Measurement variability in infant and maternal behavioral assessment

Evaluations of methodological assessments sometimes show similar, sometimes different patterns of findings reflective of different approaches to the same problem. In this study, frequencies of behaviors of infants and mothers in two cultures based on continuous coding were compared with frequencies based on time-sampling, and resulting patterns of findings were evaluated. Time-sampling and continuous coding give different estimates of absolute frequency of typical infant and maternal behaviors between individuals and between cultural groups. However, time-sampling adequately preserves the relative ranking of infant and mother behaviors among individuals and between cultural groups. If research is concerned with the relative standing of individuals and/or groups on frequency of infant or maternal behavior, then (under specified circumstances) time-sampling and continuous coding yield comparable results.     

Psychometric Properties and Norms of the Family Needs Scale

We studied 205 low-income families, using the Family Needs Scale (FNS). Factor analysis of the FNS data resulted on a 7-factor solution with high internal consistency within the various subscales. We provide normative scores based on the factor structure of the FNS. A total of 53 parents completed the FNS on two occasions with an average of four weeks between these two ratings. In general, the test-retest reliabilities were low to moderate. A total of 61 pairs of parents independently rated their families with the FNS. Again, agreement between raters was low to moderate. Several factors that may have detracted from better test-retest and interrater reliability were identified. Our data point to the need for more psychometric studies with the FNS.     

Artificial intelligence for the measurement of vocal stereotypy

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session-by-session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.     

The Explorative Nature of Heideggerian Logic

Human Studies - This paper argues for a fundamental re-reading of Heideggerian philosophy, especially regarding the logical structures presented by Heidegger in the thirties and onward. The field,...     

Peat Bogs,Sperm, and Family Values: Teaching Naturalism Charitably

Introductory courses dealing with sex, gender and sexuality often assign excerpts from Thomas Aquinas as an exemplar of the naturalist view. Given that most novice students tend to side against such naturalism uncritically, they need to be exposed to a more charitable account of the biological considerations motivating a stance like Aquinas.’ With that in mind, this article presents accessible arguments aimed at restoring deliberative balance in the classroom.