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Research Methods D Flashcards

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1
Q

1) Weak, 2) moderate and 3) strong bivariate correlations

A

1) .1-.3
2) .3-.6
3) .7-.9
(sign doesn’t influence strength)

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2
Q

When to use Pearson’s r and Spearman’s rho

A

Pearson’s when parametric, Spearman’s when non-parametric

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3
Q

Complications in correlations (5)

A 
Small samples (under 10) unreliable
Non-normal distributions
Outliers (are they omitted?)
Non-linear relationships
Hetrogenous samples
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4
Q

Example of hetrogenous sample

A

If r=.5, but for men and women in sample r=.12 and r=.14 respectively…
separate correlations produce much weaker scores but together form a moderate correlation

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5
Q

How correlation explains the variance

A

Can be used to see how much the variation of scores in the data are explained by the study… showing the overlap on a ven-diagram. Use R^2 for this

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6
Q

If r=.7, how much of the variance is explained?

A

.7 x .7 = .47, so R^2 = .49, so 49% of variance is explained

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7
Q

How to make simple correlations more inline with reality’s complexity

A

Partial out (control for) other theoretically driven causal or confounding variables when analysing

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8
Q

How to decide when to use 1) more or 2) fewer questions in a questionnaire

A

Use more when dealing with: complex concepts, attitudes/beliefs or psychometric factors
Use less when: only a few dimensions, concepts are well defined and for attributes / behaviours

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9
Q

key principles of questionnaires (3)

A

test-retest reliability
Addresses intended concept (validity)
Can be meaningfully, quantitatively, analysed

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10
Q

How to solve problem of acquiescent or socially desirable respondents of questionnaires (3)

A

Invert some questions (back-to-front coding)
Include contradictory statements to see if they answer the same
Include dummy / masker questions to make the topic of questionnaire subtler (social desirability reduced)

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11
Q

How to do questionnaire data entry (2)

A

Each participant gets a row, each item getting a column

Data must be typed in raw (no altercations)

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12
Q

Why negative items must be reverse coded

A

So all items point the same direction, the top score conceptually meaning the same thing

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13
Q

Reliability

A

Extent that the measure is stable / consistent, and produces similar results when administered repeatedly

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14
Q

How to test questionnaire reliability (3)

A

Test-retest
Split-half - giving half of questions to one group and half to the other
Item analysis (the best) - sorts the useful and non useful questions (tests internal consistency)

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15
Q

Describe Cronbach’s alpha in item analysis

A

If items on questionnaire fit together coherently, the Cronbach’s alpha will be closer to 1. If = 1, all items will have been answered the exact same

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16
Q

Describe correlations in item analysis

A

If an item makes a useful contribution to a questionnaire, its score will correlate with the questionnaire total.
If it does not, this reduces the alpha so may want to be removed

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17
Q

How to conduct an item analysis with the correlations and Cronbach’s alpha

A

Use the item total statistic (how much it correlates to questionnaire score) to decide which item to recode or delete, repeat an item analysis after each change until Cronbach’s alpha is: .7 < alpha < .8…. but preferably closer to .7.
Start by recoding the most negative items and then deleting the smallest correlations - also refer to ‘alpha if deleted’ column
(NEVER recode an item twice)

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18
Q

What the item total statistic can tell you in item analysis if negative (and moderate-strong)

A

It is measuring the conceptual opposite of what was intended

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19
Q

What the item total statistic can tell you in item analysis if low

A

The item does not differentiate between people, everybody giving the same answer… question could display too extreme a view, or too common a belief

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20
Q

What the item total statistic can tell you in item analysis if low and the alpha increases if deleted

A

Question does not measure the intended thing, it lacks relevance… answers look random on graph comparing to overall questionnaire score

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21
Q

Known groups validity and how to test

A

Differing scores found for groups already known to differ

Test with a t-test

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22
Q

Concurrent validity

A

New scale compares to the established ‘gold standard’ measure (already reliably tested) - about its predictive power against other questionnaire

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23
Q

Construct validity

A

Appears consistent with theories of construct the questionnaire is interested in

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24
Q

Content validity

A

If all aspects of the content appear reflected (and proportionally reflected) in the questionnaire

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25
Q

Criterion validity and e.g.

