unit 2 lecture 2 Flashcards

(40 cards)

1
Q

Case studies

A

in depth reports about specific cases

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2
Q

in depth reports about specific cases

A

Case studies

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3
Q

Naturalistic observations

A

observing participants in real world contexts and taking a record of their behaviour

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4
Q

observing participants in real world contexts and taking a record of their behaviour

A

Naturalistic observations

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5
Q

Surveys/questionnaires

A

involves having individuals provide answers to a series of questions

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6
Q

involves having individuals provide answers to a series of questions

A

Surveys/questionnaires

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7
Q

Correlational research

A

provides a measure of the degree of association between two variables

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8
Q

Positive correlation

A

when higher values on one variable tend to co-occur with lower values on another
Positive correlations generate correlation co efficients of 0-1.00
Negative correlations generate correlation co efficients of 0- -1.00
Strong correlations between variables (whether positive or negative) do not mean that there is a causal relationship between the two variables

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9
Q

Casual direction problem

A

example: does curly cause fun or fun cause curlier hair

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10
Q

example: does curly cause fun or fun cause curlier hair

A

Casual direction problem

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11
Q

The third variable problem

A

potential variable that causes both curlier hair and more fun

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12
Q

potential variable that causes both curlier hair and more fun

A

The third variable problem

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13
Q

Experiments

A

– uniquely designed to provide answers about the casual relationships between variables… they have this advantage because they involve direct manipulation of one or more variables

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14
Q

Random assignment

A

to experimental conditions (curled vs not curled) helps to ensure that participants in the two groups are roughly equal on all other important characteristics

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15
Q

Confounding variables

A

– variables that differ between experimental conditions other than the one that the experimenter seeks to manipulate (bad to have these in experiment)

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16
Q

– variables that differ between experimental conditions other than the one that the experimenter seeks to manipulate (bad to have these in experiment)

A

Confounding variables

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17
Q

Independent variables

A

variable(s) being manipulated in an experiment

18
Q

variable(s) being manipulated in an experiment

A

Independent variables

19
Q

Dependant variable(

A

being measured in an experiment

20
Q

being measured in an experiment

A

Dependant variable(

21
Q

Experimental conditions

A

the one involving some active treatment such as administration of a drug, providing some sort of therapy or curling their hair

22
Q

the one involving some active treatment such as administration of a drug, providing some sort of therapy or curling their hair

A

Experimental conditions

23
Q

Control condition –

A

the one involving no active treatment but that is used as a basis for comparison such as administration of a placebo, giving them some sham type of therapy or playing with their hair without curling it

24
Q

the one involving no active treatment but that is used as a basis for comparison such as administration of a placebo, giving them some sham type of therapy or playing with their hair without curling it

A

Control condition –

25
Between-subjects design
when different people participate in different conditions of an experiment
26
when different people participate in different conditions of an experiment
Between-subjects design
27
Within-subjects design
when the same people each participate in all conditions of an experiment
28
when the same people each participate in all conditions of an experiment
Within-subjects design
29
Descriptive statistics
summarize data)
30
inferential statistics
draw conclusions
31
Mean
the arithmetic average a group of scores
32
the arithmetic average a group of scores
Mean
33
median
– the middle highest value in a group of scores
34
– the middle highest value in a group of scores
median
35
mode
most common value in a group of scores
36
most common value in a group of scores
mode
37
Standard deviation
higher standard deviations reflect scores that are more dispersed further away from the mean score
38
higher standard deviations reflect scores that are more dispersed further away from the mean score
Standard deviation
39
The main purpose of inferential statistics is
statistical hypothesis testing
40
To be statistically significant
a difference between conditions or a correlation between variables must be substantially greater than the standard deviation of scores The same difference in sores may or may not be statistically significant depending on the standard deviation