Florian Pargent

Akademischer Rat (Lecturer)

Ludwig-Maximilians-Universität München

Germany

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About

Florian Pargent is an Akademischer Rat (Lecturer) at Ludwig-Maximilians-Universität München, Germany. His research primarily focuses on cost-sensitive machine learning, causal frameworks for latent variables, and the application of statistical methods in psychology. Recent publications include topics such as sample-size planning for experimental designs, reproducible research, and the evaluation of screening tools for ADHD in adults.

Articles (14)

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2021

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2020

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2020

Collaborators (8)

Lena Hahn

University of Trier

GERMANY

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Ramona Schoedel

Professor for Differential Psychology & Psychological Diagnostics

GERMANY

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Susann Fiedler

WU Vienna University of Economics and Business

AUSTRIA

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Anne-Kathrin Kleine

University of Groningen

NETHERLANDS

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Dominik Deffner

GERMANY

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Clemens Stachl

Prof. Dr.

University of St. Gallen

SWITZERLAND

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Bernd Bischl

Professor

Ludwig-Maximilians-Universität München

GERMANY

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Philipp Sterner

Guest Scientist and Lecturer

Ludwig-Maximilians-Universität München

GERMANY

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Florian Pargent

Research Interests

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Articles (14)

A Tutorial on Tailored Simulation-Based Sample-Size Planning for Experimental Designs With Generalized Linear Mixed Models

A Causal Framework for the Comparability of Latent Variables

Das Konfidenz- / Signifikanzniveau impliziert ein bestimmtes Kostenverhältnis zwischen Fehler 1. Art und Fehler 2. Art

Best Practices in Supervised Machine Learning: A Tutorial for Psychologists

Everything has its price: Foundations of cost-sensitive machine learning and its application in psychology.

Machine Learning and Risk Assessment: Random Forest Does Not Outperform Logistic Regression in the Prediction of Sexual Recidivism

Eleven strategies for making reproducible research and open science training the norm at research institutions

Florian Pargent, David Goretzko and Timo von Oertzen's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features

Evaluation of the German Version of the Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5 as a Screening Tool for Adult Attention-Deficit/Hyperactivity Disorder in Primary Care

What’s the measure? An empirical investigation of self-ratings on response scales

Aging and the rehabilitation of homonymous hemianopia: The efficacy of compensatory eye-movement training techniques and a five-year follow up

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

Not Very Powerful

Collaborators (8)

Lena Hahn

Ramona Schoedel

Susann Fiedler

Anne-Kathrin Kleine

Dominik Deffner

Clemens Stachl

Bernd Bischl

Philipp Sterner

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