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Psychology Leeds, sem 2 > RS4 > Flashcards

RS4 Flashcards

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1
Q

What is the basic comparative design?

A

Comparing the scores of one group with another.

Usually the mean

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2
Q

What is the basic correlational exam?

A

Researcher is measuring two or more different variables at the same time in a single group of cases

See if there is correlation between those variables

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3
Q

What is a correlation coefficient?

A

Standard statistical index measuring (i) the direction and (ii) the strength of a relationship between two variables

Ranges from -1 to +1

It is an effect size itself:
.1 is small
.3 is medium
.5 is large

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4
Q

What is the coefficient of determination?

A

r2 - r squared.

The proportion of variance in one variable shared by the other

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5
Q

An r of .6 indicates how much variance is shared?

A

36%

.6x.6 x 100

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6
Q

What is covariance? what is the standard measure of covariance?

A

The relationship between how much scores on two variables deviate from their respective means. So if X and Y both deviate similarly then the covariance is positive, if they do not similarly deviate then the covariance will be negative.

The correlation coefficient is the standard measure. Makes it unitless

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7
Q

What are the four types of correlation coefficients?

A

Spearman’s rho
Kendall’s Tau
Phi-coefficient
Biserial point correlation

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8
Q

When would you use spearman’s rho and Kendall’s Tau-b?

A

Smaller data set, with a large number of tied ranks (the same value)

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9
Q

When would you use point biserial correlation?

A

When one variable is dichotomous (categorical with 2 categories i.e. dead or alive), NOT when there is an underlying continuum (pass/fail)

and the other variable is continuous

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10
Q

When would you use the phi correlation (2x2)

A

Correlating between two categorical (nominal) but dichotomous variables

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11
Q

What does reliability refer to in terms of a test? Some ways it is measured…

A

The consistency over time:
- Test-retest

Consistency of the items within the measure:

  • Split half
  • Cronbach’s alpha
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12
Q

What does validity refer to in terms of a test?

A

The extent a measure measures the underlying construct

Criterion related validity:
- Predictive and concurrent validity

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13
Q

Minimum correlation of test-retest reliability expected?

A

Minimum correlation of 0.6 expected

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14
Q

Problems with split half reliability?

A

Only estimates reliability of half

Not obvious which way to split the test

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15
Q

What reliability coefficients mean it is how reliable?

A

<0.6 is suspect

.6-7 satisfactory

> .8 is excellent

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16
Q

Methods of estimating criterion related validity?

A

Predictive validity
- See how well your test predicts some later obtained criterion scores

Concurrent validity
- See how well your test scores concurrently predict obtained criterion scores

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17
Q

What is linear regression?

A

Technique used to predict an outcome score. Examines the amount of variance that can be explained by one predictor (simple) or more than one (multiple).

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18
Q

In regression what are the dependent and independent variables called?

A

Dependent is an outcome

Independent is a predictor

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19
Q

What equation is used in regression?

A

The equation of a straight line:

Y = b0 + b1X

Y = outcome variable or expected value of y given a value of x
X = predictor variable
b0 = intercept (value of Y when X is 0)
b1 = regression coefficient (gradient - strength/direction of the relationship)
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20
Q

How do we test the significance of the predictive model in simple regression?

A

F ratio test and R2.

F ratio expressed as the Mean Squares

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21
Q

General aim of the least squares method for simple regression?

A

Goal is to minimise the sum of the squared differences (error) between the observed value of the dependent (outcome) variable and the predicted value (provided by regression line)

Sum of the squared residuals.

In this way it is trying to get the best fit possible.

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22
Q

Does the intercept have to make ‘sense’

A

No - the intercept may or may not actually make sense in real life

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23
Q

How does the simple linear regression equation change when you have to estimate population parameters?

A

The y becomes yhat and the beta’s become b’s

ŷ = mean value of y for a given value for x

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24
Q

What are residuals in regression?

A

The distance from the best fit line also called the error.

Always add up to 0

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25
Q

Why do you square residuals

A
  1. makes them all positive

2. Emphasises the larger deviations - exaggerates them

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26
Q

What is the sum of squared residuals?

