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7 Validation, Constraint, and Logic Checks you must enforce in a CAPI App

In previous blogs, we discussed principles of good questionnaire design and common types of questions in quantitative research. This blog lists some of the good practices to ensure that your Computer Assisted Personal Interview (CAPI) App is smart enough to aid the enumerator to navigate properly and helpful for respondents to answer correctly and quickly.

Today, developing a CAPI App is not a complex task that only a few chosen ones can do.  Free resources such as ODK are available but with a little bit of learning curve (not a steep one, I promise). My blog compares a few commercial software-as-service platforms that are used to develop CAPI. Irrespective of the platform used, the following programming tips are possible in most and will help you get data that needs minimal cleaning and ready for analysis.

7 Validation, Constraint, and Logic Checks you must enforce in a CAPI App

1. Datatype

Select a correct data type that is most appropriate, and do not compromise thinking what if some answer comes where I have to write instead of ‘select’ a number. If it does, perhaps your questionnaire design is wrong, and most CAPI Platforms will allow you to attach a ‘comment’ to any question anyway. Common data types used are –Text, multiple-choice, Numbers (Integer, Decimal, Numeric-ID), Date, Time, GPS, Image, signature, audio, video. Most CAPI platforms will pop-up a correct keyboard as per the data type and will not allow illogical characters.

2. Range Check

This is a basic check for continuous or numeric data type where we allow an answer in a pre-specified range. You can specify the range as a minimum-maximum number, duration of audio, or length of a text string. A great value of CAPI is to make the ranges ‘dynamic’ or customized to previous answers. Think about that one in a thousand possibilities here and ensure you do not have an outlier.

3. Mandatory/Required Answers

Usually, do not allow blank answers. If a question should be skipped, then a programme should take care of it.  And if a question is not answered, you still want a special code to know the reason why it was not answered – did the respondent not know? Did she refuse? If you allow question skips without an answer, you will exactly get that… lots of blanks and no explanation about why!

4. Randomize sequence of answer or option list

Categorical or MCQ questions with many options often give us a curious case – the first 7-8 options are selected more frequently than the bottom ones. Well, we frown upon a question with more than 15 options, but if we must have a long options list, then at least, randomize the sequence in which these options appear. This is again a feature most CAPI platforms have.

5. Don’t allow an illogical set of answers to be selected in MCQ

We often find MCQs with answers such as ‘I don’t know what happened’ and ‘this is what happened’ both coded. It is illogical and CAPI should give an error message to the enumerator that the choice set selected has an illogical combination of choices.

6. Dynamically modify question content

We often end up asking respondents illogical questions or questions which are answered before! We should constantly look for ways a question can be customized to a respondent. At a basic level, it can be inserting her name in the question (if helpful). But we suggest even filtering option list and range checks according to previous answers. You can even display a previous answer in a question and customize the wording of the question to get a specific and quick answer.

7. Design ‘answer box’ such that enumerators can write correctly

This becomes important especially when a long number needs to be written (e.g., Aadhaar ID) or when the options list is too long. Most CAPI platforms allow you to present the text box, number box, or choice list in different visual patterns. For example, a long number can be split into a few small sections of 3-4 digits each, like how we are made to enter our 16-digit credit card numbers in four boxes.

Once you have the programming checks listed above in your CAPI, ensure that when an error is trapped, a ‘correct’ error message is displayed to the enumerator so she can take some action.  We know all above is easier said than done, but these 7 checks and programming will soon become second nature to you once you see how your one hour of extra efforts in CAPI programming can save at least ten hours of data cleaning and processing. You will also enjoy the high of a new skill, creativity, and innovation.


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As a Research Assistant, Alron provides support on projects, proposals, data analysis, ideation. He has over two years of experience in MIS, economic analyses, and policy research writing. He is passionate about migration-refugee crisis, decoding and mitigating disinformation mechanisms, and has written extensively on these topics, among others.

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Free or highly subsidized power for farm irrigation has been a cornerstone of India’s agricultural policy since the 1970s, but it also results in high subsidies, technical and commercial losses, and ultimately poor service to the consumers.  Government of India is investing billions to separate feeders that supply power to farmers from other consumers for better regulation, measurement and quality of supply. However, no robust evidence was available on whether and how these infrastructure improvements benefit the end consumers.  NEERMAN in partnership with Asian Development Bank and Central Discom Madhya Pradesh undertook an impact evaluation of the Feeder Separation Project in Madhya Pradesh. The study proved that feeder separation benefit farm and non-farm consumers by decreasing power theft, increasing coverage of electrical connections, reducing irrigation, and reducing the use of diesel.

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