#18506 | AsPredicted

'Effects of scarcity on child-directed speech: Homebank sample'
(AsPredicted #18506)

Created:       01/11/2019 06:16 AM (PT)

This is an anonymized version of the pre-registration.  It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.

1) Have any data been collected for this study already?
It's complicated. We have already collected some data but explain in Question 8 why readers may consider this a valid pre-registration nevertheless.

2) What's the main question being asked or hypothesis being tested in this study?
We hypothesize that scarcity systematically reduces parents’ contingent child-directed speech. Given evidence that adults feel the greatest level of financial scarcity at the end of the month, we will assess whether parent-child conversational turns are lower during the last week of the month compared to the rest of the month. We will use already collected, publicly available all-day LENA recordings from the Homebank corpus. Pending approval/availability, we will use the Bergelson, Cougar, and Warlaumont corpora.

3) Describe the key dependent variable(s) specifying how they will be measured.
Contingent child-directed speech: conversational turns per hour, as quantified by the LENA system.
Parent income: self-reported family income, as collected by the researchers.

4) How many and which conditions will participants be assigned to?

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
We will perform a linear mixed effects model predicting rate of conversational turns (CT) from time of the month as a categorical variable (beginning: days 1-23 vs. end: days 24+), with subjects as a repeated measure (random intercept). We will compare this to a null model predicting rate of CT from the intercept alone to assess whether time of month adds predictive power to the model. We predict CT will be lower at the end of the month compared to the rest of the month. We will run this analysis separately in each corpus, as well as combined across all data points with corpus as a covariate. Effects may vary as a function of corpus due to differences in participant demographics, including family income, as described below.

We will examine income by time of month interactions in predicting CT. It is possible that parents with the highest incomes will show less of a time of month effect due to experiencing less financial scarcity. On the other hand, it is also possible that parents with the lowest incomes may not show a time of month effect, due to irregular paycheck schedules and/or a more pervasive experience of financial scarcity across the month.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Recordings will be excluded if they have less than 6 hours of usable data and/or do not have clear information about the recording date, length, CT, AWC, or CVC.

7) How many observations will be collected or what will determine sample size?
No need to justify decision, but be precise about exactly how the number will be determined.

As many subjects as usable from the Bergelson, Cougar, and/or Warlaumont corpora.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)

As secondary analyses, we will also explore CT across each week, and linear and non-linear fits of data predicting conversational turns based on day of the month. We predict that there may be a peak of CT at the beginning and middle of the month, around typical paycheck schedules (though see #5 for potential income interactions).

In addition, we will examine adult word count (AWC) and child utterances (CVC) over the course of the month. We will repeat our primary analyses controlling for each of these separately.

Data have already been collected and made publicly available by other researchers, but we have not yet run any analyses.