A

The squared residuals (errors) all added together.

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27
Q

How do we determine how good the linear regression model is?

A

We are comparing it to the line without the predictor variable (the straight line) - if it is good it should reduce the distance or the SSE (sum of squared errors)

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28
Q

Is the expected value of y an exact value?

A

No it’s actually just the mean of a distribution

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29
Q

What is the least squares criterion?

A

Min Σ (yi ŷi)2

yi = actual value

ŷi = predicted

Minimum of the sum of the squared residuals (the y’s)

This formula is used to calculate the SST, SSR and SSE

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30
Q

What is the centroid?

A

The point of the mean x value and mean y value, the line of best fit must go through this

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31
Q

Interpreting the regression equation ŷi = 0.1462x - 0.8188?

A

ŷi = 0.1462x - 0.8188

b1 = 0.1462(1) (gradient)

b0 = -0.8188

SO for every £1 the bill amount (x) increases the tip (ŷi) increases by 0.1462- this part makes sense

if the bill amount is zero (x) then the expected tip is -0.8188: this obviously doesn’t make sense, but it doesn’t have to.

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32
Q

What is the SSR in simple linear regression model?

A

It’s the Sum of squares for residual.

The SST (total) is the SSE (error) in the straight line with maximum error

In our model we want less error. The SSR (regression sometimes called model) is this SST - SSE. The better the line fits the data the smaller the SSE will be and the bigger the SSR (model) will be. We are comparing to our bad model when SST = SSE

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33
Q

In the simple linear regression equation what is the SSE?

A

The difference between the predicted value of y (from the model) and the actual value at each point. The predicted value has been calculated by plugging in the x values to the simple linear regression equation.

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34
Q

What is the coefficient of determination in regression?

A

Quantifies the ratio between SSR (model) and SST (total).

Shows us how well the model fits

It is an r2 value

Do SSR/SST, can times the r2 by 100 to get a percentage

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35
Q

What is multicollinearity in multiple regression?

A

The fact that independent (predictor variables) variables can also be related to EACH other as well as the outcome (dependent variable)

This is bad if they are because it can be harder to discern what factor is really affecting the dependent variable, If this happens they are redundant and you’d take out related variables

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36
Q

Disadvantages to having lots of predictor variables in multiple regression?

A

Means that there are many more relationships to consider, have to account for each predictors relationship to the outcome as well as to each other.

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37
Q

What is the (estimated) multiple regression equation?

A

ŷ = b0 + b1x1 + b2x2 + … (however many predictors)

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38
Q

Each coefficient in multiple regression should be interpreted as what, for example the b1 in b1x1?

A

Each coefficient is the estimated change in y (outcome) corresponding to a 1 unit increase in that predictor, when all other variables are held constant.

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39
Q

What is the adjusted R squared?

A

The R squared adjusted for the number of independent variables (lower than R squared)

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40
Q

How can you use Standard Error (SE) in multiple regression?

A

Tells us how wide the ‘band’ is around the regression line - in the independent (predictor variables units)

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41
Q

How high can the VIF and tolerance be to indicate the variables are not multicollinearity?

A

VIF should be below 10

Tolerance should be higher than 0.2

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42
Q

How many participants should there be in regression analysis?

A

Liberal: 15 ptps per predictor

Conservative: Tabachnick and Fidell (1996) suggest 50 + 8 x(number of IVs)

Stepwise suggests 40 per IV

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43
Q

What is random sampling variability, and sampling error?

A

Random sampling variability is the fact that when you sample a population it may come out different with different samples i.e. you might have a different sample mean several times

Sampling error is the fact that the sample you have will be different from the mean population score

44
Q

What is the central limit theorem?

A

Formulated by pierre laplace (1810):

The idea that different samples taken from the same population will often have different sample stats such as mean and SD - random sampling variability

45
Q

What is the Standard error of the mean?

What is the standard deviation?

A

The standard error measures the expected difference (due to chance) between the sample mean and that of the population

The standard deviation is achieved becuase of random sampling variability, the standard error of the mean is essentially the standard deviation of a distribution of samples

46
Q

What’s the difference in a z score and a t score formula?

