A Note on Cohen’s d From a Partitioned Linear Regression Model

5Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

In this note, we introduce a generalized formula for Cohen’s d under the presence of additional independent variables, providing a measure for the size of a possible effect concerning the size of a difference location effect of a variable in two groups. This is done by employing the so-called Frisch–Waugh–Lovell theorem in a partitioned linear regression model. The generalization is motivated by demonstrating the relationship to appropriate t and F statistics. Our discussion is further illustrated by inference about a publicly available data set.

References Powered by Scopus

Statistical methods in psychology journals: Guidelines and explanations

2346Citations
N/AReaders
Get full text

Seasonal Adjustment of Economic Time Series and Multiple Regression Analysis

207Citations
N/AReaders
Get full text

Estimation under a general partitioned linear model

39Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management

29Citations
N/AReaders
Get full text

Multi-objective optimization and sustainable design: a performance comparison of metaheuristic algorithms used for on-grid and off-grid hybrid energy systems

28Citations
N/AReaders
Get full text

Nature-ınspired algorithms for optimizing fractional order PID controllers in time-delayed systems

9Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Groß, J., & Möller, A. (2023). A Note on Cohen’s d From a Partitioned Linear Regression Model. Journal of Statistical Theory and Practice, 17(2). https://doi.org/10.1007/s42519-023-00323-w

Readers over time

‘23‘24‘2502468

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 4

80%

Lecturer / Post doc 1

20%

Readers' Discipline

Tooltip

Materials Science 2

40%

Decision Sciences 1

20%

Medicine and Dentistry 1

20%

Social Sciences 1

20%

Save time finding and organizing research with Mendeley

Sign up for free
0