Page numbers followed by f and t refer to figures and tables, respectively.

• AAPI, see Asian Americans and Pacific Islanders
• ABCDEs of DEI, 65, 65f, 171
• Academic All‐America^® Hall of Fame, xxii
• Accenture, 140, 155
• Acceptance, 48–49, 51, 243, 252
• ADA (Americans with Disabilities Act), 97
• Adaptation, 48–49
• Adichie, Chimamanda Ngozi, xvi, 247–248
• Ahmed, Rebecca, 98
• AI, see Appreciative Inquiry; Artificial intelligence
• “A.I. is Mastering Language. Should We Trust What It Says?” (Johnson), 266
• Alchemer, 94
• Algorithmic Justice League, 267
• Alice in Wonderland (Carroll), 134
• Alpha values, 104–105
• Amazon, 70–71, 140, 155, 257, 261
• Ambivista, 94
• American Hospital Association (AHA), 164
• American Red Cross, 16, 141, 157
• Americans with Disabilities Act (ADA), 97
• Analytic frameworks, 123, 123f
• Antiracist Style Indicator (ASI), 51–53, 52f, 242
  • functioning, 51, 94, 242
  • overfunctioning, 51
  • underfunctioning, 51, 242
• Apking, Anne, 176, 179
• Appreciative Inquiry (AI), 18, 77–78, 136, 181
• Archimedes, 160
• Artificial intelligence (AI), 71, 120, 263–264
• Asian Americans and Pacific Islanders (AAPI), 83–84, 106, 244
• AskNicely, 94
• Atlas.ti, 124
• ATM (Automated Topic Modeling), 265
• Atomic Habits (Clear), 147
• AT&T, 141
• Attitudes/attributes, 5, 26–27, 44, 170, 175, 179, 247
• Automated Topic Modeling (ATM), 265

• Banaji, Mahzarin, 31
• Bank of Montreal, xvii
• BART (Bidirectional Auto‐Regressive Transformers), 264
• Baselines, 64
  • definition of, 24
  • and DEI inventory, 171, 241–243
  • establishing, xx–xxi, xxiv–xxv, 52, 74, 126, 134–135, 206
  • tracking via scorecards, 208, 211
• Benchmarks (benchmarking), 98–102, 125. See also Ranks (ranking)
  • in assessment reports, 74, 129
  • global, 190
  • and performance, 125
  • and representation, 219
  • using standardized instruments for, 91
• Bennett, Milton, 49
• Benston, Jerry, 98
• Berry, E'Driana, 48
• BERT (Bidirectional Encoder Representations from Transformers), 264
• Best Companies for Diversity (Black Enterprise), 99
• Best Companies for Working Mothers and Best Companies for Dads (Working Mother), 99
• Best for Vets Employers (MilitaryTimes), 99
• Best Places to Work for LGBTQ Equality, 99
• Best Place to Work for Disability Inclusion, 99
• Bias(es):
  • affinity, xv, 170
  • algorithmic, xiv, 70–71, 131, 261–262
  • attribution, 71–72, 131, 170
  • cognitive, 29
  • confirmation, 71–72, 131
  • data, xiv, 70–71, 75, 131
  • implicit, xiii, 15, 31, 45, 70, 72, 145, 171
  • mitigating, xvii, 6–7, 18, 31–34, 64, 70–72, 75, 98, 148, 170
  • researcher, 71–72, 131
  • selection, 71, 114, 131–132, 258, 261, 267
  • unconscious, xv, xxii, 15, 29–32, 48, 232, 256, 261
• Biden, Joe, xix
• Bidirectional Auto‐Regressive Transformers (BART), 264
• Bidirectional Encoder Representations from Transformers (BERT), 264
• The Bill and Melinda Gates Foundation, 141, 157
• BIPOC (Black, Indigenous, and Persons of Color), 141
• Black Enterprise, xxiii, 99
• Blindspot: Hidden Biases of Good People (Banaji and Greenwald), 31
• Blind spots, xii
  • and assessments, 134
  • confirmation bias and, 72
  • identifying, 79
  • insights into, 52
  • mitigating, xv, 4–5, 25–26, 28, 238, 247
  • related to practical thinking, 42
  • via preferences, xx, 124
• Blocking, 245
• Bloomberg, 99
• Blueprint Strategy Platform, 269
• The Book of Why: The New Science of Cause and Effect (Pearl and Mackenzie), 246, 256
• Boon, Edward, 176, 179
• Boston, Mass., 144
• Boston Consulting Group, xv
• Brinkerhoff, Robert, 175, 176, 179
• Bristol Myers Squibb, 141, 157
• B.R.O.A.D., 136
• Brown, Vincent, 48, 180
• Business linkage, 176
• Byrd, Damita, 98