A

Results are consistent with other measures, matching theory. e.g. IQ tests designed to correlate with child’s age

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26
Q

Face vailidity

A

If experts, participants (etc) agree that the construct is being accurately measured

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27
Q

Relationship between reliability and validity

A

Without reliability, there can be no validity… if results do not show a consistent pattern then the concepts could not have been measured

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28
Q

Describe factor analysis conceptually (3)

A

Correlates all items on the questionnaire in all possible combinations
Can then see what item’s correlations cluster together, these have something in common… meaning they explain the same part of the variance
Qualitatively decide on a label to give these clusters based on features of the questions

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29
Q

Why having factors in a questionnaire is useful (2) and not useful (1)

A

Makes the overall topic more subtle
Gives a greater understanding of scores

Not useful as makes the analysis more complicated

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30
Q

Steps of factor analysis from the output

A

Use scree plot to decide how many factors are needed
Construct the basic factors
Rotate factors, so they make more ‘sense’
Label the factors (ideally unrelated to each other)

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31
Q

Factor loadings

A

How much each item relates to factor, if it correlates to it then it relates to it

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32
Q

Scree plot

A

Graphical representation of effectiveness of each factor, showing how much variance is explained. Generally, should use if greater than 1.

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33
Q

What is the factor loading threshold for an item belonging to a factor

A

Is arbitrary but around .4 - .5

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34
Q

What to do if item belongs to more than one factor

A

Make a judgement which to include it in depending on size of factor loading, and then depending on qualitative relatedness to each factor (can belong to both factors!)

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35
Q

Rotation in factor analysis

A

Graphically, turn the factors x and y axis together, keeping perpendicular… until scores are nothing for one factor and more so for the other (increasing simplicity)
If done for all factors… are proportionally the same still

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36
Q

What to do with negative factor loadings

A

They are still members of the factor as if they were positive.

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37
Q

What to do with upside-down factors

A

If majority / all of its factor loadings are negative, be aware of what this is saying when creating a label

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38
Q

How to get the best factor analysis

A

Re-do it with differing numbers of factors depending on scree plot (especially when borderline close to 1), until satisfied with the labels and loadings etc

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39
Q

How to label factors (2)

A

Use the size and direction of loadings to determine label, they show significance of each item
Look at meaning of items together, label going beyond just one item

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40
Q

Steps to creating a questionnaire (4)

A

Item pool - defining concept and creating questions
Pilot testing - sees if it asks what we intend
Reliability check - test-retest / item analysis
Validity check - e.g. discriminative

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41
Q

Purpose of factor analysis (2)

A

Informs about the underlying structure of the construct being measured
Shows how participants conceptualise items

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42
Q

Benefit of a 4-point likert scale

A

Forces choice to either agree or disagree

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43
Q

Convergent validity

A

Correlation of results with an existing questionnaire

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44
Q

Incremental validity

A

How the questionnaire is distinguished from other measures

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45
Q

Why we use correlations (4)

A

To show reliability (e.g. test-retest)
Predict the outcome of one variable on another
To show validity
Theoretical verification

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46
Q

What R^2 is diagrammatically equivalent to

A

the overlap on a ven diagram

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47
Q

What does the ‘variance explained’ tell us

A

R^2 shows how accurate the model is based on its predictive power

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48
Q

How correlation can be closer to being causal evidence

A

If supported by theoretical or observational inputs that can explain how there are no / little other variables to effect the association

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49
Q

What is the criterion

A

The dependent variable, what we predict (y-axis)

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50
Q

What is the predictor

A

The independent variable, what we are predicting from (x-axis)

