Let's have a closer look today at Cribsheet, a book I mentioned in passing back in May in a context tangentially related to the main idea of this work, which is how to use data to inform the parenting of young children. It's not to be confused with a "how-to" guide, however, for despite the occasional straightforward remark like "data says swaddling improves sleep", the difficulty of collecting reliable data for all parenting scenarios prevents the existence of such a book - how do you track cause and effect for incidents like "baby cried"? The most important thing, as Oster hints throughout, is to understand the quality of the data before incorporating it into a decision, which is a point that reemerges constantly within my book notes.
Cribsheet by Emily Oster (February 2020)
The biggest obstacle to having high-quality data in this (or any) field that studies human behavior is that the cause and effect relationship is often confounded by underlying differences that define people's choices. For example, studying the effect of a stay-at-home parent on a child's development is complicated by the ways such a household is fundamentally different from one where both parents work full-time. It's a similar challenge to understand the effect of TV, as homes with a TV differ from those without. The problem with studying various parenting techniques under controlled conditions is that parents cannot be forced into applying any methods with their kids, which means researchers have no choice but to allow subjects to determine their own degree of involvement and commitment to the study; the fact of choice always has an immeasurable bias in the result. The same applies in any study where a control group is recruited, or participants must self-report their findings - again, the decisions made by the participants compromise the standards of the randomized control study.
This does not rule out the possibility of gleaning productive insights from data analysis. One specific recommendation I loved was to rely on distribution information about infant weight loss to better contextualize what might otherwise be alarming changes in those first few days. It speaks to the power of disaggregated data to help people relate across their differences - in this case, parents redefine their standard for "alarming weight loss" so that they can care for their infants. Disaggregation is a regular theme in Cribsheet, appearing in sections about breastfeeding (locations with poor water supplies should always encourage breastfeeding, since alternatives would involve direct use of that water) or reading (children who are read to don't see additional gains in other areas like math when compared to those who were not read to yet received a similar amount of attention, suggesting something specifically important about reading).
The ideas I'm likely to remember best are those I'd categorize as informed hunches, which seem to come not from the data but rather the wisdom of the aggregated experiences across a countless number of parents - that consistency is a critical factor in sleep training, for example, or that interactive reading (asking open-ended questions as you go) is a productive way to read to children. I also liked the note that repeated exposure is a helpful way to get children used to new foods as well as the observation that discussion rarely improves a child's behavior. Of course, as I look over these one last time, I'm struck by an odd realization - these all apply to adults, in varying degrees; perhaps we can all benefit in some ways from Cribsheet, regardless of our parenting status.
TOA Rating: Three cribs out of four.
