Measuring reliability of questionnaires

Reliability of questionnaire is a way of assessing the quality of the measurement procedure used to collect data. In order to consider a result valid, the measurement procedure must first be reliable. Choose a measure while examining the construct of a study. Construct is the hypothetical variable that is being measured and questionnaires are one of the mediums.

These questionnaires are part of the measurement procedure. As a result, this measurement procedure should provide an accurate representation of the construct, to be considered stable or constant.

For example, if we want to measure the intelligence, we need to have a measurement procedure that accurately measures a person’s intelligence.

Concept of reliability

Since there are many ways of thinking about intelligence (e.g., IQ, emotional intelligence, etc.). This can make it difficult to come up with a measurement procedure if we are not sure if the construct is stable or constant (Isaac & Michael 1970).

Reliability of a construct or variable refers to its constancy or stability. The assumption, that the variable that is to be measured is stable or constant, is central to the concept behind the reliability of questionnaire. A measurement procedure that is stable or constant should produce the same (or nearly the same) results when same individuals and conditions are used. There are threats to reliability of a measurement or construct. They fall under systematic or unsystematic categories as shown below.

Testing the reliability of a questionnaire

Testing the reliability of a questionnaire

Calculating reliability of questionnaire using Cronbach Alpha

Cronbach’s alpha determines the internal consistency or average correlation of items in a survey instrument to gauge reliability of the questionnaire. Thus, Cronbach’s alpha is an index of reliability associated with the variation accounted for by the true score of the “underlying construct” (Santos 1999). Alpha coefficient ranges in value from 0 to 1. It can be used to describe the reliability of factors extracted from dichotomous. Questions with two possible answers and/or multi-point formatted questionnaires or scales i.e. rating scale: 1 = poor, 5 = excellent; is called dichotomous. Therefore, the higher the score, the more reliable the generated scale is (Tavakol & Dennick 2011). Statistical formula to calculate reliability is:

Total scale variance = sum of item variances and all item co-variances

[k/(k-1)] * [1- (sum of item variances/total scale variance)  

Where k = number of items and ranges between 0 and 1
Criteria for assessment is:

≥ 0.70 = adequate reliability for group comparisons

≥ 0.90 = adequate reliability for individual monitoring

Alpha is an important concept in the evaluation of assessments and questionnaires. Hence, it is important that assessors and researchers estimate the quantity to add validity and accuracy to the interpretation of their data. Nevertheless, alpha is frequently reported in an uncritical way and without adequate understanding and interpretation.

Further reading

This article provided a basic idea about the usage of Cronbach’s alpha to test statistically reliability of quantitative data. Furthermore, to understand the procedure of calculating Alpha using SPSS refer to  Performing tests using Cronbach Alpha.

References

Questionnaire administration
Validity in qualitative research
Shruti Datt

Shruti Datt

Project Handler at Project Guru
Shruti is B-Tech & M-Tech in Biotechnology. Some of her strengths include, Good interpersonal skills, eye for detail, well devised analytical and decision making skills and a positive attitude towards life. Her aim in life is to obtain a responsible and challenging position where her education and work experience will have valuable application.
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2 Comments.

  1. Eduardo Charpia

    wohh just what I was looking for, thankyou for posting.

  2. ADERONKE AKEREDOLU

    I need more information on samples of In-depth Interview Questions for women entrepreneurs in rural area.

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