A Simple Method for Analyzing Books

A recent Pew Research Center study found the following:

  • Americans 18 and older read on average 17 books each year. 19% say they don’t read any books at all. Only 5% say they read more than 50.
  • Fewer Americans are reading books now than in 1978.
  • 64% of respondents said they find the books they read from recommendations from family members, friends, or co-workers.
  • The average reader of e-books read 24 books (the mean number) in the past 12 months; the average non-e-book consumer read an average of 15.

The first bullet above is pretty remarkable. Using 17 books/year with, let’s say, 40 years of reading (above the age of 18), that’s 680 books read in adulthood. That’s a lot.

This got me thinking about how we decide which books to buy and how our decisions on which books to buy adapt with each book that we read. Are we in tune with our changing desires and interests and is our feedback loop from both positive and negative reading experiences, well, accurate and efficient?

Some time ago, I began collecting data on my book reading experiences to allow me to analyze exactly that. Given the Pew study, I figure I’ll share my methodology in hopes it makes sense to someone else. Star ratings such as that on Amazon are certainly helpful, but my hope is to perfectly understand what works for me as to make my decisions on reading material accurate, efficient, and part of a lifelong journey for knowledge and inspiration.

Known Data Elements (Both Categorical and Quantitative)

  • Author
  • Type (Non-Fiction vs Fiction)
  • Genre (Thrillers/Suspense, Science/Technology, Current Affairs & Politics, etc.)
  • Number of Pages (using hardcover as a standard)
  • Date Published

Personal Data Inputs (upon book completion)

  • Date Completed
  • Tags/Notes
  • Readability, Flow, & Structure (RFS) – A score ranging from [0.0, 5.0] subjectively assigned to a book based on ease-of-read and the overall structure of the book.
  • Thought-Provoking, Engagement, & Educational Value (TEV) – A score ranging from [0.0, 5.0] subjectively assigned to a book based on how mentally stimulating it was in terms of knowledge and thought.
  • Entertainment, Suspense, & Likeability (ESL) – A score ranging from [0.0, 5.0] subjectively assigned to a book based on the entertainment value and overall likeability of the story, characters, and/or information presented.

Those three metrics (RFS, TEV, ESL) allow one to create a overall score for the book. My overall score is a simple sum of the three metrics, divided by the maximum possible score (15.0), and expressed as a percentage (ranging from 0% to 100%). Although I have not yet conducted any correlation studies or categorical analyses using my data (which I have for 42 books starting in Aug 2004), below is a snapshot. As for my next book, it’ll probably be a self-help guide to drop the data obsession. 🙂

Title Author Pages RFS [0,5] TEV [0,5] ESL [0,5] SCORE [0,100%]
A Short History of Nearly Everything Bill Bryson 560 4.5 5.0 4.5 93%
The Alchemist Paulo Coelho 208 4.5 4.5 4.5 90%
Life of Pi Yann Martel 336 4.5 4.0 4.5 87%
Moneyball: The Art of Winning an Unfair Game Michael Lewis 288 4.0 4.5 4.0 83%
Born to Be Good: The Science of a Meaningful Life Dacher Keltner 352 4.0 4.5 3.5 80%
The Tipping Point: How Little Things Can Make a Big Difference Malcolm Gladwell 288 4.0 4.0 4.0 80%
The Next 100 Years: A Forecast for the 21st Century George Friedman 272 4.0 4.5 3.5 80%
Super Freakonomics: Global Cooling, Patriotic Prostitutes, and Why Suicide Bombers Should Buy Life Insurance Steven Levitt; Stephen Dubner 288 4.0 4.0 4.0 80%
Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart Ian Ayres 272 4.0 4.0 4.0 80%
The Art of Strategy: A Game Theorist’s Guide to Success in Business & Life Avinash Dixit; Barry Nalebuff 512 4.0 4.5 3.5 80%
The Long Tail: Why the Future of Business is Selling Less of More Chris Anderson 256 4.0 4.0 3.5 77%
Outliers: The Story of Success Malcolm Gladwell 309 4.0 4.0 3.5 77%
Body of Lies David Ignatius 352 4.5 3.0 4.0 77%
A Walk in the Woods: Rediscovering America on the Appalachian Trail Bill Bryson 284 3.5 4.0 3.5 73%
Kill Alex Cross James Patterson 464 4.5 2.5 4.0 73%
The Increment David Ignatius 400 4.0 2.5 4.5 73%
A Whole New Mind: Why Right-Brainers Will Rule the Future Daniel Pink 272 4.0 4.0 3.0 73%
Blink: The Power of Thinking Without Thinking Malcolm Gladwell 288 3.5 4.0 3.0 70%
Physics of the Impossible: A Scientific Exploration into the World of Phasers, Force Fields, Teleportation, and Time Travel Michio Kaku 352 3.5 4.0 3.0 70%
The Bourne Dominion Eric van Lustbader 432 3.5 2.5 4.5 70%
Fortune’s Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street William Poundstone 400 3.0 4.0 3.5 70%
The Godfather Mario Puzo 448 3.5 2.5 4.5 70%
The Sicilian Mario Puzo 410 3.5 2.5 4.5 70%
The Invention of Air: A Story of Science, Faith, Revolution, and the Birth of America Steven Johnson 272 3.0 4.0 3.0 67%
The Drunkard’s Walk: How Randomness Rules Our Lives Leonard Mlodinow 272 3.0 3.5 3.5 67%
Cross Fire James Patterson 432 4.0 1.5 4.5 67%
The Social Animal: The Hidden Sources of Love, Character, and Achievement David Brooks 448 3.5 4.5 2.0 67%
The Golden Ratio: The Story of PHI, the World’s Most Astonishing Number Mario Livio 294 3.0 4.0 2.5 63%
Physics for Future Presidents: The Science Behind the Headlines Richard Muller 354 3.0 3.5 3.0 63%
The Future of Everything: The Science of Prediction David Orrell 464 3.0 3.5 3.0 63%
The Department of Mad Scientists Michael Belfiore 320 3.0 3.0 3.5 63%
For the President’s Eyes Only: Secret Intelligence and the American Presidency from Washington to Bush Christopher Andrew 672 3.0 3.5 3.0 63%
Born Standing Up: A Comic’s Life Steve Martin 209 4.0 2.0 3.0 60%
Science is Culture: Conversations at the New Intersection of Science + Society Adam Bly (Seed Magazine) 368 2.5 3.5 3.0 60%
1491: New Revelations of the Americas Before Columbus Charles Mann 480 2.5 3.5 2.5 57%
The Curious Incident of the Dog in the Night-Time Mark Haddon 226 3.0 3.0 2.0 53%
Group Theory in the Bedroom, and Other Mathematical Diversions Brian Hayes 288 2.0 3.5 2.0 50%
Euclid in the Rainforest: Discovering Universal Truth in Logic and Math Joseph Mazur 352 2.0 3.0 2.5 50%
This is Your Brain on Music: The Science of a Human Obsession Daniel Levitin 320 2.5 3.0 1.5 47%

