Capturing Body Functional Imagery Dataset

Dataset Collection for Body-Functional Imagery (BFI) Studies
This dataset collection supports research validating criteria for identifying body-functional imagery (BFI) across two studies. The research hypothesizes that these guidelines can enhance understanding of BFI in media, promoting better body image and reducing sexual objectification in women. Both studies included participants aged 18+, fluent in English, and active on Instagram. Demographics are available in Datasets 1 and 2. Data was collected online surveys where participants were asked to categorize and evaluate images based on pre-defined criteria.
Dataset 1: Study 1 This dataset includes data from Study 1, which introduced and validated three primary BFI criteria. Participants categorized Instagram images into three groups: body ideal, body positive, and body functional. They rated perceived objectification using the Self-Objectification Questionnaire and a binary question. Results showed accurate categorization of images, with body functional images consistently rated as non-objectifying. With correct categorization of BFI and BFI perceived as not objectifying, this data indicated that the BFI criteria is valid, and accurately depicts BFI.
Included variables: Image placement by author (NID, Image Type), participant placement (PID, BI Image 1, BP Image 1, BF Image 1), Binary objectification (Binary Objectification, Objectifying), MSOQ attributes (Strength, Sex Appeal, Physical Fitness, Energy Levels, Physical Attractiveness, Firm/Sculpted Muscles, Physical Coordination, Measurements, Health, Weight), and combined MSOQ scores (SOQ functional, SOQ Appearance, SOQ Total).
Dataset 2: Study 2 This dataset corresponds to Study 2, which introduced three additional BFI criteria, validating all six. The study replicated Study 1’s results, confirming the robustness of the BFI criteria.
Included variables: Participant ID (PIDM), author categorization of images (NID), Degree of Fit (Image category), participant categorization (PID), and Binary Objectification Score (OB Score).
Dataset 3: Probabilities/Degree of Fit The third dataset contains probabilities calculated in Study 2 to determine each image's degree of fit with its respective category. Results showed a positive correlation between degree of fit and objectification of images in appearance based imagery. The opposite was true for BFI, where the degree of fit increased, perceived objectification decreased. This dataset to explore the precision and applicability of the BFI criteria, as well as to replicate the analysis or apply similar methodologies to other sets of social media imagery.
Included variables: Author categorization and degree of fit (CAT, Author Degree of Fit), probabilities of participant image placement (Prob of BI, Prob of BP, Prob of BF), probability of incorrect placement (opprb), and participant degree of fit (Part Degree of Fit).