• Careers & the disABLED Top Employers, 99
• Carnegie Museums of Pittsburgh Innovation Studio, 235
• Caroll, Lewis, 134
• Casey, Mary Ann, 106
• Causal logic model, 257–258, 258f
• Causation, 243–246
• Centers for Disease Control and Prevention (CDC), 144
• Centre for Global Inclusion, 99
• CEO Action for Diversity & Inclusion™, 164
• CEO Action for Racial Equity, 164
• Change architecture, 170
• Change Experience Platform (CxP), xxiii, 269
• Chicago, IL, 144
• Christians, xv, 2, 36
• Churchill, Winston, 248
• Citi, 141, 155
• Clear, James, 147
• Cleveland, Ohio, 144
• Closed‐ended data, 228, 232
• Closed‐ended questions, 91
• Co:census, 124, 269
• Coding, 92, 107, 120–121, 122f, 230, 265
• Cognitive skills, xvii, 5
• Color‐coded phrases, 23t, 230–232
• Community Alliances and Consumer Engagement, xvi
• Competence continuum, 44–45, 44f, 48–52, 50f, 52f
• Competences, 27
  • 242, 176
  • antiracist, 51, 242
  • assessing, xiii, 24, 52–53, 64, 74, 91, 237–240, 242
  • attitudes/attributes, 26
  • building, 169
  • as characteristics of GDEIB, 100
  • collecting data for, 124, 237–238
  • core, 65, 65f, 67, 170–171, 175, 179, 190
  • definition of, 5
  • embedding, 156
  • increasing, 247, 252
  • intercultural, 5–6, 48–51, 68, 145, 252, 263
  • inventorying, 79
  • and inventory preferences, 80–82
  • and jaggedness principle, 269
  • knowledge, 26
  • measuring, 144–145, 148, 170, 194
  • personal, xx, 6, 53, 162–163, 176, 177, 179–180
  • preferences vs., 27, 72
  • quantitative data of, 124
  • skills, 26
  • understanding, 25–27
• Compliance, 12, 100, 235
• C.O.M.P.T., 26
• Concrete actions, 99
• Confidence levels, see Sampling error
• Connected Understanding, 47, 171, 179, 190
• Context Principle, 269
• Converge, 267
• Corporate Inclusion Index (Hispanic Association on Corporate Responsibility), 99
• Correlation, 243–246, 256
• Covey, Stephen, 7
• Crawl‐Walk‐Run Personal Action Plan, xxv, 135, 168, 177, 180, 195
• The Creative Brain (Hermann), 38
• CRM, see Customer relationship management
• Cross‐tabulations, 102–103, 104t, 105t, 106
• Crotonville, NY, 38
• Customer relationship management (CRM), 93, 224, 258, 268–269
• CxP (Change Experience Platform), xxiii, 269

