ReCentering Psych Stats

Format:

Book

Author/Creator:

Bikos, Lynette, author.

Language:

English

Subjects (All):

Social sciences--Textbooks.

Social sciences.

Psychology--Textbooks.

Psychology.

Physical Description:

1 online resource

Place of Publication:

Seattle, WA Seattle Pacific University Library 2023.

Language Note:

In English.

Summary:

To center a variable in regression means to set its value at zero and interpret all other values in relation to this reference point. Regarding race and gender, researchers often center male and White at zero. Further, it is typical that research vignettes in statistics textbooks are similarly seated in a White, Western (frequently U.S.), heteronormative, framework. ReCentering Psych Stats seeks provide statistics training for psychology students (undergraduate, graduate, and post-doctoral) in a socially and culturally responsive way. All lessons use the open-source statistics program, R (and its associated packages). Each lesson includes a chapter and screencasted lesson, features a workflow for statistical decision-making, and includes all R code necessary to conduct the statistic. Research vignettes are drawn from the published psychology literature. When possible, these articles are authored by individuals who hold identities that have, been marginalized in the scientific literature; correctly use the statistic that is being taught in the lesson; and focus on issues of justice, equity, inclusion, or and diversity. When possible, lessons include interviews with researchers from the featured vignettes. Each chapter includes suggestions for practice that are graded in complexity, such that learners can choose the degree of challenge. ReCentering Psych Stats is perpetually-in-progress; suggestions for corrections or chapters are welcomed: ReCenteringPsychStats@spu.edu

Contents:

Introduction

Ready_Set_R

Preliminary Analyses

One Sample t-tests

Independent Samples t-test

Paired Samples t-test

One-way ANOVA

Factorial (Between-Subjects) ANOVA

One-Way Repeated Measures ANOVA

Mixed Design ANOVA

Analysis of Covariance

Type I Error

Examples for Follow-up to Factorial ANOVA

One-Way Repeated Measures with a Multivariate Approach

Notes:

Description based on print resource