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Low Cronbach's alpha: what to do

The common reasons for a low alpha, how to find the problem in SPSS, and what you may and may not do to fix it.

Part of the series: Statistical Analysis for a Seminar Paper or Thesis: The Complete Guide

Cronbach's alpha measures internal consistency: how well the items in a questionnaire "go together". A value of .70 or higher is usually considered adequate, but that's a rule of thumb, not a law. With short scales or in exploratory research, slightly lower values are sometimes accepted, as long as you explain.

The most common cause: reverse-worded items that weren't recoded

Many questionnaires include items worded in the opposite direction ("I feel calm" in an anxiety scale). If you don't reverse their scoring before the calculation, alpha drops sharply.

How to spot it: in the Item-Total Statistics table, an item with a negative correlation to the total score (negative Corrected Item-Total Correlation) is almost always a reversed item that wasn't recoded.

How to fix it: Transform → Recode into Different Variables, and on a 1–5 scale, reverse 1→5, 2→4 and so on.

Other causes

  • Few items. Alpha depends on the number of items. On a 3–4 item scale, a relatively low value is expected.
  • The scale measures more than one construct. If the questionnaire has several subscales, compute alpha for each subscale separately, not for all of them together.
  • A small or homogeneous sample. When almost everyone answers the same way, there isn't enough variance for the correlations between items to show.
  • Translation or adaptation. A questionnaire translated without a validation process may behave differently from the original.

How to check it in SPSS

Analyze → Scale → Reliability Analysis. Under Statistics, tick Scale if item deleted. In the output, look at two columns:

  • Corrected Item-Total Correlation: items below about .30 contribute little to the scale.
  • Cronbach's Alpha if Item Deleted: what alpha would be if you removed the item.

What's allowed, and what isn't a good idea

  • You may remove a problematic item, provided there's a content-based justification, not just a numerical one, and you report it in the method chapter.
  • Don't remove items one after another just to reach .70. That changes what the questionnaire measures.
  • Do check the questionnaire's structure with factor analysis if you suspect it measures more than one construct.
  • You can also report McDonald's omega (ω), which is now considered more accurate in most cases.

How to report it

The internal consistency reliability of the scale in the current study was α = .64. After removing item 7, whose correlation with the total score was low (.08), reliability rose to α = .72.

And if alpha stays low, report it honestly and discuss it as a limitation in the discussion chapter.

Next steps

Reliability is reported in the instruments section - which has a template for describing a questionnaire. If alpha stays low, it goes into the limitations in the discussion chapter. And if the problem is unanswered items - missing data.

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