• D'Angelo, Anthony J., 160
• “The Danger of a Single Story”, xvii, 247–248
• Dashboards, 214, 247. See also Scorecards
  • creating, 223–233
  • developing, 233–236
  • organizational, 215–223, 220t–222t
  • personal, 214–215
  • scorecards vs., 236–237, 236t
• Data analysis and reporting, 82, 130
• Data disaggregation, 82, 130, 223
• Data‐driven DEI:
  • definition of, xx–xxi
  • five‐step cycle of, xxiii–xxv, xxivf, 20, 22
  • future of, 255–270
  • impact, 1f
  • imperatives of, 133f
  • incentives, 1f
  • initiatives, 167f
  • insights, 159f
  • inventory for people, 23f, 125
• Data stories and storytelling, 233
  • components of crafting, 250–253
  • definition of, 248–249
  • elements of, 249–250, 250f
  • power of, 247–248
• Debrief, debriefing, 42, 51, 79, 107, 109, 115–120. See also Moderating
• Deep learning, 263–267
• DeepMind, 264
• DEI, see Diversity, Equity, and Inclusion
• DEIA (Diversity, Equity, Inclusion, and Accessibility), xix, 17
• DEI aims, 19f
  • DEI objectives vs., 18–20
  • in focus groups, 107
  • organizational, 18, 19t, 76t
  • and organizational DEI assessments, 75–79
  • personal, 8–9, 9f, 18, 20f, 24, 64, 67, 68f, 138, 171
• DEI assessment:
  • organizational, 22
  • personal, 24
• DEI champions, xx, xxii, 10, 45, 109, 252–253
• DEI councils, xx, 17–18, 125, 136–137, 149, 181–182, 194–195
• DEI Disability Equity Index™, 99
• DEI impact, 205–253, 268
• DEI imperatives, xxiv, 133–158, 181–182, 189–190, 268
• DEI incentives, 1–22
• DEI initiatives, 167–203, 268
• DEI insights, 159–163, 171, 176, 181, 182, 190, 268
• DEI Inventory, 24, 74, 132
• DEI Inventory for Organizations, 74
• DEI Inventory for People, 24, 74–75
  • explanation of, xxiv, 22, 23–72
  • and impact of DEI, 237–238
  • inventorying preferences using, 79
  • mitigating bias using, 130–131
  • and personal DEI strategy framework, 169, 189
• DEI Workforce and Workplace Assessment™ (DWWA™), 93, 190, 242
• Deloitte, xv
• Demings, W. Edwards, xiv
• Denial, 48–49
• Descriptive data, xxi
• Developmental Model of Intercultural Sensitivity, 48–49
• Development orientation (DO), 49–50
• Diagnostic data, xxi–xxiv
• Dignity, xix, 6, 100, 248
• Dimensions:
  • external, 4–5, 10, 12, 82
  • internal, 2, 82, 102, 223
  • organizational, 14, 82, 102–103, 145, 223
• Disability Equality Index (Disability:IN), 99
• Disparate impact, 143–144
• Disparities, 31, 143–144, 164, 265. See also Inequities
• Display logic, 94
• DistilBERT, 264
• Diverse 360° assessments, 53, 145, 148–149, 190, 208, 238
• Diverse Intelligence Series (DIS) Report (2019), xvi
• Diversity:
  • cognitive, xvii, 36, 38
  • community, 223
  • of cultures, xv, 49, 94
  • definition of, xviii
  • four layers of, 81, 81f, 102–103, 145, 223
  • metrics for, 144, 147
  • of people, xiv–xv
  • of thought, xv, xvii, 16
• Diversity, Equity, and Inclusion (DEI):
  • definition of, xvi
  • in mission and vision statements, 15–17
  • organizational case for, xvi
  • personal case for, xvi–xviii
• Diversity, Equity, Inclusion, and Accessibility (DEIA), xix, 17
• DiversityInc, 99, 130
• “Diversity is a Lagging Measure of Inclusion” (Russell), 147
• Diversity wheel, 81
• DO (Development orientation), 49–50
• Doshi, Tulsee, 266
• DWWA™, see DEI Workforce and Workplace Assessment™

• eLearning, 177
• Emotional Event/Relationship, Significant, 47, 171, 179, 190, 238
• Empathy:
  • cognitive, 47
  • compassionate, 47
  • emotional, 47, 171
  • Respectful, 46, 171, 179, 190
• Employee resource groups (ERGs), 15, 96, 108, 133, 161, 180, 256
• Emprising™, 94, 269
• The End of Average: How We Succeed in a World That Values Sameness (Rose), 269
• Engstrom, Ted, 4
• Enterprise resource planning (ERP), 148, 195, 224, 258, 268–269
• Equitable Analytics™, xxii, 256f, 259f, 260f, 268–269
  • and algorithmic bias, 261–262
  • and DEI, 256–263
  • and equity assessments, 260–261, 261f
  • precision modeling with, 259
  • and selection bias, 131
  • using results from, 262–263
  • as a “what works” model, 245–256
• Equitable Impact Platform™ (EquIP™), xxii, 223, 229f, 256–257
• Equity assessments, 149, 260–261
• ERGs, see Employee resource groups
• Ernst & Young, xxiii
• ERP, see Enterprise resource planning
• European Journal of Social Psychology, 176, 239
• Evaluative data, xxi
• Everett, LaToya, 48
• Executive Order 14035, xix

• Facebook, 141, 157
• Family Medical Leave Act (FMLA), 97
• “Fast Forward: A New Framework for Rapid Organization Change” (Murray and Richardson), 240
• Fazio, Russell, 46
• The Fifteen Percent Pledge, 164
• The Fifth Discipline (Senge), 10
• First Things First (Covey), 7
• Fishbowl, 267
• Five P's, 117
• FMLA (Family Medical Leave Act), 97
• Focus group data, 124–125
• Focus group questioning route, 108–110, 111t, 112t
• Focus Groups: A Practical Guide for Applied Research (Krueger and Casey), 106
• Forbes, xvi, xxiii, 249
• Ford Foundation, 17
• “From SMART to SMARTIE: How to Embed Inclusion and Equity in Your Goals” (blog post), 142

