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 |
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Physical Description: | 1 online resource (386 pages) |
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Starbuck, Craig. The Fundamentals of People Analytics : With Applications in R. 1st ed. Cham : Springer International Publishing AG, 2023. Ã2023. 1 online resource (386 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Print version: Starbuck, Craig The Fundamentals of People Analytics Cham : Springer International Publishing AG,c2023 9783031286735 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30622092 Click to View |
language |
English |
format |
eBook |
author |
Starbuck, Craig. |
spellingShingle |
Starbuck, Craig. The Fundamentals of People Analytics : With Applications in R. 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. |
author_facet |
Starbuck, Craig. |
author_variant |
c s cs |
author_sort |
Starbuck, Craig. |
title |
The Fundamentals of People Analytics : With Applications in R. |
title_sub |
With Applications in R. |
title_full |
The Fundamentals of People Analytics : With Applications in R. |
title_fullStr |
The Fundamentals of People Analytics : With Applications in R. |
title_full_unstemmed |
The Fundamentals of People Analytics : With Applications in R. |
title_auth |
The Fundamentals of People Analytics : With Applications in R. |
title_new |
The Fundamentals of People Analytics : |
title_sort |
the fundamentals of people analytics : with applications in r. |
publisher |
Springer International Publishing AG, |
publishDate |
2023 |
physical |
1 online resource (386 pages) |
edition |
1st ed. |
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. |
isbn |
9783031286742 9783031286735 |
callnumber-first |
Q - Science |
callnumber-subject |
QA - Mathematics |
callnumber-label |
QA276-280 |
callnumber-sort |
QA 3276 3280 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30622092 |
illustrated |
Not Illustrated |
oclc_num |
1390203552 |
work_keys_str_mv |
AT starbuckcraig thefundamentalsofpeopleanalyticswithapplicationsinr AT starbuckcraig fundamentalsofpeopleanalyticswithapplicationsinr |
status_str |
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(MiAaPQ)50030622092 (Au-PeEL)EBL30622092 (OCoLC)1390203552 |
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cr |
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The Fundamentals of People Analytics : With Applications in R. |
marc_error |
Info : Unimarc and ISO-8859-1 translations identical, choosing ISO-8859-1. --- [ 856 : z ] |
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