Dealing with missing sections

This article shows a possible workaround to a common problem encountered when dealing with a standard Munich ChronoType Questionnaire (MCTQ) and \(\mu\)MCTQ data.

It’s helpful to have the standard MCTQ questionnaire and the guidelines for standard MCTQ variable computation open while reading this article. This will enhance your understanding of the data objects discussed. You can download the MCTQ full standard version here and the guidelines for standard MCTQ variables here.

Working around missing sections

Although the standard and micro versions of the MCTQ asks for respondents to complete the workdays and work-free days sections, even when they do not have a regular work schedule (wd = 0) or have a 7 day/week work schedule (wd = 7), some of them may still end skipping one of this parts of the questionnaire. In those cases, sd_week(), sloss_week(), le_week(), msf_sc(), sjl_rel(), sjl(), sjl_sc_rel(), and sjl_sc() will produce NA (Not Available) as output. That’s because those computations combine workdays and work-free days variables.

For those special standard and micro MCTQ cases, where one section is missing, a NA value is the correct output for the functions mentioned above when wd (number of workdays per week) are wd > 0 & wd < 7, but it may not be when wd == 0 or wd == 7. While some researchers may just invalidate these latter cases, we propose a different approach.

To illustrate this approach, consider the following.

If a respondent does not have a regular work schedule (wd == 0), only answered the work-free days section, and does not use an alarm clock on their free days (i.e., alarm_f == FALSE), it would be fair to assume that there’s no sleep correction (sc) to be made, therefore, their chronotype (msf_sc) must be equal to their midsleep on work-free days (msf).

Following this same line of thought, we can also say that:

• sd_week (average weekly sleep duration) must be equal to sd_f (sleep duration on work-free days) since the respondent does not have workdays.
• sloss_week (weekly sleep loss) must be equal to 0s since there’s no sleep debt.
• le_week (average weekly light exposure) must be equal to le_f (light exposure on work-free days) since there are no workdays.
• sjl_rel (relative social jet lag), sjl (absolute social jet lag), sjl_sc_rel (Jankowski’s relative sleep-corrected social jetlag), and sjl_sc (Jankowski’s sleep-corrected social jetlag) must be equal to 0s since there’s no discrepancy between social and biological time.

Note that the chronotype computation follows a similar line of thought.

The opposite scenario, i.e., when the respondent works 7 days per week (wd == 7) and only answered the workdays section, can also have different outputs. sloss_week(), msf_sc(), sjl_rel(), sjl(), sjl_sc_rel(), and sjl_sc() should still produce a NA as output since there’s no way to know the real behavior of the respondent’s sleep-wake cycle. But, according to this reasoning, sd_week and le_week can have different outputs.

• sd_week (average weekly sleep duration) must be equal to sd_w (sleep duration on workdays) since the respondent does not have work-free days.
• le_week (average weekly light exposure) must be equal to le_w (light exposure on work-free days) since the respondent does not have work-free days.

If you agree with this line of thought, we recommend creating dummy variables to identify those two situations and then change the values as mentioned. You can see this procedure in action with the data wrangling algorithms made to produce the fictional std_mctq dataset, provided by the mctq package.

Please note that this workaround is not mentioned or endorsed by the MCTQ authors. If you use it, you must mention this reasoning in your methods section.