Regression-Based Normative Data for Psychological Assessment : A Hands-On Approach Using R / by Wim Van der Elst.

Format:

Book

Author/Creator:

Van der Elst, Wim.

Language:

English

Subjects (All):

Psychology.

Psychological tests.

Psychology--Methodology.

Social sciences--Statistical methods.

Behavioral Sciences and Psychology.

Psychological Assessment.

Psychological Testing.

Psychological Methods.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Local Subjects:

Behavioral Sciences and Psychology.

Psychological Assessment.

Psychological Testing.

Psychological Methods.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Physical Description:

1 online resource (485 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

Over the last 20 years, so-called regression-based normative methods have become increasingly popular. In this approach, regression models for the mean and the residual variance structure are used to derive the normative data. The regression-based normative approach has some important advantages over the traditional normative approach, e.g., it allows for deriving more fine-grained norms and typically requires a substantially smaller sample size to derive accurate norms.This book focuses on regression-based methods to derive normative data. The target audience are psychologists and other researchers in the behavioral sciences who are interested in deriving normative data for psychological tests (e.g., cognitive tests, questionnaires, rating scales, etc.). The book provides the essential theoretical background that is needed to understand the methodology, with a strong emphasis on the practical/real-life application of the methodology. To this end, the book is also accompanied by an open-source software package (the R library NormData) that is used to exemplify how normative data can be derived in several case studies. Provides a solid introduction in regression-based normative methods without being overly technical; Comes with a comprehensive open-source software package to help efficiently derive regression-based normative data; Focuses strongly on the practical application of the methodology using various real-life case studies. .

Contents:

General introduction.-The R programming language

Normative data accounting for a binary independent variable

Assumption of the normal error regression model

Normative data accounting for a non-binary qualitative independent variable

Normative data accounting for a quantitative independent variable

Normative data accounting for multiple qualitative and/or quantitative independent variables

Quantifying uncertainty in regression-based norms.

Other Format:

Print version: Van der Elst, Wim Regression-Based Normative Data for Psychological Assessment

ISBN:

9783031509513

OCLC:

1438668415