REAL CASE: WHEN A MEASUREMENT SCALE FAILS RELIABILITY—DO YOU NEED TO COLLECT DATA AGAIN?

REAL CASE: WHEN A MEASUREMENT SCALE FAILS RELIABILITY—DO YOU NEED TO COLLECT DATA AGAIN?

“I already have 350 valid observations, but Cronbach’s Alpha is only 0.58. Do I have to distribute the questionnaire all over again?”

This is one of the questions Dr. Mỹ Academy frequently receives from Master’s, DBA, and PhD researchers.

In reality, a low Cronbach’s Alpha does not necessarily mean that your data are problematic. In many cases, the underlying issue lies in the way the measurement scale was developed.

One doctoral researcher once approached Dr. Mỹ Academy after spending weeks struggling with their measurement results. After reviewing the entire research process, we found that the problem was not with SPSS or AMOS, but with several steps that came before data analysis:

• The measurement scale was adapted from multiple previous studies without sufficiently evaluating its suitability for the research context.
• Some observed variables did not accurately reflect the constructs they were intended to measure.
• Certain survey questions could be interpreted differently by respondents.
• Reverse-coded items had not been properly recoded before analysis.

After refining the measurement scale, reviewing the questionnaire, and reassessing the measurement model, the results improved—not only in terms of reliability, but also in the consistency and logic of subsequent EFA, CFA, and SEManalyses.

The key point is:

When a measurement scale fails to meet reliability requirements, do not immediately assume that you need to collect data again. First, investigate why the scale failed before deciding what action to take.

In academic research, software only gives you the results. The researcher must understand the reasons behind the numbers.

🎓 Dr. Mỹ Academy supports Master’s, DBA, and PhD researchers in developing measurement scales, validating measurement models, and conducting data analysis according to international research standards.

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