gaurav soodoku

Personality Change

Personality psychology reports "moderate stability" of traits across adulthood, but what does this mean for individuals? We simulated how likely someone is to obtain similar Big Five personality test results over a 30-year span, critically accounting for measurement error.

Method: We generated 10,000 individuals with Big Five traits following a multivariate normal distribution with empirical intercorrelations (e.g., Neuroticism with Extraversion: -0.22). We modeled personality at Time 2 using the test-retest formula: New_Score = Old_Score × r + error × √(1-r²), adding measurement error (reliability = 0.82, per Gnambs 2014) at both time points. We defined "meaningful change" as shifts exceeding 0.5 standard deviations. Crucially, we established a baseline scenario with perfect stability (r=1.0) to isolate measurement error effects, finding that 33% appear to change due to measurement error alone. We then examined: Soldz & Vaillant's (1999) 30-year correlations (E: 0.19, N: 0.20, O: 0.3

To go with Andrew's https://statmodeling.stat.columbia.edu/2017/05/19/continuous-hinge-function-bayesian-modeling/

Also includes alternate smoothers outside of logistic.

Used Claude (but verified).

Scraping the abstracts for the discussion papers by i4replication.org.

RQ Boot to Latex

ModelSummary and Stargazer fail when RQ s.e. calculation fails

stargazer(m_demo_int_qr)
Error in base::backsolve(r, x, k = k, upper.tri = upper.tri, transpose = transpose,  : 
  singular matrix in 'backsolve'. First zero in diagonal [13]

	# -- coding: utf-8 --
	"""sentiment_ribeiro_style_checklist.ipynb

	Automatically generated by Colab.

	Original file is located at
	https://colab.research.google.com/drive/1uyBg9-sidauCSSO6dcztl552HVSAgkUJ
	"""

	"""

	data {
	int<lower=0> N;
	int<lower=0> D;
	matrix[N, D] x;
	array[N] int<lower=0, upper=1> y;

	real<lower=0> h;
	int<lower=0> B;
	array[B] vector[D] beta0;

	#!/usr/bin/env python3
	import argparse
	import json
	import requests
	import sys
	from urllib.parse import quote

	def parse_arguments():
	"""Parse command line arguments"""
	parser = argparse.ArgumentParser(description="List Dataverse datasets owned by a user and convert to JSON")

	#!/usr/bin/env python3
	"""
	Dataverse File Uploader

	A script to upload files to Harvard Dataverse or other Dataverse instances.
	"""

	import os
	import argparse
	import requests