A

The t score formula uses estimated standard error instead of standard deviation

used when we need to estimate the standard deviation (error) of a population because we don’t know it - most situations, in this situation we use N-1 to estimate the SD formula

47
Q

How do we estimate confidence limits in t scores?

How does this then translate into Confidence intervals

A

We know that 95% of the scores will be +/-1.96 SEs (SDs) from the mean (if the sample is normally distributed) - when samples are above 100, if they are lower you have to increase that SE figure.

The Confidence Interval will be the confidence interval x the SE(mean) so:

Upper 95% CI: Sample mean + (1.96 x SE(mean))

(-) for lower 95% CI

48
Q

If there is no direction specified in the prediction of a test, is this a one tailed or two tailed test?

A

Two tailed

49
Q

Is the RQ: ‘does alcohol improve problem solving ability?’ a one tailed or two tailed hypothesis?

A

1 tailed

50
Q

SE equation?

A

population mean/square root of N

51
Q

Do the SD and population N decrease/increase to get the SE to decrease

A

SD decreases

N increases

52
Q

What is the coefficient of multiple determination? How could you get it to increase?

A

R2, you could add addition predictor variables to a multiple regression equations

53
Q

What is the formula Z = (sample mean(Xbar) - population mean(μ)) / SD(σ)

A

The way of making a linear transformation of a normal variable into a standard normal variable

54
Q

What is psychometric testing?

A

Measuring a psychological variable with objective numerical or categorical measures

55
Q

Advantages of questionnaires?

A
  • Don’t have to manipulate variables
  • Quick and easy to administer
  • Potentially large number of responses
  • Anonymous responding may produce more truthful responses
56
Q

Disadvantages of questionnaires?

A
  • Response rate is low - internet
  • Difficult to correct misunderstanding (if you aren’t there then people may interpret questions in different ways)
  • Potential influence of question order
  • potential influence of question wording
57
Q

What different formats of questionnaires are there?

A

Open questions

Closed questions;

  • Yes/No
  • PANAS - positive and negative affect schedule - continuum of agreement
  • Likert-like
58
Q

Advantages and disadvantages of open questions?

A

Advantages:

  • Gets all the info
  • Does not lead respondent
  • More naturalistic

Disadvantages:

  • Can be difficult to complete
  • Difficult to code and analyse
  • Poor when numeric results required
59
Q

Advantages and disadvantages of closed questionnaires?

A

Advantages:

  • Easy to code and analyse
  • Good when a numerical result is required
  • Quick for respondents to complete

Disadvantages:

  • Can encourage bias (if worded badly)
  • Can miss possible answers
  • Create opinions where none exist
60
Q

Why have lots of different items on a psychometric questionnaire?

A

So that you can try and eliminate error.

The observed response is a combination of true response and error.

61
Q

What are the possible sources of error in psychometric questionnaires?

A

Ptp must read and understand question

Ptp must decide on their attitude

Ptp must match their attitude to the scale in the questionnaire

Things can go wrong at any of these steps

62
Q

How can you minimalise issues with misinterpretation in questions in questionnaires?

A

Short, clear and unambiguous

be very clear with how you define terms that can be interpreted differently, use simple language

Use don’t know or unambiguous

Avoid double barrelled questions i.e. only ask about one thing, and clearly do it.

Avoid quantitative statements:
‘Private education is better than non-private’
- could say no and mean that private was the same or worse

Avoid leading questions

Avoid loaded terms

Avoid hypothetical situations

Avoid double-negatives

63
Q

Why do we use 5 or 7 items in a likert response format?

A

Attitude is a continuum

5/7 have a reasonable number of choices but are not unmanageable

More than 7 - labelling becomes more difficult

If you leave them unlabelled this can leave to ambiguity

64
Q

What is the funnel approach to question order in psychometric questionnaires?

A

Very broad questions going down to narrow specific questions

This encourages completion and allows for logical expression of ideas§

65
Q

Why use a scale rather than a single item?

