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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() |