Principles of Forecasting

I just finished reading a couple books about future studies and the nature of predictions and forecasts: (1) Future Savvy, by Adam Gordon and (2) The Future of Everything, by David Orrell. From the former of the two, I wanted to pull a good portion of the content from Chapter 11 and structure it here for use in future posts and projects. In Chapter 11 of his book, Gordon outlines the important questions to ask of any forecast. As decision makers and leaders, analysts and synthesizers, and organizations and citizens, it’s critical that we learn to properly evaluate and filter statements about the future so that we can optimize our decisions and, ultimately, our positioning for the future.

With that as a quick intro, here are the questions we should ask of any prediction or forecast. As Gordon states of forecasts: “they are not in themselves valuable, they are only valuable alongside a clear way to separate the wheat from the chaff”.

Purpose

  • What is the purpose of the forecast? Is the forecast upfront about its purpose?
  • Is the forecast future-aligning or future-influencing?
    • Is the forecast widely publicized?
    • Does it specify action to take in the external world?
    • Is it a forecast of extremes?

Specificity

  • Is he forecast mode predictive – spelling out what will happen – or speculative, illuminating possible alternatives?
  • Is there too much certainty?
  • Is there enough certainty? Is the forecast hedging?
  • Is the forecast clear about the pace of change? Does it specify timelines or does it leave the question hazy?

Information Quality

  • How extensive and how good is the base data?
    • Is the data up to date?
    • Does the forecast use secondary data?
    • Is the data real or a projection?

Interpretation and Bias

  • Are the forecast’s biases natural or intentional?
  • What is the reputation of the forecaster and forecast organization? Does the forecaster have anything to lose by being wrong?
  • Are bias-prone contexts at hand?
    • Is the forecast sponsored?
    • Is self-interest prominent?
    • Are ideology and idealism prominent?
    • Does the forecast focus on a “single issue” future?
    • Is editorial oversight bypassed?

Methods and Models

  • Does the forecast specify its methods?
  • Does the forecaster imply the method is too complex, too arcane, or too proprietary to share?
  • Do forecast proponents trumpet their unique or “new and improved” methods?

Quantitative Limits

  • Is the use of quantitative methods appropriate?
  • Is a machine doing the thinking?

Managing Complexity

  • Does the forecast oversimplify the world?
  • Does the forecast acknowledge systemic feedback?
  • Does the forecast anticipate things that could speed up the future, or push it off track? Does it account for triggers and tipping points?
  • Does the forecast expect exponential change?

Assumptions and Paradigm Paralysis

  • Has adequate horizon scanning been done?
  • Are the assumptions stated? Is the forecaster aware of his or her own assumptions? Is the forecaster willing to entertain alternative assumptions?
  • Do the forecaster’s assumptions appear valid and reasonable?

Zeitgeist and Groupthink

  • Is the zeitgeist speaking through the forecaster?
  • Is the forecast jumping on the bandwagon?
  • Does the forecast rely on “experts”?
  • Does the forecast do stretch thinking? Does it allow us to break free from the “official future”?

Drivers and Blockers

  • Are change drivers and enablers identified? Or are trends simply projected?
  • Are blocking forces identified and fully accounted for? Is friction factored in?
    • Have utility questions been asked and adequately answered?
    • Are there proposing or opposing stakeholders, particularly powerful individuals and powerful organizations?
    • Does the forecast challenge social, cultural, or moral norms?
    • Whose side is the law on?
    • Is the forecaster in love with the technology?
    • Does the forecast underestimate the time to product emergence? Does it overestimate the pace at which people’s habits change?
    • Does the forecaster assume change? Does the forecast underestimate the full hump change must overcome? Does the forecaster recognize what doesn’t change?