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UID:pretalx-sips2026-dc-JYU7RR@pretalx.com
DTSTART;TZID=EST:20260609T141000
DTEND;TZID=EST:20260609T141500
DESCRIPTION:Psychological science relies heavily on scale scores for diagno
 sis\, prediction\, and evaluating intervention effectiveness. Yet scale sc
 ores often aggregate items that reflect multiple\, and sometimes weakly re
 lated\, constructs. Drawing on validation work with the Adolescent Attachm
 ent Anxiety and Avoidance Inventory (AAAAI)\, this talk examines what is g
 ained and lost when item-level information is reduced to a scale score. Sc
 ale scores are contrasted with the shared variance of underlying latent fa
 ctor structure\, illustrating how theoretically distinct dimensions can be
  “smushed” into one number. At the same time\, different populations (
 such as clinical and non-clinical) distributions overlap\, such that a pro
 posed cutoff value trades misclassification between groups. This multi-dat
 aset analysis invites closer scrutiny of routine scale score practices by 
 using a concrete example to show how assumptions involving latent homogene
 ity and cutoff values are embedded in a single scale score.
DTSTAMP:20260524T192125Z
LOCATION:AUDITORIUM
SUMMARY:Scale Scores as Summary Statistics: An Example of What Gets Assumed
  Along the Way - Jason Payne
URL:https://pretalx.com/sips2026-dc/talk/JYU7RR/
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