Research Methods D Flashcards
1) Weak, 2) moderate and 3) strong bivariate correlations
1) .1-.3
2) .3-.6
3) .7-.9
(sign doesn’t influence strength)
When to use Pearson’s r and Spearman’s rho
Pearson’s when parametric, Spearman’s when non-parametric
Complications in correlations (5)
Small samples (under 10) unreliable Non-normal distributions Outliers (are they omitted?) Non-linear relationships Hetrogenous samples
Example of hetrogenous sample
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
How correlation explains the variance
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
If r=.7, how much of the variance is explained?
.7 x .7 = .47, so R^2 = .49, so 49% of variance is explained
How to make simple correlations more inline with reality’s complexity
Partial out (control for) other theoretically driven causal or confounding variables when analysing
How to decide when to use 1) more or 2) fewer questions in a questionnaire
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
key principles of questionnaires (3)
test-retest reliability
Addresses intended concept (validity)
Can be meaningfully, quantitatively, analysed
How to solve problem of acquiescent or socially desirable respondents of questionnaires (3)
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)
How to do questionnaire data entry (2)
Each participant gets a row, each item getting a column
Data must be typed in raw (no altercations)
Why negative items must be reverse coded
So all items point the same direction, the top score conceptually meaning the same thing
Reliability
Extent that the measure is stable / consistent, and produces similar results when administered repeatedly
How to test questionnaire reliability (3)
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)
Describe Cronbach’s alpha in item analysis
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
Describe correlations in item analysis
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
How to conduct an item analysis with the correlations and Cronbach’s alpha
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)
What the item total statistic can tell you in item analysis if negative (and moderate-strong)
It is measuring the conceptual opposite of what was intended
What the item total statistic can tell you in item analysis if low
The item does not differentiate between people, everybody giving the same answer… question could display too extreme a view, or too common a belief
What the item total statistic can tell you in item analysis if low and the alpha increases if deleted
Question does not measure the intended thing, it lacks relevance… answers look random on graph comparing to overall questionnaire score
Known groups validity and how to test
Differing scores found for groups already known to differ
Test with a t-test
Concurrent validity
New scale compares to the established ‘gold standard’ measure (already reliably tested) - about its predictive power against other questionnaire
Construct validity
Appears consistent with theories of construct the questionnaire is interested in
Content validity
If all aspects of the content appear reflected (and proportionally reflected) in the questionnaire