The Fundamentals of People Analytics : : With Applications in R.
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| Place / Publishing House: | Cham : : Springer International Publishing AG,, 2023. Ã2023. |
| Year of Publication: | 2023 |
| Edition: | 1st ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | 1 online resource (386 pages) |
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Table of Contents:
- Intro
- Foreword
- Preface
- Contents
- Getting Started
- Guiding Principles
- Pro-Employee Thinking
- Quality
- Prioritization
- Tooling
- Data Sets
- Employees
- Turnover Trends
- Survey Responses
- 4D Framework
- Introduction to R
- Getting Started
- Installing R
- Installing R Studio
- Installing Packages
- Loading Data
- Case Sensitivity
- Help
- Objects
- Comments
- Testing Early and Often
- Vectors
- Vectorized Operations
- Matrices
- Factors
- Data Frames
- Lists
- Loops
- User-Defined Functions (UDFs)
- Graphics
- Review Questions
- Introduction to SQL
- Basics
- Aggregate Functions
- Joins
- Subqueries
- Virtual Tables
- Window Functions
- Common Table Expressions (CTEs)
- Review Questions
- Research Design
- Research Questions
- Research Hypotheses
- Internal and External Validity
- Research Methods
- Research Designs
- Experimental Research
- Quasi-Experimental Research
- Non-Experimental Research
- Review Questions
- Measurement and Sampling
- Variable Types
- Independent Variables (IV)
- Dependent Variables (DV)
- Control Variables (CV)
- Moderating Variables
- Mediating Variables
- Endogenous vs. Exogenous Variables
- Measurement Scales
- Discrete Variables
- Nominal
- Ordinal
- Continuous Variables
- Interval
- Ratio
- Sampling Methods
- Probability Sampling
- Simple Random Sampling
- Stratified Random Sampling
- Cluster Sampling
- Systematic Sampling
- Non-Probability Sampling
- Convenience (Accidental) Sampling
- Quota Sampling
- Purposive (Judgmental) Sampling
- Sampling and Nonsampling Error
- Sampling Error
- Selection Bias
- Nonsampling Error
- Nonresponse Bias
- Nontruthful Responses
- Measurement Error
- Scale Reliability and Validity
- Reliability
- Validity
- Face validity
- Content Validity
- Construct Validity.
- Criterion-Related Validity
- Review Questions
- Data Preparation
- Data Extraction
- Data Architecture
- Data Lake
- Data Warehouse
- Data Mart
- Database Normalization
- Modern Data Infrastructure
- Data Screening and Cleaning
- Missingness
- Outliers
- Low Variability
- Inconsistent Categories
- Data Binning
- One-Hot Encoding
- Feature Engineering
- Review Questions
- Descriptive Statistics
- Univariate Analysis
- Measures of Central Tendency
- Mean
- Median
- Mode
- Range
- Measures of Spread
- Variance
- Standard Deviation
- Quartiles
- Skewness
- Kurtosis
- Bivariate Analysis
- Covariance
- Correlation
- Review Questions
- Statistical Inference
- Introduction to Probability
- Probability Distributions
- Discrete Probability Distributions
- Continuous Probability Distributions
- Conditional Probability
- Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- Alpha
- Type I &
- II Errors
- p-Values
- Bonferroni Correction
- Statistical Power
- Review Questions
- Analysis of Differences
- Parametric vs. Nonparametric Tests
- Differences in Discrete Data
- Chi-Square Test
- Fisher's Exact Test
- Differences in Continuous Data
- Independent Samples t-Test
- Mann-Whitney U Test
- Paired Samples t-Test
- Wilcoxon Signed-Rank Test
- Analysis of Variance (ANOVA)
- Factorial ANOVA
- Review Questions
- Linear Regression
- Assumptions and Diagnostics
- Sample Size
- Simple Linear Regression
- Multiple Linear Regression
- Collinearity Diagnostics
- Variable Selection
- Moderation
- Mediation
- Review Questions
- Linear Model Extensions
- Model Comparisons
- Hierarchical Regression
- Multilevel Models
- Polynomial Regression
- Review Questions
- Logistic Regression
- Binomial Logistic Regression
- Multinomial Logistic Regression
- Ordinal Logistic Regression.
- Review Questions
- Predictive Modeling
- Cross-Validation
- Validation Set Approach
- Leave-One-Out
- k-Fold
- Model Performance
- Classification
- Forecasting
- Bias-Variance Tradeoff
- Tree-Based Algorithms
- Decision Trees
- Random Forests
- Predictive Modeling
- Classification
- Forecasting
- Review Questions
- Unsupervised Learning
- Factor Analysis
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis (CFA)
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Review Questions
- Data Visualization
- Best Practices
- Color Palette
- Chart Borders
- Zero Baseline
- Intuitive Layout
- Preattentive Attributes
- Step-by-Step Visual Upgrade
- Step 1: Build Bar Chart with Defaults
- Step 2: Remove Legend
- Step 3: Assign Colors Strategically
- Step 4: Add Axis Titles and Margins
- Step 5: Add Left-Justified Title
- Step 6: Remove Background
- Step 7: Remove Axis Ticks
- Step 8: Mute Titles
- Step 9: Flip Axes
- Step 10: Sort Data
- Visualization Types
- Tables
- Heatmaps
- Scatterplots
- Line Graphs
- Slopegraphs
- Bar Charts
- Combination Charts
- Waterfall Charts
- Waffle Charts
- Sankey Diagrams
- Pie Charts
- 3D Visuals
- Elegant Data Visualization
- Review Questions
- Data Storytelling
- Know Your Audience
- Production Status
- Structural Elements
- TL
- DR
- Purpose
- Methodology
- Results
- Limitations
- Next Steps
- The Appendix
- Q&
- A
- Review Questions
- Appendix
- Appendix
- 4D Framework
- Discover
- Client
- Primary Objective
- Problem Statement
- Guiding Theories
- Research Questions
- Research Hypotheses
- Assumptions
- Cadence
- Aggregation
- Deliverable
- Filters and Dimensions
- Design
- Data Privacy
- Data Sources and Elements
- Data Quality
- Variables
- Analysis Method
- Dependencies
- Change Management.
- Sign-Off
- Develop
- Development Patterns
- Productionalizable Code
- Unit Testing
- User Acceptance Testing (UAT)
- Deliver
- Data Visualization
- Step-by-Step Visual Upgrade
- Tables
- Heatmaps
- Scatterplots
- Line Charts
- Slopegraphs
- Bar Charts
- Combination Charts
- Waterfall Charts
- Waffle Charts
- Sankey Diagrams
- Pie Charts
- Bibliography
- Index.