• Gardenswartz and Rowe, 81
• Gating mechanisms, 46
• GDEIB, see Global Diversity, Equity & Inclusion Benchmarks
• GE (General Electric), 38–39
• Gender‐Equality Index (Bloomberg), 99
• Gender‐Science IAT, 32, 34, 36–38, 148, 189–190
• General Electric (GE), 38–39
• Generating findings, 206, 246–247
• Generative Pre‐Trained Transformer (GPT), 264–265
• Gestalt psychology, 51
• Glassdoor, 267
• Global Diversity, Equity & Inclusion Benchmarks (GDEIB), xxii–xxiii, 99–102, 100f, 102, 130, 145, 190
  • five levels of, 101t
  • four groups and the 15 categories of, 101t, 125, 136–137, 149, 181–182, 195, 199
  • mapping, 182t
  • for organizations, 145
• GlobeSmart, 269
• Goal statements, 147–149
• Google, 266
• Google Forms, 94
• Google Meet, 120
• GPT (Generative Pre‐Trained Transformer), 264–265
• GPT‐J, 264
• GPT‐NeoX, 264
• Grace, Cheryl, xvi
• Greenwald, Anthony, 31
• Grundy, Dallas, xxii

• Hammer, Mitchell R., 49, 71
• HBDI^®, see Herrmann Brain Dominance Instrument^®
• HBDI^® Pair Profile, 80
• HBDI^® Profile, 24, 39, 41f, 43f, 145
• Head‐Hands‐Heart, 170, 177, 179–180, 195
• Healthline, 176
• Hendricks, Astrid, 48
• Herrmann Brain Dominance Instrument^® (HBDI^®), xxii, 25, 27, 42–43, 238
  • assessing preferences and competences using, 52–53
  • and confirmation bias, 72
  • measuring DEI using, 194
  • measuring diversity with, 144–145
  • two‐dimensional preference map of, 41f, 43f
  • and Whole Brain^® Thinking model, 38–39
• Hewlett‐Packard Enterprise, 141, 156
• Hibbert, Lawrence, xxii
• High Performance Learning Journey (HPLJ), xxii, 176
• Hispanic Association on Corporate Responsibility, 99
• Homophily, xv
• “How Long Does It Take for a New Behavior to Become Automatic” (Healthline article), 176
• “How Parroting is Used in Therapy: An Effective Conversational Technique” (blog post), 118
• HPLJ (High Performance Learning Journey), xxii, 176
• HRIS, see Human resources information system
• Hudon, François, xvii
• Human resources information system (HRIS), 195, 224, 242, 258, 268–269
• Human Rights Campaign Corporate Equality Index™, 99
• Hunt, David, 93

