Table 1.
Descriptive statistics for parent and child linguistic markers and child phenotypic data
| Child linguistic markers | M | SD | Min | Max |
|---|---|---|---|---|
| Total word count | 399.06 | 166.38 | 15.00 | 953.00 |
| Speech rate | 124.12 | 17.72 | 72.66 | 180.68 |
| Interruption duration | 19.76 | 46.16 | 0.04 | 268.32 |
| Positive emotion words | 1.16 | 0.81 | 0.00 | 6.67 |
| Sad words | 0.65 | 0.57 | 0.00 | 2.92 |
| Anger words | 0.80 | 0.58 | 0.00 | 3.05 |
| Parent linguistic markers | M | SD | Min | Max |
| Total word count | 598.90 | 264.20 | 86.00 | 1227.50 |
| Speech rate | 183.72 | 29.00 | 108.32 | 244.37 |
| Interruption duration | 42.08 | 83.24 | 0.11 | 484.16 |
| Positive emotion words | 1.05 | 0.49 | 0.00 | 3.29 |
| Sad words | 0.40 | 0.34 | 0.00 | 2.38 |
| Anger words | 0.36 | 0.38 | 0.00 | 2.42 |
| Child phenotypic data | M | SD | Min | Max |
| CU traits (latent score) | 0.00 | 0.49 | -1.19 | 1.13 |
| Conduct problems (latent score) | 0.03 | 0.44 | -0.46 | 1.66 |
Linguistic markers were generated from processing text files in LIWC software (Boyd et al., 2022), which calculates counts for positive emotion, anger, and sad words, presented and analyzed here as a percentage of the total number of words spoken. Conversational markers were derived from utterances produced by each speaker, until a speaker changed, which were considered one turn. Utterances within turns were defined as speech segments. To assess interruptions, we identified overlapping speech (i.e., negative duration between prior and subsequent speech segments). We included the sum of overlapping speech duration (i.e., sum of interruptions) in analysis. We classified overlapping speech as parent or child interruption depending on who was interrupting the previous speaker. We assessed speech rate (number of words per minute) by dividing the total number of words by the sum duration of speech segments plus within-turn (i.e., without a speaker change) and between-turn (i.e., with a speaker change) pause durations and multiplying this value by 60 (Cho et al., 2023). To generate a robust assessment of lexical and conversational features across contexts, we computed mean scores for linguistic markers for children and parents combining across the two storybooks.