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Research Methods Flashcards

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1
Q

content validity

A

Evidence that the content of a test corresponds to the content of the construct it was designed to cover

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2
Q

Ecological validity

A

evidence that the results of a study, experiment or test can be applied, and allow inferences, to real-world conditions.

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3
Q

reliability

A

the ability of the measure to produce the same results under the same conditions.

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4
Q

test-retest reliabillity

A

The ability of a measure to produce consistent results when the same entities are tested at two different points in times.

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5
Q

Correlational research

A

observing what naturally goes on in the world without directly interfering with it.

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6
Q

Cross-sectional research

A

This term implies that data come from people at different age points with different people representing each age point

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7
Q

Experimental research

A
  • One or more variable is systematically manipulated to see their effect (alone or in combination) on an outcome variable.
  • Statements can be made about cause and effect
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8
Q

Systematic variation

A

differences in performance created by a specific experimental manipulation

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9
Q

unsystematic variation

A

Differences in performance created by unknown factors. (age, gender, IQ, Time of Day, Measurement error etc.)

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10
Q

Randomization

A

Minimizes unsystematic variation

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11
Q

Frequency distributions (AKA Histograms)

A

A graph plotting values of observations on the horizontal axis, with a bar showing how many times each value occurred in the data set.

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12
Q

The ‘Normal’ Distribution

A
  • Bell shaped
  • Symmetrical around the centre
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13
Q

Properties of frequency distributions

A
  • Skew
  • Kurtosis
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14
Q

Skew

A
  • The symmetry of the distribution
  • Positive skiew (scores bunched at low values with the tail pointing to high values)
  • negative skew (scores bunched at high values with the tail pointing to low values)
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15
Q

Kurtosis

A
  • the ‘heaviness of the tails
  • leptokurtic = heavy tails
  • Platykurtic = light tails
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16
Q

Deviance

A
  • we can calculate the spread of scores by looking at how different each score is from the center of a distribution eg: the mean
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17
Q

Sum of squared errors (SS)

A
  • indicates the total dispersion, or total deviance of scores from the mean
  • it’s size is dependent on the number of scores in the data.
  • More useful to work with the average dispersion, known as the variance
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18
Q

The sum of squares, variance, and standard deviation represent the same thing

A
  • the ‘fit’ of the mean to the data
  • the variability in the data
  • how well the mean represents the observed data
  • error
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19
Q

Population

A
  • The collection of units (be they people, plankton, plants, cities, suicidal authors etc.) to which we want to generalize a set of findings or a statistical model.
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20
Q

Sample

A

a smaller (but hopefully representative) collection of units from a population used to determine truths about that population

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21
Q

calculating ‘error’

A
  • a deviation is the difference between the mean and an actual data point
  • deviations can be calculated by taking each score and subtracting the mean from it:
    deviance=outcome(i)-model(i)
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22
Q

Sum of squared errors

A
  • we could add the deviations to find out the total error
  • deviations cancel out because some are positive and others negative
  • therefore, we square each deviation
  • if we add these squared deviations we get the Sum of Squared Errors (SS)
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23
Q

Mean squared error

A

Although the SS is a good measure of the accuracy of our model, it depends on the amount of data collected. To overcome this problem we use

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24
Q

The standard error

A
  • SD tells us how well the mean represents the sample data
  • but, if we want to estimate this parameter in the population, then we need to
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25
Q

why can’t we prove certainty in stats

A
  • because it’s inferential statistics
  • it’s based on probability
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26
Q

Type I error

A
  • occurs when we believe that there is a genuine effect in our population, when in fact there isn’t
  • the probability is the a-level (usually .05)
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27
Q

Type II error

A
  • occurs when we believe that there is no effect in the population when, in reality, there is.
  • The probability is the B-level (often 0.2)
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28
Q

regression has no IV, DV

A

Predictor = IV
Outcome = DV

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29
Q

misconceptions around p-values No1

A

A significant result means that the effect is important =
no, because significance depends on sample size

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30
Q

misconceptions around p-values No 2

A

A non-significant result means that the null hypothesis is true = no, a non-significant result tells us only that the effect is not big enough to be found (given our sample size), it doesn’t tell es that the effect size is zero.