• I3™, see Intrinsic Inclusion Inventory™
• ICS^® (Intercultural Conflict Style Inventory^®), 27
• ICS Inventory, LLC, 71
• IDC™ (Intercultural Development Continuum™), 48–49, 252
• IDEA, xix, 137
• Identity wheel, 81
• IDI^®, see Intercultural Development Inventory^®
• IDI, LLC, 49
• IDI^® Group Profile Report, 80
• IDIs, see In‐depth interviews
• IFDHE (Institute for Diversity and Health Equity), 164
• Implicit Association Test (IAT), 27, 36, 43, 80. See also Gender‐Science IAT; Race IAT
  • assessing bias using, 72, 148
  • assessing preferences using, 29, 31–34, 52–53
  • and DEI inventory, 171
  • measuring diversity using, 144–145
  • one‐dimensional preference scale using, 35f
  • two‐dimensional preference scale using, 37f
• Improving Performance Through Learning: A Practical Guide for Designing High Performance Learning Journeys (Brinkerhoff, Apking, and Boon), 176
• Inclusion:
  • creating, 45
  • definition of, xviii
  • increasing, 242, 256, 263
  • intrinsic, 46, 46f
  • measuring, 144–147
• Inclusion accelerators, 46–48, 47f, 171, 179–180
• Inclusion Habit™, xxii, 269
• Inclusion index, 219
• Inclusive Leader, Six Signature Traits of an, 175
• In‐depth interviews (IDIs), 103
  • collecting data using, 106–107
  • combining with audit process, 97
  • combining with DWWAs, 94, 265
  • and DEI inventory, 206, 239
  • determining impact with, 237
• Inequities, 143–144, 251, 265, 268. See also Disparities
• Institute for Diversity and Health Equity (IFDHE), 164
• Integrated platforms, 268–269
• Intercultural competence, 5–6, 49–51, 64, 145, 252, 263
• Intercultural Conflict Style Inventory^® (ICS^®), 27
• Intercultural Development Continuum™ (IDC™), 48–49, 252
• Intercultural Development Inventory^® (IDI^®), 194, 243
  • assessing preferences using, 52–53
  • and competence continuum, 50f
  • explanation of, 49–51
  • increasing score of, 252
  • and intercultural competence, 48–51
  • measuring diversity using, 144–145
  • and personal competences, 27
• Internal previews, 118
• Internal summaries, 118
• Interview data, 125
• Intrinsic Inclusion™, xx, xxii, 179–180, 208, 269
  • characteristics of, 45, 47f
  • and DEI insights, 190, 193
  • as model for personal DEI core competences, 171
  • M.O.D.E model of, 46f
• Intrinsic Inclusion: Rebooting Your Biased Brain (Reid and Brown), 45, 180
• Intrinsic Inclusion Inventory™ (I3™), xxii, 45, 146, 149
  • assessing competencies with, 80, 144
  • assessing preferences with, 52–53
  • explanation of, 48
  • measuring DEI using, 194
  • strategies using, 189–190
• Intrinsic Inclusion Inventory™ for Organizations, 104, 190
• Inventory, See under DEI Inventory headings
• Ivey Business Journal, 240

• Jaggedness Principle, 269
• JEDI, xix, 137
• Johnson, Steven, 266
• Johnson & Johnson, 16

• Kanarys, 269
• Key performance indicators (KPIs), xiv, 157
  • comparisons using, 243
  • and dashboards, 214–215
  • and data storytelling, 249
  • and DEI impact, xxv
  • evaluating goals using, 135, 168
  • generating outcomes using, 247
  • and integrated platforms, 268
  • measures and metrics vs., 144–149, 194
  • and oversight, xxi
  • and scorecards, 207
• King, Martin Luther, Jr., 179
• Kite‐shaped figures, 42
• Klipfolio, 144
• Knowledge, skills, and attitudes/attributes (KSAs), 5, 26–27, 44, 51, 170, 247, 269
• Korn Ferry, xviii
• KPIs, see Key performance indicators
• Krueger, Richard, 106
• KSAs, see Knowledge, skills, and attitudes/attributes
• Kulka, Lisa, 48

• Lagging indicators, 143, 146–149, 157. See also Leading indicators
• Large language models (LLMs), 264–266
• Latent Dirichlet Allocation (LDA), 265
• Lattice, 92
• LDA (Latent Dirichlet Allocation), 265
• Leadership involvement, 99, 125
• Leading indicators, 143, 146–149, 247. See also Lagging indicators
• Learn‐Do‐Inspire methodology, 170, 177, 179–180, 195
• Learning management systems (LMS), 195, 224, 258, 268–269
• “Lessons from the Cheshire Cat”, 134
• LifeLabs, 92
• Likert scale, 91, 102
• LLMs (Large language models), 264–265
• LMS, see Learning management systems
• Longitudinal data, 102

• McDonald's, 138, 149
• Machine learning (ML), xxii, 92
• Mackenzie, Dana, 246
• McKinsey, xvi
• MacLean, Paul, 39
• Manage HR Magazine, xxiii
• Management practices, xx, 12, 22, 74, 96, 125, 161
• Margin of error, see Sampling error
• Marr, Bernard, 146
• Mathison, 269
• MAXQDA, 124
• Measures, see Key performance indicators (KPIs)
• Medium, 147
• Melford, Patricia, 48
• Mentimeter, 94
• Metrics, see Key performance indicators (KPIs)
• Microcommitments, xxii–xxiii, 269, 277
• Microsoft, 138, 155
• Microsoft Forms, 94
• Microsoft Teams, 120
• MilitaryTimes, 99
• Minimization, 48–49, 243, 252
• Mission and vision statements, 7, 15–18
• MIT Media Laboratory, Epistemology and Learning Group, xxii
• MIT School of Engineering, xxii
• MIT Sloan School of Management, xxii
• ML (Machine Learning), 92
• MODE (motivation and opportunity and be determinants of spontaneous behavior), 180
• M.O.D.E model, 46, 46f
• Moderating, 107, 109, 115–120. See also Debrief (debriefing)
• Modes of thinking, 42
• Molefi, Nene, 99
• Moten, Andera, 98
• Motivation and opportunity and be determinants of spontaneous behavior (MODE), 180
• Murray, Elspeth, 240

