of Statistical hypotheses are stated

A

Null hyp
Alternative hyp

(mutually exclusive so both should never be true on the treatment parameters)

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18
Q

What does this Greek letter represent
µ

A

The MEAN

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19
Q

The parameters of the distribution for each treatment population include which 2 things

A

The mean
µ

The standard deviation σ

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20
Q

Assuming a normal distribution (bell curve),
what represents the length and the width

A

Length- µ (mean)

Width- σ (standard deviation)

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21
Q

What symbol represents the Null hypothesis

A

H0

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22
Q

What symbol represents the Alternative hypothesis

A

H1

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23
Q

A classic statistical test seeks to do what

This is the same as saying that no treatment effects are present in the population

A

Seeks to accept or reject the null hyp

24
Q

Generally, what does the equation for the Null hyp express

A

It expresses the equality between the different treatment populations

25
Q

What does this Null equation represent:

𝑯𝟎: 𝝁𝟏= 𝝁𝟐= 𝝁𝟑

A

𝑯𝟎: 𝝁𝟏= 𝝁𝟐= 𝝁𝟑
-for experiment with 3 levels

No treatment effects are present in the population

26
Q

If the treatment parameters do not agree with the null hypothesis
(i.e., one or more of the differences between treatment means is greater than expected by chance, what must we do

A

We reject the null hyp in favour of its inverse, the alternative hypothesis (𝐻1 or 𝐻𝐴).

27
Q

Usually, what does 𝐻1 simply state

A

the parameters are NOT all equal between treatment populations

28
Q

When do we decide NOT to reject 𝐻0 (Null) and instead reject 𝐻1 (Alternative)

A

when our parameter estimates do not differ beyond what would be expected by chance

29
Q

When we gain a sample from a population
our observed parameters are the…

A

sample mean 𝒙
sample standard error s

These are approximations to the “true” population parameters µ (mean) and σ (SD)

30
Q

Why do we use random sampling

A

To choose a sample that is representative of the underlying population

Population samples may deviate from the population, which is determined by random variation (a.k.a. chance)

31
Q

To compare existing statistical data with data from our study, what distribution would we use

A

An F-distribution

This allows us to define chance, by comparing our observed group differences to the theoretical F-distribution

32
Q

What does the p-value represent

A

The probability that you could have had a statistical difference due to chance

33
Q

The lower the p-value…

A

The less likely 𝐻0 is true

34
Q

In practice, to decide whether to reject 𝐻0 or not, we quantify differences between groups as an F-value (our test statistic)

What do we do next
(hint: probability p value)

A

Next, we compute the probability (p-value) of finding this F-value (or greater), given that 𝐻0 is true, by comparing it to the F-distribution

35
Q

What does the statistical threshold 𝛼 represent

A

Alpha 𝛼=0.05

36
Q

If the p-value for a hypothesis test is LESS than 𝛼=0.5, we say that it is what

A

“statistically significant”

and we reject 𝐻0 (and implicitly accept 𝐻1)

37
Q

If the p-value for a hypothesis test is MORE than 𝛼=0.5, we say that it is what

A

NOT statistically significant

and we fail to reject” 𝐻0
(accept the 𝐻0)

38
Q

The F-test procedure does not guarantee that a correct inference will be drawn

there is a possibility that 𝐻0 is wrong

What are the two basic types of error:

A

Type I Error (False positive)

Type II Error (False negative)

39
Q

Type I Error (False positive)

A

Decision:
We have rejected 𝐻0
and accepted 𝐻1

Reality:
We should have accepted 𝐻0 as
𝐻0 is true (no difference between groups)
𝐻1 is false

40
Q

Type II Error (False negative)

A

Decision:
We have failed to reject 𝐻0
and instead rejected 𝐻1

Reality:
We should have rejected 𝐻0 as
𝐻0 is false (real difference between groups)
𝐻1 is true

41
Q

When you change your alpha threshold…

A

you are changing the capacity for more chances of a type 1 or type 2 error

42
Q

What is a true score

A

A measurement without error
Is approached as the number of items in a test increases.

43
Q

Increasing length of a test will do what to the reliability

A

Increase the reliability

since the randomness associated with individual items tends to average out

44
Q

How do you calculate the
Test score variance?

A

True score variance
+ The error variance
= Test score variance

eg. True score variance 35 + the error variance 15 = Test score variance 50

45
Q

How do we calculate the reliability
(proportion of test score variance due to true score variance)

A

True score variance 35
÷Test score variance 50
= 0.70

46
Q

Which type of validity is most subjective?

A

Face validity

47
Q

Which type of validity is the most theoretically based?

A

Construct validity

48
Q

How do you calculate an upper limit of its validity based on its reliability?

A

The upper limit is the square root of the reliability

49
Q

Why is experimental control used?

A

To handle nuisance variables
that confound scientific inference

eg. randomised design

50
Q

Psychologists’ goal is to minimise the chance that the decision is what

A

erroneous (type 1 or type 2)

51
Q

A researcher does a survey randomly calling phones that have land lines.
People who only have cell phones are not sampled.
This is an example of…

A

Undercoverage bias

since those with only cell phones are not only undercovered but not covered at all

52
Q

A radio station asks readers to phone in their choice in a daily poll. This is an example of…

A

Self-selection bias

since those with strong feelings are most likely to respond

53
Q

A researcher surveys people who have been in therapy for 5 years with the same psychotherapist. This is an example of…

A

Survivorship bias

Those who stay for 5 years may be more satisfied with their therapist than average.
May also have more severe problems if they stay in therapy so long

54
Q

If treatment is successful more in women than men
What is the interaction?

A

The interaction is between gender and treatment

55
Q

The sum of the effects of the unmeasured variables can be estimated by…

A

by the within-group variances

Differences within a group are due to unmeasured variables

so the variances within the groups can be used to estimate the effects of the unmeasured variables

research methods (12 decks)

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