Reliability of research tool with Yes or No answer? I am doing research on school security. Questionnaire pertains to questions regarding availability or non availability of certain items so as to reach on conclusion that what is available and what is to be provided to complete the security package. 
There are 57 questions and almost all are "yes" or "no" with few exceptions of answers as "not applicable" or some "range of amount in currency" like "less than $5000" and "Less than $ 10000". Now, how do I check the reliability. Someone said that since it is quantitative so "Cronbach's Alpha". But once I apply that it discard many questions and index is never more than 0.5. Please help and guide.
In addition, I have consulted few statisticians and they said that the questionnaire I have is just like a checklist which is conformation of the instructions given to schools by the department. So the answer is just yes and no. In this case reliability is not required to be checked. 
 A: I would encourage you to maintain your focus on your scientific questions, and select your statistical techniques appropriately to those questions. Issues like the 'reliability' of questionnaire items should be explored with particular reference to your realistic concerns about the information they yield.
It sounds to me as if you believe your questionnaire items ask for straightforward factual information from competent people who are in a position to know the correct answers. But maybe your supervisor has thought of something you've missed; it would pay to ask. Unless you (or your supervisor) can tell a reasonable story about how one or more of the items might give unreliable information, then you have no basis for choosing any statistical technique for assessing 'reliability'.
The higher your ratio of thinking to statistics, the more useful your work is likely to be. There are few things that more clearly flag poor social science research than the desultory, mechanical application of 'obligatory' statistical tests (or other procedures) in the absence of genuine scientific motivation. As one example of where a low $thinking:statistics$ ratio can lead, consider this cautionary tale.
If your site visits showed that there were any items that were not answered as reliably as you hoped, and if you have questions about how to present or analyze your data about those specific reliability problems, then you may wish to ask a new CV question.
A: There is no need to check the reliability of demographic type data like (range of amount etc..).
While You can check the reliability of rest of items. It looks like you have a dichotomous (Yes/No, True False) data. In this case, You can either use Cronbach Alpha or KR-20 test both will produce identical results. 
And If Index is not more than .50, it does not mean that there is issue in process. There may be issues in items.
Firstly, check the demographics of all items (minimum and maximum values), check weather the range of each item's values are correct. 
Secondly, check weather there is any item with negative wordings.
Thirdly, generate the correlation matrix of all items, You must have some very low or negative correlated items. 
Lastly, If I were you I would closely observe each items and group them into categories (factor) then check the reliability of factor (subscale). 
A: You might want to think about the possibility that your questionnaire is assessing different latent constructs. I suggest you factor analyze your data to determine what those constructs are. This would fall more in the realm of validity but you may want to consider this to see how well your items work together (broadly speaking, of course). 
With regard to reliability- have you considered all the factors that influence a measure's reliability? For example... 1) test length, 2) time given to participants to complete the measure, 3) item difficulty or clarity, 4) intervals between tests (if you are looking at test-retest reliability, and 5) the environment in which your participants are answering your questionnaire. Something else to consider would be to examine how your participants' characteristics are influencing their responses. Participant homogeneity is another major factor that influences a measure's reliability. 
