import os import torch import dash import streamlit as st import pandas as pd import json import random import firebase_admin from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline from firebase_admin import credentials, firestore from dotenv import load_dotenv import plotly.graph_objects as go import demo_section import explore_data_section load_dotenv() if 'collect_data' not in st.session_state: st.session_state.collect_data = True if 'user_id' not in st.session_state: st.session_state.user_id = random.randint(1, 9999999) st.markdown(""" # Machine-Based Item Desirability Ratings This web application accompanies the paper "*Expanding the Methodological Toolbox: Machine-Based Item Desirability Ratings as an Alternative to Human-Based Ratings*". *Hommel, B. E. (2023). Expanding the methodological toolbox: Machine-based item desirability ratings as an alternative to human-based ratings. Personality and Individual Differences, 213, 112307. https://doi.org/10.1016/j.paid.2023.112307* ## What is this research about? Researchers use personality scales to measure people's traits and behaviors, but biases can affect the accuracy of these scales. Socially desirable responding is a common bias that can skew results. To overcome this, researchers gather item desirability ratings, e.g., to ensure that questions are neutral. Recently, advancements in natural language processing have made it possible to use machines to estimate social desirability ratings, which can provide a viable alternative to human ratings and help researchers, scale developers, and practitioners improve the accuracy of personality scales. """) demo_section.show() explore_data_section.show()