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    Cronbach Alpha vs Pearson r




    Hi all,

    this might sound like a completely amateurish question but...what is the practical difference between cronbach's alpha and the Pearson product moment correlation coefficient?

    I just made up an example: let's say you want to measure the degree of literacy of a population and you use two items: the 1) # of books read per year and 2) the education level.

    You have a reasonable expectation that they measure the same underlying phenomenon (the literacy of that sample) and very likely there is a strong linear correlation between the two variables (i.e. high value of r). Would computing the cronbach alpha add any additional information in such a case?

    Thanks in advance for your answers.

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    Re: Cronbach Alpha vs Pearson r

    With two variables and in your scenario it wouldn't really add much at all, would seem redundant. You typically select the one that fits your goals and is common practice for that area.

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    Re: Cronbach Alpha vs Pearson r

    They are used for different things in different fields. Pearson's looks at the correlation between interval data, regardless of what it is. Cronbach Alpha (commonly used in psychology and education) is really geared to whether raters are consistant among themself in the way they code data although it can get at reliability more generally.
    Hail A.I.M!

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    Re: Cronbach Alpha vs Pearson r

    Quote Originally Posted by noetsi View Post
    They are used for different things in different fields. Pearson's looks at the correlation between interval data, regardless of what it is. Cronbach Alpha (commonly used in psychology and education) is really geared to whether raters are consistant among themself in the way they code data although it can get at reliability more generally.
    Thanks a lot for your answer! I have myself the feeling that it is field-dependant.

    I'd very much like if you could extend the part of your answer in bold. Couldn't you get a measure of raters' consistency simply by looking at Pearsons'r between test items?

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    Re: Cronbach Alpha vs Pearson r

    Quote Originally Posted by hlsmith View Post
    With two variables and in your scenario it wouldn't really add much at all, would seem redundant. You typically select the one that fits your goals and is common practice for that area.
    Thanks a lot for your answer.

    What would be a scenario in which they would not be redundant? :-)

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    Re: Cronbach Alpha vs Pearson r

    You probably wouldn't ever report them together to examine the same thing, as it has been pointed out they are context specific. You should just look at when certain fields use each one. An example on how both could be used during a project, but to examine DIFFERENT things would be:

    Cronbach alpha to examine how well a set of survey items measures a single characteristic
    Pearson correlation (or Spearman) to interpret various survey item responses possibly gouped in the cronbach alpha or not.

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    Re: Cronbach Alpha vs Pearson r

    I think it is easier to consider the relationship between Cronbach's alpha and the the Pearson correlation in Standardized form as:

    \alpha =\frac{k\bar{\rho }}{1+\left ( k-1 \right )\bar{\rho }}

    where k is the number of classes (e.g. # of test items, # of raters). Thus, coefficient alpha can be considered as a function of the average of the pairwise Pearson correlations across the k classes.

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    Re: Cronbach Alpha vs Pearson r

    As Dragan shows the two are closely related. If we know the correlation between two items then this is enough information to calculate the Cronbach's alpha, and vice versa. (The alpha value will be higher).

    Quote Originally Posted by hlsmith View Post
    Cronbach alpha to examine how well a set of survey items measures a single characteristic
    Cronbach's alpha is often referred to as a measure of unidimensionality, but in reality it gives you very little information about unidimensionality. Useful paper: On the use, the misuse, and the very limited usefulness of Cronbach's alpha

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    Re: Cronbach Alpha vs Pearson r

    Thanks a lot for all your excellent replies!

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    Re: Cronbach Alpha vs Pearson r

    Hello everyone,
    I have a question regarding Cronbach's Alpha which might seem daft BUT when do you actually conduct a cronbach's alpha test? is it during piloting your questionnaire or after you collect the data from your sample?

    Help a confused postgrad student make her dissertation happen!

    Thanx

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    Re: Cronbach Alpha vs Pearson r

    You can do both, but what's reported in journal articles etc is usually the Cronbach's alpha from the "real" sample - often the questionnaire will be slightly altered after piloting, so what's of most interest to readers is the reliability of the final version in the fuller-sized sample.

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    Re: Cronbach Alpha vs Pearson r

    Thanx for the great answers everyone.

    But I am still confused, as I did my pilot testing without the respondents having to take the questionnaire, just look at it and tell me if it makes sense etc.
    Also, all my items are taken from papers which have a cronbach alpha of at least 0.65 or more. Does this mean I still better do the Cronbach Alpha? because I really feel I want to start collecting data and not sure how to do this test from qualtrics to SPSS.

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    Re: Cronbach Alpha vs Pearson r

    Quote Originally Posted by victorxstc View Post
    Oh that alpha thing sucks IMHO, and as stated in this thread by the experts. Becuase I have had studies in which alpha was not checked and after collecting the results, we saw our alpha was less than 0.5, but still the results taken from our questionnaires quite met our expectations. But unfortunately many reviewers ask for it. Although if the other aspects of your research is good, they might ignore it. It depends then.
    Thanx for your answer victorxstc,

    I think I might as well try to do it so not to be bumped by my results later and get my supervisor dissatisfied!

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    Re: Cronbach Alpha vs Pearson r


    Hi Basma

    Cronbach's alpha is pretty much standard to all pyschometric research, and it can actually be useful both for developing survey instruments and post-hoc validation of the instrument. If you have a mesurement model where you have a few sub scales or "dimensions," which are each manifest by say 5 or 6 questions on the pilot survey, the alpha can help you determine if those questions are truly reflective of their respective dimensions. After a pilot test, where you have actual test subjects take the survey, the alpha can help you decide which questions are important and which can be dropped to streamline the final survey. Some statistical programs (e.g., SAS) will calculate an alpha after dropping each successive question, to show you how much higher alpha would be without that question. An ideal alpha is .7 to .9, but .6 is acceptable. If it's above .9, then your questions are probably redundant and not varied enough in scope for that dimension (they're asking nearly the same thing).

    It sounds like you did not actually "pilot test" your survey, but that you did qualitative testing to confirm face validity (the survey looked right and seemed relevant to potential subjects) and perhaps content validity (the questions were correct in scope and depth, at least according to the subjects). A pilot test is usually accomplished by administering the survey to test subjects (n>30) and assessing the results. Sometimes this is out of your scope or resource budget, but it's the best way to catch problems if you're serious about your survey's reliability and validity and you can't afford to make a big mistake with the "live" survey.

    HTH !

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