Posted by
Kornbrot, Diana on
Jan 01, 2015; 4:32pm
URL: http://spssx-discussion.165.s1.nabble.com/Question-About-Tests-of-Normality-and-Choice-of-Statistical-Analysis-tp5728302p5728318.html
Contemplating an inferential t-test for a Likert ITEM suggests that you are using MEAN of an ordinal variable as a descriptive measure.
This is the statistical equivalent in believing in a flat earth. Move on
Likert items are ORDINAL. Consequently the only appropriate descriptive measure are raw probabilities of items 1 -n or cumulative probabilities of [<=1, <=2,…<= n-1]
If you are using raw probabilities then chi=square contingency test [pearson or log likelihood] is good for single predictor variable, and multinomial for > 1 predictor.
If you are using cumulative probabilities then ordinal regression [either logit or probit] is good
SPSS REGRESSION [ordinal or binary logit] is good fro between grip designs
SPSS MIXED is good for designs that include repeated measure, or GENERALIZED linear models with generalised estimating equation
Its pointless to ask whether ordinal measure are normally distributed . The answer is ALWAYS NO, higher mean implies negative skew, lower mean positive skew
Both normal based [t-tests] or rank based [mann-whitney] inferential tests on means or rank are meaningless, as they assume metric data, i.e. difference between agree and strongly agree is same as between neutral and agree. This is a nonsensically improbable hypothesis.
There is no excuse for any SPSS user, or anyone else as there always R, doing inappropriate t-tests on Likert items. The right tests are easily available.
A user who is unfamiliar with these kinds of tests should consult someone with statistical expertise and ask the RIRHT question, which is:
‘how do I analyse this Likert item data’
not the wrong question,
‘how do i test if this is data is normally distributed’
when consulting it is always a good idea to give expert FULL picture of problem to be solved and data collected [or better to be collected]. assuming you know the right test and skiing how to do that test is a recipe for disaster. if you are unlucky expert will tell you how to do the test without probing whether it is the right test
End of RANT, Happy New Year
NB if you have Likert SCALE rather than a Likert item, then assumption of metric properties may be appropriate.
best
Diana
Bruce,
That is very nice.
But you never even mentioned the assumptions of the relevant non-parametric
tests that are based on the rank-transformation: continuous data of similar-
shape distributions in both samples, and few ties. Some of your examples
("Normal versus skewed") would not be appropriate for testing by ranks.
Likert-type items deserve normal testing for various reasons, including the
occasional weird scoring that you can observe as resulting from rank-transforms.
Continuous items with similar skew, etc., usually should be transformed by taking
logs or reciprocal (whatever is appropriate) to "normalize"
- That improves both the metric and the test. I can regard rank-testing as a sloppy,
time-saving expedient, compared to doing a transformation that is apparent.
- If there is not a transformation available, then there is big doubt about whether
these data fit the non-par assumption.
--
Rich Ulrich
===================== To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
__________________________
Diana Kornbrot
19 Elmhurst Avenue
London N2 0LT, UK
+44 [0] 208 444 2081 home
+44 [0] 7403 18 16 12 mobile
skype: kornbrotme
=====================
To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the
command. To leave the list, send the command
SIGNOFF SPSSX-L
For a list of commands to manage subscriptions, send the command
INFO REFCARD