The alpha value below 2.0 suggests that the reliability of your scale may not be adequate. However, it is important to consider other factors such as the content and construct validity of the items and the research question you are trying to answer before making a decision on whether to use the scale or not.

If the items are conceptually and theoretically related and measure a specific construct of religious tolerance, then the low alpha value may not necessarily indicate a problem with the scale and you may choose to proceed with the analysis.

However, if the items are not conceptually or theoretically related, or if there is some ambiguity in the meaning of the items, it may be appropriate to revise the scale, drop items or create a new scale altogether.

Additionally, it is also important to consider the size of the sample and the ratio of cases to variables in your data. If the sample size is small and the ratio of cases to variables is low, the alpha value may be artificially low, even if the items are theoretically and conceptually sound.

In summary, a low alpha value may not necessarily indicate a problem with the scale, but it is important to consider other factors and carefully evaluate the content and construct validity of the items before making a decision on whether to proceed with the analysis or not.

A Cronbach's alpha value of 2 does not make sense (do you mean 0.2?). As a composite reliability coefficient, acceptable/meaningful values for alpha fall between 0 (completely unreliable) to 1 (perfectly reliable).

Cronbach's alpha is an appropriate index for computing composite reliability when the test score variables (items, components, or subscales of a test) are in line with the classical test theory (CTT) measurement models of essential tau equivalence or strict tau equivalence. These measurement models are fairly restrictive versions of confirmatory factor analysis (CFA) models. They imply CFA models with a single latent factor, equal factor loadings, and uncorrelated error variables. (In the strict tau-equivalence model, also the means of the test score variables (items/components) are restricted to be equal. This constraint is relaxed with essential tau equivalence.)

[As an aside, for measures that are unidimensional but have different factor loadings (so-called congeneric measures), Cronbach’s alpha represents a lower bound for composite reliability (i.e., it tends to underestimate the composite reliability of congeneric measures). Other measures of composite reliability such as McDonald's omega are more appropriate for unidimensional measures that have different loadings.]

The first thing that I would do in your case is fit a single-factor CFA model to your four items and see if the model fits well and if it does, whether the items have the same (unstandardized) loadings. If the model fits well and if the loadings are the same (or at least similar), Cronbach's alpha would be appropriate. Using CFA requires a large enough sample, for four items probably at least 100 cases.

From the item wording, it appears that at least some of the items may be multidimensional (measure more than one attribute/factor). If this is the case, then your 1-factor CFA model would likely be rejected in a chi-square test of model fit and/or you would obtain a solution with small standardized loadings. In case the items are multidimensional, Cronbach's alpha would not be meaningful as a measure of composite reliability. You would also see this from the fact that the inter-item correlations would likely be rather small in that case.

It is not clear to me that all your items are in the same direction. To be certain, you should examine a correlation matrix to check whether you have only positive correlations.

David L Morgan It seems that atleast 2 item has a negative correlation in a group of any 3. The items are carefully coded to mean the same thing (same direction). What could be done in this case?

The only way to raise your alpha is to remove some of the items. Does your software's version of alpa include a value for "alpha if item deleted"?

[David L Morgan] Removed 2 variables based on factor analysis. Scale with the rest of the 2 variables yields an alpha of 0.341

All of your correlations are low, so that is not surprising. Have you checked for problems with missing data or other issues in the scoring of your variables? If not, take a look at the distribution for each variable.

You don't state what your response scale is. Is it degree of agreement? If so, Item 1 is pro-tolerant and Items 2-4 are anti-tolerant? Did you recode either item 1 or Items 2-4 to compensate for the difference in direction of wording? If not, that would certainly screw up estimated reliability, notably in such a short scale. (By the way, reliability generally increases with the number of items-- more are better.)

I do find your item-wording awkward. That wouldn't help reliability.