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51
Q

What non-linear regression looks like

A

Curved line on the graph

52
Q

Equation of a linear regression line, and what each bit means

A

y = b0 + b1(x) … + bn(x) + error
b0 is the intercept at y axis
b1 is the slope of the line (deciding the steepness)
error is the extent of residuals from the line

53
Q

Why is the equation of a linear regression line useful

A

Can be used to predict y when x is know, or deciding what x should be… (or vise-versa, when rearranging)

54
Q

How to see extent of a residual using equation of a linear regression

A

By inputting a subjects x into equation, giving y value on the regression line… how far this is from subjects actual y value is the residual

55
Q

What does a regression line do in terms of residuals / error

A

Tries to make them as small as possible overall, being the line of best fit… making the sum of squared errors as small as possible (squared to cancel direction of error)

56
Q

On SPSS what to do before carrying out linear regression (2)

A

Check normal distribution in the explore function, then create a scatter plot on chart builder

57
Q

What does linear regression assume (4)

A

A linear relationship, other functions are for non linear
Is homoscedastic, y being normal (the same) at all values of x (CANT be hetroscedastic)
Y’s spread is the same at all values of x
Criterion is normally distributed, not needed for predictor

58
Q

Different types of outlier (2)

A

Leverage - distance from the mean of the plot

Error - distance from the regression line

59
Q

Which type of outlier effects the strength of the model more

A

The error, can still have a strong model if have a large leverage but small error… not the case if a large error and small leverage

60
Q

What the linear regression equation graphically represents if there are two predictors

A

A flat plane (surface), but a residual plot is a better visualisation

61
Q

Why can there never be more than one b0

A

It represents the intersection with the y axis, and there is only ever one y axis

62
Q

Why does the sum of each predictors R^2 (x100) almost never equate to the total variance explained of the predictors

A

Because predictors almost always overlap in the variance they explain, they correlate with one another (called co-linearity)

63
Q

What happens if co-linearity between predictors is too high

A

Some variable may be dropped from the analysis as they do not predict any unique variance… it is explained fully by the other predicting variables

64
Q

Why is a ven-diagram good for multiple linear regression

A

Can visually see how much variance each predictor uniquely explains and how much they correlate together

65
Q

Part correlation

A

Amount of unique variance explained by the predictor, as a proportion of the total variance in the criterion (whole circle of DV in ven-diagram)

66
Q

Partial correlation

A

Unique variance explained by predictor (once the other relationships have been ‘partialled out’ (controlled for)), as a proportion of the variance of the criterion that is not explained by anything (not overlapping in ven-diagram)

67
Q

When is multiple regression analysis most effective

A

When predictors are not too strongly inter-correlated… wont be as much unique variance explained

68
Q

Describe linear regression in psychological terms

A

How much extra of the differences in peoples scores can be explained by each additional relationship

69
Q

What is the best way to do linear regression equation

A

Is not neccasarily the most sensible, can have negative regression coefficient even if the correlation coefficient is possitive

70
Q

Simultaneous multiple regression

A

Take all the predictors wanted from the model and see if they make a significant contribution to the model

71
Q

Benefit of comparing models

A

Can work out the additional variance explained by making the 2nd model the same as the first other than adding a predictor (IV)… can then see what this contributes individually
(Compare model with predictor against one without it!)

72
Q

How to decide what models to choose to compare

A

Theoretically motivated from previous literature or logical thinking, e.g. which variable is likely mediating between other variable and DV

73
Q

Why the order predictors are entered into a model matters

A

Only true if the predictors correlate (overlap) at all… usually the case.
The first predictor entered takes all the variance it explains, if the second correlates to the first then the output wont include overlapping variance explained with the first for the second (will have smaller variance than if entered first)

74
Q

Reporting a regression analysis (4)

A

Descriptive statistics (can be in table)
Correlation matrix
Description of analysis carried out (e.g. order of predictors)
Table of the model along with interpretation (e.g. of Beta scales)

75
Q

Logistic regression

A

Predicting of group membership, is a regression with a categorical criterion variable (DV is in distinct categories)