• National Basketball Association, 138
• National Heart, Lung and Blood Institute (NHLBI), 140
• National Institutes of Health (NIH), 139
• Natural language processing (NLP), xxii, 92, 124, 263–264, 267
• Natural language understanding (NLU), 92, 264–276
• Neilsen, xvi
• New York, N.Y., 92, 144
• New York Times Magazine, 265
• NHLBI (National Heart, Lung and Blood Institute), 139
• NIH (National Institutes of Health), 139
• Nike, 139, 154
• NLP, see Natural language processing
• Nosek, Brian, 31
• Null hypotheses, 104–105
• NVivo, 124

• Objectives:
  • and DEI goals, 193
  • establishing, 136–137
  • strategies for, 189–190
• Objectives and Key Results (OKR), 135
• Objective statements:
  • bridging group, 130, 141, 156, 190
  • and DEI goals, 143, 147–150
  • examples of, 139–141
  • external group, 130, 141, 157
  • formatting, 137
  • foundation group objectives, 130, 138–140, 149–155
  • internal group, 130, 140, 155
• Observational studies, 246
• OGSM (objectives, goals, strategies, and measures) strategic planning framework, 135, 135f, 157, 168, 195
• Ohio State University, xvii
• Ohno, Taiichi, 160
• OKR (Objectives and Key Results), 135
• Olsen, Michael, 46
• O'Mara, Julie, 99
• #123forEquity pledge, 164
• OpenAI, 264
• Open‐ended data, 228, 232
• OpenSesame, 269
• Organizational DEI assessment framework, 20–22, 21f, 74–79
  • analyzing data in, 102–106, 124–129, 242–243
  • delivering, 124–129
  • designing, 91–93
  • evaluating, 241
  • example of, 93–96
  • guidelines for, 241t
  • outline for, 130f
  • reporting, 181
  • and selection bias, 131–132
  • sharing, 124
  • tools for, 84–91
• Organizational DEI charters, 17–18, 131–132, 136
• Organizational DEI framework, 17–18, 136, 181
• Organizational extrinsic factors, 12, 14
• Organizational intrinsic incentives, 14
• Organizational policies, xx, 22
  • in assessment frameworks, 74
  • barriers to DEI in, 98
  • definition of, 12
  • and HR policies, 96
  • and management practices, 125
  • and “What Works” model, 161
• Organizational structure, 99, 125
• Organizational transformation, 11f
  • in DEI integrated platforms, 269
  • dimensions of, 2, 9–12, 20, 74
  • personal transformation vs., 12–14
  • steps toward, 240–241
• Organization development, 100
• Organizations, DEI Inventory for, 74
• Orientation gap, 49, 145
• Otter.ai, 120
• Outcomes, xxi
  • beliefs' effect on, 4
  • and causal logic models, 257–258
  • disparities in, 143–146
  • equitable, 7, 18, 91, 107, 251–257, 268–269
  • and Equitable Analytics™, 263
  • evaluating results using, xxi–xxv
  • and goal setting, 142
  • impact of, 237, 243–247
  • indicators of, 146–147
  • learning, xvii, 176–177
  • negative, 25
  • outputs vs., 137, 137t, 147
  • performance, xvii, 175–176
  • and Personal DEI Learning Journeys, 175
• Outputs:
  • definition of, 147
  • gauging progress using, xxi, xxv
  • outcomes vs., 137, 137t, 147
• Oxford University, xxii

