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UID:pretalx-sips2026-dc-MJXKKQ@pretalx.com
DTSTART;TZID=EST:20260608T123500
DTEND;TZID=EST:20260608T124000
DESCRIPTION:Scales are the primary assessment method for subjective data in
  psychological science. However\, validating the scale's hypothesized fact
 or structure using a confirmatory factor analysis (CFA) requires a large s
 ample\, which is not always feasible. Therefore\, we propose an approach t
 o validating existing scales on a large sample without new data collection
 . By systematically searching for publicly available data from studies tha
 t used the original unmodified scale\, a CFA can be conducted on the combi
 ned datasets. This approach is beneficial in cases where the scale has not
  been validated on large samples and where new validation is needed using 
 samples from different populations and contexts. We applied this approach 
 to validate two widely used scales in human–robot interaction research a
 nd found only moderate support for their original factor structures. In ad
 dition\, we identified limited generalization across stimulus types\, high
 lighting the need for validation to ensure appropriate scale usage.
DTSTAMP:20260524T192124Z
LOCATION:AUDITORIUM
SUMMARY:A Systematic Approach for Validating Scales Using Open Data - Eilee
 n Roesler
URL:https://pretalx.com/sips2026-dc/talk/MJXKKQ/
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