Hello there.

I have 5 Likert scale questionnaires that have been answered by the same people who have been randomly selected (N=24) and i have the following problem:

I first test the data distribution of each scale in order to determine what statistical analysis tests should i employ (parametric or non parametric).

The first two scales (let call them A and B) have approximately normally distributed data based on visual, numerical and normality tests.

The next three scales (C,D and E) however have some anomalies and normality cannot be assumed.

These anomalies rely on 1 outlier in scale C and D and E is just weirdly distributed.

Trying to transform data using LG10 or SQRT functions doesnt really work. Therefore, everyone seem to suggest that i should use non-parametric test on these scales.

My question is:

Can i proceed with the use of Parametric tests (means and sd's and correlations) on these scales and justify it by assuming normality based on the fact that the sample has been randomly selected and on the normality tests of the previews scales (A and B)? If yes, is there any reference to support this?

For example:

From: http://www.psychwiki.com/wiki/How_do...n-normality?

OR

From:

http://ceaccp.oxfordjournals.org/content/7/4/127.full

Thanks.

I have 5 Likert scale questionnaires that have been answered by the same people who have been randomly selected (N=24) and i have the following problem:

I first test the data distribution of each scale in order to determine what statistical analysis tests should i employ (parametric or non parametric).

The first two scales (let call them A and B) have approximately normally distributed data based on visual, numerical and normality tests.

The next three scales (C,D and E) however have some anomalies and normality cannot be assumed.

These anomalies rely on 1 outlier in scale C and D and E is just weirdly distributed.

Trying to transform data using LG10 or SQRT functions doesnt really work. Therefore, everyone seem to suggest that i should use non-parametric test on these scales.

My question is:

Can i proceed with the use of Parametric tests (means and sd's and correlations) on these scales and justify it by assuming normality based on the fact that the sample has been randomly selected and on the normality tests of the previews scales (A and B)? If yes, is there any reference to support this?

For example:

*"Leave your data non-normal, and conduct the parametric tests that rely upon the assumptions of normality. Just because your data are non-normal, does not instantly invalidate the parametric tests. Normality (versus non-normality) is a matter of degrees, not a strict cut-off point. Slight deviations from normality may render the parametric tests only slightly inaccurate. The issue is the degree to which the data are non-normal."*From: http://www.psychwiki.com/wiki/How_do...n-normality?

OR

*"However, it is not actually necessary for sample data to follow a normal distribution in order to subject the data to parametric statistical analysis (which is often the case with the smaller sample sizes described in clinical studies). Rather, it is necessary for the sample data to be compatible with having been drawn from a population, which is normally distributed."*From:

http://ceaccp.oxfordjournals.org/content/7/4/127.full

Thanks.

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