How Long? How Many? How Much? Evidence of Convergent Validity Among Thin-Slice Behavioral Coding Metrics.

Despite the ubiquity of thin-slice coding for behavioral measurement, there exists relatively little systematic research into the convergent validity of thin-slice coding metrics for nonverbal behaviors when using human coders. This study utilized five previous datasets to measure four commonly-meas...

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Publicado en:Journal of Nonverbal Behavior Vol. 49; no. 3; pp. 307 - 324
Autores principales: Murphy, Nora A., Ruben, Mollie A., Stosic, Morgan, Renier, Laetitia A., Schlegel, Katja, Hall, Judith A., Schmid Mast, Marianne, Marroquín, Brett
Formato: Artículo
Publicado: Springer Nature Sep2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        10.1007/s10919-025-00489-w
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        atl: How Long? How Many? How Much? Evidence of Convergent Validity Among Thin-Slice Behavioral Coding Metrics.
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        au:
          Murphy, Nora A.
          Ruben, Mollie A.
          Stosic, Morgan
          Renier, Laetitia A.
          Schlegel, Katja
          Hall, Judith A.
          Schmid Mast, Marianne
          Marroquín, Brett
        affil:
          https://ror.org/00xhj8c72 Loyola Marymount University, Los Angeles, USA
          https://ror.org/013ckk937 University of Rhode Island, Kingston, USA
          https://ror.org/01adr0w49 University of Maine, Orono, USA
          https://ror.org/01g1xae87 KBR, Behavioral Health and Performance Laboratory, Biomedical Research and Environmental Sciences Division, Human Health and Performance Directorate, NASA Johnson Space Center, NASA Johnson Space Center, USA
          https://ror.org/019whta54 University of Lausanne, Lausanne, Switzerland
          https://ror.org/02k7v4d05 University of Bern, Bern, Switzerland
          https://ror.org/04t5xt781 Northeastern University, Boston, USA
      su:
        Nonverbal communication
        Body language
        Interpersonal relations
        Facial expression
        Statistical correlation
        Cronbach's alpha
        Data analysis
        T-test (Statistics)
        Research evaluation
        Statistical sampling
        Descriptive statistics
        Medical coding
        Research methodology
        Statistics
        Video recording
        Eye movements
        Predictive validity
      sug:
        subj:
          Nonverbal communication
          Body language
          Interpersonal relations
          Facial expression
          Marketing Research and Public Opinion Polling
          Statistical correlation
          Cronbach's alpha
          Data analysis
          T-test (Statistics)
          Research evaluation
          Statistical sampling
          Descriptive statistics
          Medical coding
          Research methodology
          Statistics
          Video recording
          Eye movements
          Predictive validity
      keyword:
        Behavioral Coding
        Convergent Validity
        Gaze
        Gesture
        Nod
        Smile
        Thin Slices
        Behavioral Coding
        Convergent Validity
        Gaze
        Gesture
        Nod
        Smile
        Thin Slices
      ab: Despite the ubiquity of thin-slice coding for behavioral measurement, there exists relatively little systematic research into the convergent validity of thin-slice coding metrics for nonverbal behaviors when using human coders. This study utilized five previous datasets to measure four commonly-measured nonverbal behaviors (gaze, gestures, nods, smiles) using three different coding metrics (duration, frequency, rating) coded in 2 or 3 min slices. Convergent validity was measured by comparing a given behavior coded with at least two different metrics. Meta-analytic assessments across studies, behaviors, and metrics indicated strong convergent validity for various metrics for each behavior. Results provide confidence to researchers on the validity of using these thin-slice coding metrics for nonverbal behaviors.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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