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RS3 > Definitions > Flashcards

Definitions Flashcards

1
Q

Ratio scales

A

have equal intervals between adjacent scores on the scale and an absolute 0.

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2
Q

Interval scales

A

have equal intervals between adjacent scores but do not have an adjacent 0.

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3
Q

Ordinal scales

A

have some sort of order to the categories but the intervals between adjacent points on the scale are not necessarily equal

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4
Q

Extraneous variables

A

might have an impact on the other variables that we are interested in but may have failed to take these into account when designing the study

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5
Q

Confounding variables

A

are a specific type of extraneous variable that is related to both of the main variables we are interested in.

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6
Q

correlational designs

A

those that investigate relationships between variables

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7
Q

experimental designs

A

where the experiment manipulates the IV to see what effect this has upon the DV

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8
Q

Quasi-experimental designs

A

involve seeing if there are differences on the DV between conditions of the IV. Unlike experimental designs, there is no random allocation of participants to the various conditions of the IV.

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9
Q

within participants deisgns

A

have the same participants in every condition of the IV. Each participant performs under all conditions in the study.

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10
Q

order effects

A

are a consequence of a within participants design whereby completing the conditions in a particular order leads to differences in the DV that are not a result of the manipulation of the IV.

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11
Q

Counterbalancing

A

Where you systematically vary the order in which participants take part in the various conditions of the IV.

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12
Q

Between participants designs

A

have different groups of participants in each condition of the IV. Thus, the group of participants in one condition of the IV is different from the participants in another condition of the IV

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13
Q

Between participants advantages

A
  • relative absence of practice and fatigue effects

- participants less likely to work out purpose of study

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14
Q

Between participants disadvantages

A
  • need more participants

- not as much control of confounding variables between conditions

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15
Q

Within participants advantages

A
  • need fewer participants

- greater control of confounding variables between conditions

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16
Q

Within participants disadvantages

A
  • increased likelihood of practice or fatigue effects

- participants more likely to guess purpose of the study

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17
Q

Population

A

consists of all possible people or items who/which have a particular characteristic

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18
Q

Sample

A

refers to a selection of individual people or items from a population

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19
Q

parameters

A

descriptions of populations whereas statistics are descriptions of samples

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20
Q

Measures of central tenendcy

A

give us an indication of the typical score in our sample. it is effectively an estimate of the middle point of our distribution of scores

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21
Q

frequency histogram

A

graphical means of representing the frequency of occurrence of each score on a variable in our sample

22
Q

stem and leaf plots

A

similar to histograms but the frequency of occurrence of a particular score is represented by repeatedly writing the particular score itself rather than drawing a bar on a chart.

23
Q

box plots

A

enable us to easily identify extreme scores as well as seeing how the scores in a sample are distrbuted

24
Q

outliers or extreme scores

A

are those scores in our sample that are a considerate distance either higher or lower than the majority of the other score in the sample

25
Q

Variance or variation of scores

A

indicates the degree to which the scores on a variable are different from one another

26
Q

variance

A

the average squared deviation of scores in a sample from the mean

27
Q

standard deviation

A

the degree to which the scores in a dataset deviate around the mean. it is an estimate of the average deviation of scores from the mean.

28
Q

kurtosis of a distribution

A

is a measure of how peaked the distribution is

29
Q

leptokurtic

A

is a very peaked distribution

30
Q

platykurtic

A

is a flat distribution

31
Q

skewed distributions

A

are those where the peak is shifted away from the centre of the distribution and there is an extended tail on one of the sides of the peak

32
Q

the p-value

A

is the probability of obtaining the patterns of results we found in our study if there was no relationship between the variables in which we were interested in the population.

33
Q

the null hypothesis

A

always states that there is no effect in the underlying population

34
Q

type 1 error

A

where you decide to reject the null hypothesis when it is in fact true in the underlying population

35
Q

type 2 error

A

where you conclude that there is no effect in the population when in reality there is. it represents the case when you do not reject the null hypothesis when in fact you should do because in the underlying population the null hypothesis is not true.

36
Q

one tailed hypothesis

A

is one where you have specified the direction of the relationship between variables or the differences between two conditions

37
Q

2 tailed hypothesis

A

where you have predicted that there will be a relationship between variables or a difference between conditions, but you have not precited the direction of the relationship between the variables or the difference between the conditions

38
Q

Measures of central tendency

A

gives us an indication of the typical score in our sample

39
Q

independent samples t-test

A

compares two groups of participants

40
Q

paired samples t-test

A

compares the same participants in two different conditions.

41
Q

calculating standard deviation

A
  1. square all the deviations from the mean
  2. add them up (gives us the sum of squares)
  3. calculate the average by dividing the sum of squares by the number of scores (gives us variance)
  4. take the square root of the variance (SD)
42
Q

Sampling distribution

A

hypothetical distribution.
where you have selected an infinite number of samples from a population and calculated a particular statistics (e.g. mean) for each one

43
Q

standard error

A

refers to the standard deviation of a particular sampling distribution

44
Q

factor

A

refers to an independent variable

45
Q

level

A

refers to a level of the independent variable

46
Q

ANOVA

A

is used when you want to compare more than two means.
it looks at whether there are differences in the means of groups.
it does this by comparing the group means to the grand mean and seeing how different each group mean is to the grand mean.

47
Q

total sum of squares

A

the total variance in data

48
Q

modal sum of squares

A

tells us the variation that the experimental manipulation explains

49
Q

residual sum of squares

A

tells us the total variation that is due to extraneous factors

50
Q

mean square

A

can be seen as the ‘average deviation’ from the mean

= sum of squares / degrees of freedom

51
Q

calculate within subjects sum of squares

A
  1. calculate the variance in an individuals scores
  2. compare each individuals data points to the individuals own mean
  3. add all of the participant variances together

RS3 (6 decks)

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