A

May be several dimensions to the attitude we want to measure

Minimise the effects of random error

66
Q

How can you balance a scale, what is a balanced scale?

A

A balanced scale is one where the questions are framed from both sides of a viewpoint, so do that.

This can reduce acquiescence bias.

67
Q

What is acquiescence bias?

A

Tendency to agree with a statement regardless of its content

68
Q

How many items do you want in a pilot questionnaire?

A

More than you want overall - some will not be suitable

69
Q

What is internal reliability?
How should items relate to each other?
How do you measure it?

A

The internal consistency of a test or scale

Items should correlate with each other. One set of items should correlate with another set.

Use Cronbach’s alpha or split-half

70
Q

How do we deal with variance in item scores?

A

Items with large variance is good. They will discriminate between high and low scorers

71
Q

What is item-item correlation?

A

How items correlate with others: look at overall correlation matrix

If an item is removed then you have to relook at all the data again

72
Q

What is the item-total approach?

A

Reject items that do not correlate with the total score.

Preferred way to do this is correlate it with the total excluding the item you are measuring

73
Q

How might you perform split-half reliability?

A

Does one half correlate with the other:

  • Odd and even numbers
  • First half with the second half
  • Random selection
74
Q

What is the spearman-brown reliability coefficient?

What is considered sufficient?

A

The coefficient for split half reliability

0.7 is normally adequate but 0.6 may be allowed.

1 + [(n -1) x known reliability]

n is normally 2, but can be more if you need to determine how long your questionnaire needs to be.

75
Q

What is cronbach’s alpha?

A

The average of every correlation between every possible half of the items with every other possible half

Best measure of reliability - does not depend on one particular split

76
Q

How do you calculate cronbach’s alpha coefficient?

A

Add all the spearman-brown coefficients and divide by the number of them

Using means squares:

between people variance

77
Q

Main difference in item analysis and factor analysis?

A

Item analysis works on the assumption that we are only measuring one construct - and all questions relate to that.

Factor analysis is measuring the different factors that make up your questionnaires

78
Q

What is factor analysis?

A

Data reduction statistical technique

Takes a large set of variables and reduces it using smaller set of factors/components that are independent of each other.

79
Q

What is deductive inductive and abductive reasoning?

A

Deductive:

  • All As and Bs
  • All Bs are Cs

All As are C

Inductive:
A1 is B
A2 is B
A3 is B

All B’s must be A

  • you are generalising from a finite number of observations
Abductor reasoning (factor analysis):
- devising a theory from observations (but not with direct testing)

The surprising fact C is observed,

If A were true C would be a matter of course, hence there is reason to suspect A is true

Not conclusively true however

80
Q

What is a factor? What is it’s loading?

A

Hypothetical variable assumed to underlie a group of highly correlated items

The greater the loading the more that factor explains the variance of those items

An items ‘loading’ is a correlation coefficient, ranges from -1 to +1

81
Q

Goal of factor analysis?

A

Trying to understand the underlying dimensions and psychological processes behind the responses

Have as clear a solution as possible

82
Q

Types of factor analysis?

A

Exploratory:
- Highlight factors within a set of responses

Confirmatory:
- Used to test whether a set of data fits a pre-existing pattern of factors

83
Q

Stages of explorative factor analysis?

A

Extraction:

  • Determines how many factors underlie the data
  • Normally principle components analysis

Rotation:

  • determines the loading of each item
  • Either Orthogonal or Oblique

Orthogonal: assumes each factor is unique, theoretical model may suggest independent variables (e.g. varimax)

Oblique: More often used, determines the relationship of factors to one another

84
Q

Tests to determine the suitability of data for factor analysis?

A

Look at correlation matrix, want a few correlations above 0.3.

Barlett’s test of sphericity: should be above 0.6

KMO - Kaiser-Meyer-Olkin: Should be above 0.6

85
Q

Two techniques to determine number of factors to retain?

A

Look at the eigenvalue: Factors with a value above 1 will be retained

Scree test: at which point does it become horizontal

86
Q

What is rotation in factor analysis?

