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Join Fritz Scheuren of NORC, University of Chicago for insights on US statistical practices, adaptation to technological trends, and the future of statistical analysis, embracing hindsight, insight, and foresight.
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Foresight, Insight. HindsightUS Statistical Observations Fritz Scheuren NORC University of Chicago
Reminder on Definitions • Hindsight reflecting on the past –Personally/Collectively • Insight, where it all comes together, like this Conference • Foresight future seeing or shaping, also familiar but bears discussion
A Small Statistical Corner • US Official Statistics • Censuses and Surveys • Administrative Records • Focus on lived experiences, ala Deming
Times They are a Changing • Relevance of our Discipline? • Responsiveness of Statistics? • Information Age? • Misinformation Age? • Service Partnerships?
Children’s Teaching Game? • High? • Low? • You’re Too Slow! • How Avoid Being Too Slow?
But Change is Accelerating! • Is our Discipline Keeping Up? • Certainly Computationally! • Tool/Theory Building too! • Practice Slower? • Organizational Issues?
How To Keep Up/Catch Up? • Google (of course) • Metadata Revolution -- Still more Promise than Practice • Meta-Analytic Reuse -- Still Often Too Hard
Responsiveness to Trends • How Well Do We Play? • High? • Low? • You’re Too Slow!
Response Times to Trends(organizational clockspeed = rate at which an organization introduces new products, adopts new production processes, or reorganizes itself; Sources: Charles H. Fine, 1998; David W. Rejeski, 2003)
Technological Mega-Trends • Faster and Faster Computing (Slower for Official Statistics) • Descriptive to Analytic • Randomization-based to Model-based • Producer Dominated to Customer Shared
Typical Grief Response to Change Still Often True • Denial • Anger • Bargaining • Depression • Acceptance
Examples of Hindsight, Insight, Foresight • Nonresponse Circa 1980 • US Census Taking Circa 1990 • Paradata Circa 2000 • Visualization Circa 2010 • Next Steps Together?
Nonresponse Hindsight Example
40 Year CPS Income Trend • Insignificant Missingness in 1962 • Now Nearly Half of Interviews have Some Missingness • About a Third of the Amount is Imputed • But Still using the Same Basic “Hot Deck” Methods Today
Greater Bias Concerns • Possibility of Greater Nonresponse Bias • Potentially More Income Understatement • Also Characteristics of Poor Blurred
Variance More Understated • Growing Variance Not Directly Reduced • Rubin’s Multiple Imputation Solution Still Not Used in CPS • Remains Descriptive Rather than Analytic
Paradata Modeling Insight Example
Meta-Data Revolution • Applying Computing to Documentation and Training • Including Measurement Process or Paradata • Achieving Full Systems Thinking
Unify Meta/Paradata • Bringing All Survey Aspects together electronically • Sharing with All Stakeholders • Breaking Down Barriers between Departments
Unify Meta/Paradata • Bringing All Survey Meta-Data together electronically • Sharing with All Stakeholders • Breaking Down All Barriers Between Departments
Manage System as a Whole • Not Just Conformance to Requirements Quality • But Total Fitness for Use Quality • From Sampling/Nonsampling to Total Survey Inference • Record linkage Example
Total Systems Thinking • Turning Sample “Models” • Into Full Survey “Models” • Using Paradata and Experience • Politz-Simmons Example
Visualization Foresight Example
Complex Survey Graphics • Clustering and Weighting Distort • Analytically these can be “solved” Approximately • Design Effect Example
Restoring Visual Metaphor • Inverse Sampling Algorithm • Works for Many Designs • Satisfactory Analytically • Works Graphically too but not yet Always
Simulation Alternatives • Capture Essential (Sufficient?) Conditions • Simulate Graphics Analytically • Retain Real Sample? • Apply Empirical Residuals?
Regression Diagnostics • Design-Based and Analytic Alternatives Being Examined Now • Both Have Merits • May Imbed both in an Open-Source, like R
Next Steps? Further Considerations and Examples