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Open Access 2023 | Open Access | Buch

The Fundamentals of People Analytics

With Applications in R

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SUCHEN

Über dieses Buch

Dieses Open-Access-Buch bereitet aktuelle und aufstrebende Analyseprofis darauf vor, diesem Bedürfnis wirksam zu begegnen, indem es Schlüsselkonzepte über den gesamten Analytics-Lebenszyklus kuratiert und Schritt-für-Schritt-Anleitungen für ihre Anwendung auf reale Probleme mithilfe allgegenwärtiger und frei verfügbarer Open-Source-Software gibt. Dieses Buch setzt keine Vorkenntnisse in Statistik, Abfrage von Datenbanken oder Erstellung leistungsfähiger Codes voraus; frühe Kapitel beinhalten eine Einführung in R und SQL sowie einen Überblick über statistische Grundlagen. Humankapital ist der wichtigste Aktivposten einer Organisation. Ohne das Wissen und die Fähigkeiten der Menschen kann eine Organisation nichts erreichen. Die Akquisition, Entwicklung und Bindung kritischer Talente wird zunehmend komplexer und schwieriger, und Organisationen tätigen beträchtliche Investitionen, um ein tieferes, datengestütztes Verständnis der Organisationsphänomene zu erlangen, die sich auf das Ergebnis auswirken. Am Ende dieses Buches werden die Leser in der Lage sein: • Empirische Forschung zu entwerfen und durchzuführen • Daten mittels SQL abzufragen und zu streiten • Daten mithilfe von R zu profilieren, zu reinigen und zu analysieren • geeignete statistische und ML-Modelle auf eine Reihe von Anwendungsfällen anzuwenden • Analysen zu bündeln und zu präsentieren, um Stakeholdern wirkungsvolle Erkenntnisse zu vermitteln

Inhaltsverzeichnis

Frontmatter

Open Access

Getting Started
Abstract
This chapter outlines guiding principles and a framework to support the success of people analytics projects. In addition, analytics tooling and data sets utilized in this book are discussed.
Craig Starbuck

Open Access

Introduction to R
Abstract
This chapter covers the basics of R, including how to install the software and packages, load libraries and data, and work with various types of objects and functions.
Craig Starbuck

Open Access

Introduction to SQL
Abstract
This chapter covers the basics of SQL and how to implement SQL statements within the R environment. Concepts and examples span SQL clauses, aggregate functions, joins, subqueries, virtual tables, window functions, and common table expressions (CTEs).
Craig Starbuck

Open Access

Research Design
Abstract
This chapter provides an overview of the elements of research, including research questions, hypotheses, methods, and designs. Factors influencing internal and external validity are also discussed.
Craig Starbuck

Open Access

Measurement and Sampling
Abstract
This chapter surveys variable types and measurement scales as well as probability and non-probability sampling methods. In addition, topics in sampling and nonsampling error and scale reliability and validity are covered.
Craig Starbuck

Open Access

Data Preparation
Abstract
This chapter explains data architecture concepts needed to properly integrate and extract data from analytics platforms as well as methods of screening and cleaning data (e.g., missingness, outliers, data binning, one-hot encoding, feature engineering).
Craig Starbuck

Open Access

Descriptive Statistics
Abstract
This chapter reviews types of univariate analyses (e.g., measures of central tendency and spread) and bivariate analyses (e.g., covariance and correlation).
Craig Starbuck

Open Access

Statistical Inference
Abstract
This chapter covers the fundamentals of statistical inference. Topics include discrete and continuous probability distributions, conditional probability, Central Limit Theorem (CLT), confidence intervals, hypothesis testing, multiple testing, and statistical power.
Craig Starbuck

Open Access

Analysis of Differences
Abstract
This chapter examines parametric tests and nonparametric alternatives for testing whether statistical differences are observed in data measured on discrete and continuous scales. Methods of quantifying the magnitude of observed differences are also reviewed.
Craig Starbuck

Open Access

Linear Regression
Abstract
This chapter covers one of the most valuable tools for people analytics professionals: linear regression. Concepts, assumptions, and step-by-step implementations are presented for both simple and multiple linear regression as well as methods for testing more complex moderated and mediated relationships.
Craig Starbuck

Open Access

Linear Model Extensions
Abstract
This chapter covers methods of extending the linear regression model for mixed effects, non-linear relationships, and use cases that involve examining changes to variable and model-level performance with stepwise variable selection procedures. Comparing and interpreting coefficients across multiple models is also discussed.
Craig Starbuck

Open Access

Logistic Regression
Abstract
This chapter covers a type of generalized linear model, logistic regression, that is applied to settings in which the outcome variable is not measured on a continuous scale. Binomial, multinomial, and ordinal logistic regression models are covered.
Craig Starbuck

Open Access

Predictive Modeling
Abstract
This chapter progresses from explanatory to predictive models. Topics include cross-validation, model performance metrics, prediction intervals, bias–variance tradeoff, tree-based algorithms, and various types of models with utility in classification and forecasting applications.
Craig Starbuck

Open Access

Unsupervised Learning
Abstract
This chapter covers dimension reduction techniques that have utility in exploring and confirming the factor structure of psychological instrumentation; techniques include exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and principal components analysis (PCA). K-means and hierarchical clustering methods are examined for surfacing patterns and insights in unsupervised settings in which there is no outcome variable.
Craig Starbuck

Open Access

Data Visualization
Abstract
This chapter provides best practices for visualizing data and discusses common types of data visualizations and their respective applications in people analytics contexts. Types of data visualizations include tables, heatmaps, scatterplots, line graphs, slopegraphs, bar charts, combination charts, waterfall charts, waffle charts, Sankey diagrams, and pie charts.
Craig Starbuck

Open Access

Data Storytelling
Abstract
This chapter provides best practices for effectively communicating to stakeholders with data. Topics include knowing the audience, implementing a status taxonomy for analysis documents, and various structural elements of analysis presentations (e.g., TL;DR, purpose, methodology, results, limitations, next steps, appendix).
Craig Starbuck
Backmatter
Metadaten
Titel
The Fundamentals of People Analytics
verfasst von
Craig Starbuck
Copyright-Jahr
2023
Electronic ISBN
978-3-031-28674-2
Print ISBN
978-3-031-28673-5
DOI
https://doi.org/10.1007/978-3-031-28674-2