You can look at alpha-cronbach after you have standardized the data in your hand. Your credibility has three major parameters. Alfa-cronbach coefficient method, method of correlation between question and whole (Item-total correlation) and reliability coefficient if question is deleted. You can do these via SPSS.

Thank you very much Mr. Ongel for your answer. I'll try with SPSS to see if I have the answer. What I am confused here is that I thought the Cronbach Alpha is applied for Likert questions only? What could I do if the questionnaire has both MCQs and Likert and even open-ended questions?

Please correct me if I'm wrong. 

Dear Phuong-Le,

The purpose of a reliability analysis is to reduce the dimensions of your data set. It is useful when you have a group of questions which are about the same thing. So you first need to decide whether you have a single group or multiple groups.

We recommend using a Principal Component Analysis before a Cronbach's alpha analysis. You are correct in stating that this technique is appropriate with Likert questions but not with questions yielding nominal or textual data.

You can not use this method for open-ended questions. (At least, I don't know such a method.) I suggest you to standardize your open-ended questions...

Hi Phuong-Le,

When we have multiple-choice questions, we will have binary answers (right or wrong or 0 and 1). The more appropiet test, for these cases, is the Kuder-Richardson KR-20.

Already in open-ended ones case you need create a scale by use "judges" for give hierarch values. for example. 0 = totaly wrong, 1 = have 20% correct, 2 = have 40% and following until 100%. The Judges have a important work. They need create a scale. After this, you can use the Cronbach alpha test.

Hope that help

Best Regards

Dirceu

Dear Phuong-Le,

you can test reliability of questionnaire by reliability test. For this purpose, take  a small sample and administer questionnaire among them tabulate the data . Again after some time. the same questionnaire may be administered among  the same set of respondents. Data obtained from both the count may be compared . If the response of  both sets are almost same say 95%  responses are same  then we can say reliability is 95% and accept the same.But all it depends on your acceptance level

For Reliability, different statistics are used, however, these methods are applicable or not for multiple choice questions, I am not sure. For multiple choice questions, face validity or content validity is more appropriate.

Cronbach's  (Alpha)

Average Variance Extracted

Construct Reliability

Indicator Reliability

KR20 and Coefficient alpha are the same thing, but alpha is generalized to work with polytomous item responses and not just dichotomous.  KR20 uses the term pq for binomial variance, which of course assumes binary data.

If you have a larger sample size and really want to evaluate the quality of the assessment, you should use item response theory.  The shortcomings of coefficient alpha, and classical analysis in general, are well documented.

My english is very poor. This is an automatic translation. I include the testo in Spanish in the end.

Open-ended questions should be codified (standardized) before analyzing reliability. One possibility that you can add to what you have already answered is that you do a first exploratory part with open questions with a reduced sample and then build that other closed for the definitive instrument.

I hope I have brought something useful.

Mi inglés es muy pobre. Esta es una traducción automática. Incluyo al final el testo en castellano.

Las preguntas abiertas deberían ser codificadas (standardized) antes de analizar al confiabilidad. Una posibilidad que puede agregar a lo que ya contestaron anteriormente es que Ud. haga una primera parte exploratoria con preguntas abiertas con una muestra reducidas y luego sobre esa base construya otras cerradas para el instrumento definitivo.

Espero haberle aportado algo útil.

There are many ways to measure reliability, including a very simple do we agree or not.  However, reliability is an important measure to a questionnaire's worth.  In other words, can you and a colleague administer the same (or part of) a questionnaire, or test, and get the same result.  I always have my students look at the published reliability of an assessment, for example, before administering.  What is an acceptable reliability measure is of importance, but difficult to quantify, in my opinion.

Thank you RG Friends

Thank you very much for your help!

It has been a pleasure to participate! The discussion is very interesting!

I think if u can categorize the open-ended question then it will be easy to to use cronbach's alpha to check the reliability for your questionnaire

For checking the reliability of questionnaire and response, you can do a pre-test/reliability test on a small sample say about 5-10% of the sample