• Parrot (parroting), 118
• Pathways principle, 269
• Pauses (pausing), 45, 109, 117
• PC&EM (Psychological Climate and Effort Measures), 92
• PDLJ, see Personal DEI Learning Journey
• Pearl, Judea, 246, 256
• People, DEI Inventory for, 24, 74–75
• Perceived Orientation (PO), 49–50
• Performance data, xxi
• Personal DEI Assessment Framework, 20–22, 21f, 24, 24f, 241–242, 241f
  • analyzing results of, 175, 189
  • choosing tools for, 64–71
  • conducting, 27–52
  • evaluating, 241–246
  • frequency guidelines for, 241t
  • identifying tools for, 53–63
  • re‐administering, 237–241
• Personal DEI Learning Journey (PDLJ):
  • The crawl, walk, run approach to, 179–180
  • designing, 175–177
  • examples of, 177–179
  • maximizing, 240
• Personal DEI Strategy Framework, 169
• Personal extrinsic incentives, 6
• Personal intrinsic factors, 4, 5–6
• Personality, xx, 45, 51
  • and blind spots, 26
  • definition of, 5
• Personal transformation:
  • in DEI integrated platforms, 269
  • dimensions of, 2, 12–14, 20
  • organizational transformation vs., 12–14
• Pew Research Center, xv
• Philadelphia, PA, 144
• Pivots (pivoting), 118
• Plummer, Deborah L., 51
• PO (Perceived Orientation), 49–50
• Polarization, 48–49
• Pollfish, 94
• PopIn, 267
• Population, xxii, 106, 114, 219
  • eight P's of, 250
  • middle‐class, xiv–xv
  • proportion, 83–84
  • sampling, 239
  • and selection bias, 131
  • size of, 83
• Potential barriers, to DEI, 98
• Potential solutions, of DEI, 98
• Power of the Pause, 46
• Precision Analytics, see Equitable Analytics™
• Precision Modeling, 256–260
• Predictive data, xxi
• Preference codes, 41–42
• Preference maps:
  • examples of, 36–44
  • preference scales vs., 32–34
  • two‐dimensional, 34–35, 35f, 37f, 38–39, 41f, 43f
  • understanding, 34–35
  • value of, 42
• Preferences:
  • assessing, 28–29, 52, 240, 246–247
  • collecting data for, 124, 237–238
  • competences vs., 27, 43–44, 72
  • core, 67
  • definition of, 6
  • expanding, 169, 170–171, 176, 177, 180
  • increasing use of, 164
  • inventorying, 79
  • and jaggedness principle, 269
  • learned, 25
  • measuring, 25, 71, 145
  • neutral, 25
  • personal, xx, 160–161, 171–175, 190, 242, 267
  • unconscious bias and, 29–32
  • understanding, 25–26
• Preference scales:
  • competence continuum vs., 44
  • and diversity measures, 144
  • example of, 31–32
  • one‐dimensional, 29f, 38
  • preference maps vs., 32–34
  • two‐dimensional, 34
  • understanding, 28–29
• Prescriptive data, xxi
• Princeton University, xvii–xviii
• Probability value, 104–105
• Probes (probing), xiv, 117
• Proceedings of the National Academy of Sciences of the United States of America (PNAS), 31
• Profiles, xxi, xxiv–xxv, 24, 171, 238, 241
  • team, 80
  • triple dominant, 42
• Profile scores, 41–42, 72
• Project Implicit, 31–32, 34, 38, 171
• Promising practices, xxi, 98
• Psychological Climate and Effort Measures (PC&EM), 92
• Psychological safety, 10, 92, 115
• Psychology Today, 180
• Pulse surveys, 239
• p‐values, 104–105, 245

• Qualitative data analysis, 91–92, 120–124, 165
• Qualitative data collection, 106–120
• Qualtrics, 94
• Quantitative data analysis, 102–106
  • definition of, 80
  • in findings, 247
  • with mixed‐methods integrated data, 124–125
  • on scorecard/dashboard, 225
  • and subgroups, 81–82
  • with surveys, 91–92
  • and visualization, 225, 232
• QuestionPro, 94
• Questions:
  • “all things considered”, 110, 119
  • closed‐ended, 91
  • ending, 110, 119
  • final, 110, 119
  • key, 95, 109–110
  • opening, 109
  • summary, 110, 119
  • transition, 109

• Race IAT, 32, 36–38, 189–190
• Rali, xxii, 170, 193, 269
• Randomized controlled trials (RCT), 131, 244
• Ranks (ranking), 98–102. See also Benchmarks (benchmarking)
  • in assessment reports, 74
  • and performance, 125
  • using cross‐tabulations for, 102–103
• RCT (randomized controlled trials), 131, 244
• Readiness gap survey (2021), xv
• Reid, Janet, 45–46, 48, 180
• Reliability, 39, 64, 84
• Representation numbers, 147
• Resources, DEI, 98
• Respectful Empathy, 46, 171, 179, 190
• Reuters, 71, 233
• Reuters Diversity & Inclusion Index, 155
• Rev.com, 120
• Rhodes Scholars, xxii
• Richardson, Peter, 240
• Richter, Alan, 99
• Robinson, Jeffrey, xxii
• Roosevelt, Eleanor, 160
• Root causes, of problems, 98, 251–252
• Rose, Todd, 269
• Royal Bank of Canada, 141, 157
• Russell, Lisa, 147
• Rutgers University, 141