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31
Q

misconceptions around p-values No 3

A

A significant result means that the null hypothesis is false? = no, it is logically not possible to conclude this

32
Q

Researcher degrees of freedom

A

A scientist has many decisions to make when designing and analysing a study

33
Q

Continuous DV over categorical DV

A

ANOVA

34
Q

Noir

A

Nominal
Ordinal
Interval
Ratio

35
Q

Measurements of error

A

Deviances

36
Q

how to get rid of a 0 deviance

A

square it

37
Q

Standard deviation

A
  • estimate of error
38
Q

crombachs alpha

A

tests internal validity

39
Q

random allocation

A
  • attempt to control for individual difference
  • each person has an equal chance
40
Q

Matched pairs

A

-is a within subjects
- doesnt test same subject but matches on characteristics

41
Q

mean squared error

A

refers to variance

42
Q

mean

A
  • the one number that bests represents a normal distribution
  • best represents central tendency
  • it needs to be thought of as a model not a number
43
Q

Degrees of Freedom

A

The number of scores that are free to vary

44
Q

Variance

A

SS/df

45
Q

within group variance

A

the estimate of error

46
Q

test statistic for and ANOVA

A

F statistic

47
Q

Big F

A

is significant and is ratio divided by error

48
Q

levels

A

the divides with the IV’s

49
Q

ANOVA

A
  • tests the mean differences between levels
  • controls for type I error
50
Q

Cheffe’s

A
  • Post-hoc
  • the most conservative (least likely to make a Type I error)
  • keeps a large estimate of error
51
Q

when to do post-hoc

A

After
- 3 or more levels
- IFF
- main effect is significant

52
Q

factorial designs

A
  • have multiple IV’s (Factors)
53
Q

interaction

A
  • the effect of one IV depends upon the level of another IV
  • are more important to interpret than main effect
  • should be interpreted first
  • IV-DV-IV
54
Q

how to check interaction

A
  • visual inspection, if the lines are parallel there is no interaction
  • the differences between cell means (if the differences are the same there may be no interaction)
55
Q

how many hypothesis for a two-way ANOVA

A
  • 3
  • one for the IV A on the DV
  • IV B on the DV
  • Interactional effect on the DV
56
Q

If there is a significant interaction

A
  • use a simple effect analysis
57
Q

Independent design

A
  • different entities in all conditions
58
Q

Repeated measures design

A
  • the same entities in all conditions
59
Q

Mixed design

A
  • different entities in all conditions of at least one IV, the same entities in all conditions of at least one other IV
  • SPANOVA
60
Q

you can have a significant interaction without having

A
  • significant main effect
61
Q

ANOVA (Between)

A

main effect

62
Q

ANOVA (Within)

A

Error

63
Q

F

A
  • total variability
    =MS between/MS Within
64
Q

all statistical statements

A

Statistic, degrees of freedom, value, significance, effect size

65
Q

tests of between-Subjects effects

A
  • the amount of stats needed depends on factor levels
66
Q

repeated measures attempt to control for

A
  • individual differences
67
Q

when accounting for differences in statistical equations

A
  • you must make changes to both numerator and denominator
68
Q

Advantages of repeated measures

A
  • Sensitivity (unsystematic variance is reduced, more sensitive to experimental effects)
  • Economy (requires less participants)
69
Q

disadvantage of repeated measure

A
  • practice effect
  • fatigue
70
Q

can you use post hoc for repeated measure?

A
  • no, you need to use contrast (pre-planned comparison)
71
Q

Covariance

A

the amount to which scores vary together
- needs to be measured before everything else

72
Q

how to tell if it is an ANCOVA

A
  • there is no interaction
73
Q

when you have a significant covariate

A

-make an adjustment to the means
- compare the estimated margin means

74
Q

what N is needed to reach normality

A

30

75
Q
A

Psychology (9 decks)

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