It is not clear if you create all this subsets of variables or if they were measures already develop by someone else. By what your are saying it may look that it did not follow the proper procedure, but again it could be that we dont have enough information. It is not clear for me how you create or develop your subset of variables each with 3 to 6 items. So, for example, standard errors will be larger, so if there were a t-test the ratio would be lower than it should be and your p-value would be too large and therefore you would be less likely to find a 'statistically significant' result. Honestly, the only problem with having a lower reliability is that you statistics will be biased in a particular dirrection because errors will be larger. So if the trait is restricted, the reliability will decrease because the proportion that is true score compared to error will deecrease.

Could that sample have been more diverse with regard to what the subscales measure that the larger sample? That matters because under classical measurment theory the error part is random while the true score part varies with the level of the trait in the individual. What's puzzling is that your first exercise had a smaller N. Reliability (here measured as internal consistency) is largely a characteristic of a measure, although arguably in a population, but not a sample or subsample. Alpha in a subsample is probably irrelevant. I agree that dropping to two items probably lowers the content validy of the measure.

Reliability meaning in psychology