76
Q

Predictors in logistic regression

A

Similar to multiple regression, can have multiple predictors of continuous or categorical nature (we are only doing continuous)

77
Q

Types of criterion variable in logistic regression (2), and methodological change depending on the type

A

Can be binomial (2 groups), or multinomial

need bigger sample for more groups

78
Q

How to categorise in logistic regression

A

Split the data into arbitrary sections depending on DV score (see example in ppt)

79
Q

Odds for an occurrence = …

A

Probability of an event occurring / probability of an event not occurring
(odds against an occurrence = vise-versa)

80
Q

Types of log used in logistic regression odds

A

Either natural log, or log-e, where e is a special number such as pi

81
Q

What values mean in log odds

A

Are from negative to positive infinity, negative odds mean it is less likely whereas positive odds mean more likely (0 = 50/50?)

82
Q

How log odds are used in logistic regression

A

Use the continuous predictors to determine the likelihood of someone belonging to a particular category / group

83
Q

Limitations of logistic regression (4)

A

Relationship between probabilities on DV is assumed sigmoidal (S-shaped)
Is very sensitive to outliers
Ratio of the sample size to variables needs to be high
SPSS assumes we are describing relationships , not making predictions… so no population info

84
Q

What is logistic regression’s equivalent to R^2

A

Cox or Smell methods make predictions of variance explained, but R^2 does not exist in logistic regression

85
Q

What is equivalent to an ANOVA, explain…

A

Regression with categorical predictors, information is just reported differently… ANOVAs just test the significance of a regression model, but regression also gives significance

86
Q

How categorical predictors work

A

Dummy variables created whereby IVs are coded 1 (yes) or 0 (no) into dummy predictors so each IV has a unique binary code

87
Q

Equation when using categorical predictors, and what this would mean if IV1 = 1 , 0

A

y = b0 + b1(T1) + b2(T2)…

For IV1: y = b0 + b1

88
Q

Where dummy variables are added to during regression analysis using categorical predictors

A

In the Fixed Factors box, where IVs go for linear regression, to code the conditions into the analysis

89
Q

How ANOVAs and regression analysis differ in information presented

A

Regression gives coefficients and a constant, ANOVA gives the same info of coefficients but with the constant added to them, displayed as the mean of each group

90
Q

ANCOVA

A

A combination of ANOVA and linear regression, it shows the effect of an IV on a DV whilst partialling out the effect of a co-variate

91
Q

Why is an ANCOVA advantageous

A

It reduces error variance by removing a confounding variable (co-variate)

92
Q

Example of a co-variate

A

If looking at if different styles of lecturing help learning, a co-variate could be the interest in learning for each group… if some groups have much higher interest (helping learning) then interest will confound results

93
Q

How ANCOVA is seen graphically

A

Shows each groups DV on the y-axis, with the x-axis showing their scores on the co-variate

94
Q

What an ANCOVA output will show

A

Means of each group on DV, like an ANOVA… also the means of each group on DV once co-variate has been partialled out (can compare to original)

95
Q

Applications of ANCOVA (3)

A

Removing the influence of confounding factors
Calculate difference between pre and post test scores, in order to remove pre-test scores
For non equivalent, intact group designs (naturally occurring)

96
Q

Problem and solution with using ANCOVA for pre and post test scores

A

Influence of pre test score is not partialled out if the difference between pre and post test scores are correlated
Solved by having the pre test score as the co-variate in the ANCOVA

97
Q

Example of covariate in intact (existing) groups

A

Comparing women testosterone between different occupation groups, a co-variate would be age. Testosterone differs with age and different occupations are likely to have differing ages (e.g. bar staff or scientist)… so would confound

98
Q

Graphically creating adjusted means for co-variates

A

On scatter plot with DV (y-axis), co-variate (x-axis) and different groups plotted… plot the mean of each group for the DV (horizontal) and its intersect with groups regression line (initial mean). Adjust it to the intersect of the co-variates grand mean (vertical) with the regression line of each group (adjusted mean).

