Does using a Likert Scale require me to always measure the internal consistency? I am doing a Masters project (in environmental sociology) which includes a series of 15 questions that use a 6-point Likert scale to respond to various statements. In my pilot survey, my supervisor calculated Cronbach's alpha and said the internal consistency is too low (alpha = 0.63) and suggested I redo the questions.
Personally, I am confused about the need to assess the internal consistency in my research. The Likert Scales in my research assess a range of statements across different themes so the responses are not measuring/validating the same construct. If anything, the statements are designed to assess the potential variation in perceptions across those different themes.
My question is simple. If I use a Likert Scale, does that also require a high internal consistency even if the statements involve different themes?

Not necessarily, no.  @Student100 gives good answer but misses one important point, measures of internal consistence assume that the measurements are theeffect of the construct, that is, are they caused by the construct. For example:

*

*Whether you get a math question correct is caused by your math ability.

*How you answer a question about having high self esteem is caused by your self esteem.

These are called effect indicators, because the indicator is the effect of the construct.
But your construct might have causal indicators. A causal indicator is the cause of the construct.  And it's often not clear:

*

*I don't have many friends.

*Sometimes I feel alone.

Are these the effect of loneliness (in which case alpha would be appropriate) or the cause of loneliness, in which case it is not appropriate.
A more extreme example is a measure of stress, which might look at events that happened to you recently, and how much they affected you:

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*Lost job

*Death in family

*Moved house

*Been ill

There is not a construct of stress that is the cause of each of these things. Rather each of these things adds stress, and the more there are, the greater the impact.
The classic reference on this is  Bollen and Lennox (1991): https://www.researchgate.net/publication/232516427_Conventional_Wisdom_on_Measurement_A_Structural_Equation_Perspective - but there has been lots of work since.