• Sample size, 80, 83–84, 235–236
• Sampling error, 83–84
• Sarwana, Miriam, 48
• SAS, 106, 245
• Saturation, 113
• Schwedt, Ashley, 92–93
• Science, technology, engineering, and math (STEM), 148, 190
• Scorecards, 214, 247. See also Dashboards
  • and benchmarks, 99
  • creating, 223–233
  • dashboards vs., 236–237, 236t
  • definition of, 207–208
  • developing, 233–236
  • managing, 268
  • organizational, 211–212, 212t–213t
  • personal, 208, 209t
• Senge, Peter, 10
• “7 Tips for Conducting Your Next DEI Survey” (Test), 92
• SFR (Structured free recall), 72, 170
• Shafak, Elif, xv
• Shared Trust, 46, 171, 179, 190
• Significant Emotional Event/Relationship, 47, 171, 179, 190, 238
• Six Signature Traits of an Inclusive Leader, 175
• Skip logic, 94
• S.M.A.R.T., 142
• S.M.A.R.T.I.E., 141
• SMEs (subject matter experts), 182
• Social cognitive theory, 29
• Social justice, 100
• Societal trends, xiv–xv
• Sperry, Roger Walcott, 39
• SPSS, 106, 245
• Standard Chartered Bank, 141, 156
• Starbucks, 141, 156
• Stata, 106, 245
• STEM (science, technology, engineering, and math), 148, 190
• Stereotypes, xv–xvii, 31, 235
• Stories, see Data stories and storytelling
• Strategic plan scorecard, 219
• Stratified sampling plans, 83–84, 239
• Structured free recall (SFR), 72, 170
• Subgroups, 80–84, 103, 113–114
• Subject matter experts (SMEs), 182
• SUDAAN, 106, 245
• Survey data, 82, 98, 125
• Survey fatigue, 83
• SurveyGizmo, 94
• SurveyMonkey, 94, 124

• Tag clouds, see Word clouds
• Tapia, Andrés, xviii
• Taylor, Jonathan, 144, 146
• TED Talks, xvi, 247
• Test, Lyssa, 93, 129
• Theoretical knowledge, 5, 26
• Thomas, Barry, 98
• Thompson, Phylicia, 48
• ThoughtExchange, 267
• Through My Eyes™ Virtual Reality, xxii, 193, 269
• The Tiffany Circle (American Red Cross), 141, 157
• Tinberg, Donna, 134
• Tokenism, 143
• Top Companies for Diversity Index (DiversityInc), 99
• Training Industry, 177
• Transparency, 53, 118, 233, 263, 267–268
• Trint, 120
• Trust, Shared, 46, 171, 179, 190
• Twitter, 124
• Two‐dimensional preference maps, 34–35, 35f, 37f, 38–39, 41f, 43f
• Typeform, 94

• Ubiquity, see Transparency
• Understanding, Connected, 47, 171, 179, 190
• University of Akron, xvii
• University of California, Davis, 16, 141, 156
• University of Tennessee, 46
• University of Virginia, 31
• University of Washington, 31
• U.S. Department of State, 139

• Validity, 39, 64, 84
• Vanderbilt University Center for Teaching, 177
• Verizon, 140, 155
• Verywell Mind, 118
• Virtual reality (VR), xxii, 177, 269
• VMWare, 141
• VR (Virtual reality), xxii, 177, 269

• W. Edwards Demings Institute, xiv
• WesVar, 106, 245
• “What is a KPI, Metric or Measure?” (blog post), 144
• “What is a Leading and a Lagging Indicator? and Why You Need to Understand the Difference” (Marr), 146
• “What Works” models, 171, 190, 223
  • definition of, 160–161
  • and Equitable Analytics™, 256–263, 260f
  • identifying, xxi–xxv
  • Mapping GDEIB Groups to, 182t
  • for organizations, 164–166, 165t–166t
  • for people, 161–163, 162t–163t
  • and personal learning journeys, 176
• Whole Brain^® Thinking model, 38–41, 39f, 40f, 40t
• Wikipedia, 264
• Word clouds, 228–230
• Workday, 141
• Working Mother, 99
• World Vision International, 4

• York, Peter, 48, 266
• “Your Voice Counts: Diversity at Reuters” (report), 233
• Youth for Christ, 4
• Yum! Foods, 141

• Zoho Survey, 94
• Zoom, 120