99
Q

ANCOVA assumptions (3)

A

Normal stuff: ND, HoV and int / rat data
Linear relationship between DV and co-variate
Homogeneity of regression (each group has parallel(ish) repression lines)
Covariate is reliably measured

100
Q

In item analysis, if the next item to be deleted has the same ‘alpha if deleted’ as another, how do I decide which to delete first?

A

Delete the item with worse wording, or the one with lower item total statistic.

101
Q

Types of closed question (3)

A 
Likert scale (agree to disagree)
Self-efficacy scale (cannot do to can do)
Semantic differentials (e.g. dominant to submissive)
102
Q

Valance in item analysis

A

If the item is positively or negatively weighted towards the construct under investigation

103
Q

Getting to item analysis in SPSS

A

Analyse - Scale - Reliability analysis

104
Q

Reporting an item analysis

A

Report negative correlated items removed and how many weak correlations removed, and final alpha value

105
Q

Discriminant validity and how to test

A

If believed unrelated constructs are actually unrelated

Test with a t-test

106
Q

Eigenvalue in factor analysis

A

Is the y-axis on a scree plot, is a measure of the amount of variance explained by a factor

107
Q

Communalities in factor analysis

A

How much items variance is explained by the factor

108
Q

Factor analysis in SPSS

A

Analyse - Dimension reduction - Factor

109
Q

Subscale totals in factor analysis

A

Add together item scores (from questionnaire) for all items loading onto a factor

110
Q

Reporting factor analysis (4)

A

How many factors are extracted
That rotation was used
The factor labels with a brief definition
Amount factor accounts for overall variance

111
Q

What is R^2 adjusted

A

Predictive power of regression equation for the population, not the sample`

112
Q

Writing up linear regression (4)

A

Include R^2, relate to the hypothesis, an ANOVA report (F, df, p) and report coefficients table (beta, t and p)

113
Q

Writing up multiple linear regression (4)

A

Table of the descriptives
Table of correlations
Describe the analysis done
Show output in table form

114
Q

When to use a hierarchal regression rather than simultaneous

A

When there are theoretical considerations that can help achieve the best model

115
Q

Why use hierarchal over a stepwise regression

A

It has theoretical input, more manually search for best model so cannot be confounded by chance variations in the data

116
Q

What method does simultaneous multiple regression use

A

The Enter method

117
Q

Stepwise regression

A

Uses statistical differences to determine which predictors come up with the best model for regression

118
Q

How stepwise regression works

A

All variables initially entered into model. Then, in a series of steps, removes possible variables and adds unused ones until the optimum model found

119
Q

Types of stepwise regression (2)

A

Forward selection - variables only ever added, cannot be removed
Backward elimination - All entered at start, then some removed and cannot be re-entered

120
Q

Caution about stepwise regression

A

It is not theoretically motivated, meaning it can be influenced by chance variations in the data. We should not make strong claims about the regression equation

121
Q

Writing up a hierarchical regression (3)

A

Descriptives, correlations and hierarchical output… all in table form

122
Q

Difference between logistic regression and multiple regression

A

Logistic uses a non linear equation applied to the values predicted by the regression equation, in order to predict group membership (DV)

123
Q

Writing up logistic regression (3)

A

Chi squared reported
Estimates of variance explained (by Cox and Smell method)
Whether each predictor was significant, with values

124
Q

Why logistic regression is sensitive to outliers

A

Logistically, participants cannot be ‘a bit’ part of a group, therefore small variations have a big impact on group membership

125
Q

Writing up ANCOVA (2)

A

ANOVA report and table of means, after the covariate is partialled out

126
Q

ANCOVA equation

A

y = b0 +b1 (T1) + b2 (T2) + b3 (X)

X = covariate

127
Q

What the varimax rotation method tries to do

A

Get factors with the least amount of overlap by getting items to load only onto one factor

Psychology (9 decks)

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