A

tries to present the factors so that factors are easiest to interpret, so that they load on the factors that they are most correlated with.

87
Q

What is test-retest reliability?

A

Correlation of between two scores at two different times of testing, this can be affected by:

  • Internal reliability
  • External factors, such as mood or fatigue
88
Q

Test-retest reliability is useful when measuring what sort of characteristics?

A

Characteristics that are stable and do not vary much in the short term

Others do vary e.g. mood

89
Q

What is alternate forms reliability?

A

Attempt to reduce any carry-over effect from doing the test before

Can give another version of the test

Limited by internal reliability of the two forms of the test

90
Q

What is validity generally?

A

The extent to which the test measures what it claims to measure.

91
Q

What is face validity?

A

Subjective test of whether the items appear to be measuring what you think they are

92
Q

What is content validity?

A

Trying to get a full range of the content the items should be measuring

Subjective

Seek out experts or people who are similar to those you are trying to measure

93
Q

What is concurrent validity?

A

Comparing your test to a previously established test.

May be a more established and accepted test of the same variable, i.e. physiological measures.

94
Q

What is predictive validity?

A

The ability of the test to predict future events e.g, if intelligence predicts employment

No necessary for a test - but may add to validity

95
Q

What is convergent and discriminant validity?

A

Convergent: measures of the same variable must all correlate with each other, regardless of the type of measure e.g. interview should correlate with questionnaire and physiological measures.

Discriminant validity is the opposite, there should be little correlation between a test and measures of DIFFERENT variables

96
Q

What is construct validity?

A

A form of validity when the construct is not assessed with respect to external criteria. A test that you believe is the same thing you believe in.

97
Q

What is the nomological net?

A

The idea that a new construct has few specifiable associations in which to pin down the construct. The construct will send out roots in many directions attaching it to associations as research proceeds.

The construct will change and become more defined and rigid as research goes on.

98
Q

How do you test construct validity?

A

TRICK QUESTION. HA. You Can’t. The whole investigation is a test in a way or some other suitably fluffy bollocks explanation. Martin goes on about this for about 15mins.

99
Q

What is effect size?

A

The size of the relationship between two variables.

100
Q

How can you define effect size?

What is a small, medium or large effect size?

A

R squared or eta squared (correlation) is an effect size

Cohens d, look at the difference between two means, and standardise it (SD), so you can compare across studies:

SD

D=0.2 is small

  1. 5 is medium
  2. 8 is large

Can report as SD’s or the original units

101
Q

What is type 1 and 2 error?

A

Type 1 error:
- Finding a significantly result when it is not really there.

Type 2 error:
- Failure to find a significant result when there is one.

102
Q

What is statistical power?

A

Likelihood of finding an effect when one does really exist:

A study with a power of 0.8 has an 80% chance of finding a significant effect when it does actually exist.

103
Q

What affects power?

A

Effect size - the larger the better

Sample size - the larger the better

Alpha level - the less stringent the more power, however increases chance of type 1 error. So can’t change.

104
Q

How can you do effect size before research has started?

A

Could do pilot.

Getting the average effect size from other research (meta-analysis). If research is new then the effect size might be new (you don’t know)

On the side can do cost-benefit analysis. Minimum effect size that you need to see for the study to be worthwhile.

105
Q

How do you do a meta-analysis?

A

Define variables of interest.

Plan database search - inclusion and exclusion criteria

Calculate effect sized from other research

Combine them: Convert to z score, calculate average z-score then convert back.

106
Q

How can you increase power?

A

Less stringent alpha (not gonna happen)

Increase sample size

Reduce noise (reduce SD):

  • Standardise procedures
  • More reliable measures
  • Repeated measures design

Focused (planned) contrasts rather than omnibus test.
- Use a test that only look for a linear relationship: not ANOVA.

Combine results of individual studies

107
Q

What are attitudes?

A

A psychological tendency expressed as evaluating a particular entity with some degree of favour or disfavour

Latent

Complex

Not the responses, but the attitude underlying that response

Psychology Leeds, sem 2 (6 decks)

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