January 12, 2017

New book now out: Information Quality

I'm excited to announce that our book Information Quality - The Potential of Analytics to Generate Knowledge (with Ron Kenett) is finally out just in time for the new year. The book introduces the Information Quality (InfoQ) framework, which is useful for evaluating the potential or usefulness of a dataset for answering a specific question or goal, given the use of data analysis (statistical modeling, data mining, etc.). It is also useful for evaluating studies that use data analysis.

A bit on the history of InfoQ:

  • Ron and I started thinking and discussing the topic more than 10 years ago
  • In 2010 we introduced the framework in our paper On Information Quality (JRSS-A vol 177(1), pp. 3-38, with 6 discussion papers and rejoinder)
  • In 2013 I presented InfoQ in a 30-min webinar by the Royal Statistical Society journal club.
  • We published papers applying the InfoQ framework to different domains: reviewing empirical articles (Helping Reviewers Ask the Right Questions: The InfoQ Framework for Reviewing Applied Research), official statistics (From Quality to Information Quality in Official Statistics, Journal of Official Statistics, vol 32 no 4, pp. 1–19),
  • We used the InfoQ dimension of Generalization to discuss Reproducibility, Replicability, and Repeatability (Clarifying the terminology that describes scientific reproducibility, Nature Methods, Vol. 12(8), p 699, August 2015).
  • I gave the opening keynote “Information Quality: Can Your Data Do the Job?” at the 11th Statistical Challenges in eCommerce Research (SCECR) Symposium, Addis Ababa, Ethiopia, June 2015.

    The book has three parts: (1) the InfoQ framework, (2) application of InfoQ in different fields (education, healthcare, customer surveys, and more), and (3) Implementing InfoQ in software (with a special JMP add-on).

    Where now?
    Above is InfoQ 101. There's much more going on! See more publications and talks on the InfoQ website and follow news on the FB page.

November 20, 2016

Keynote at Israeli Conference on Mechanical Engineering at Technion

On Wednesday morning (Nov 23), I'll be giving a keynote talk at the 2016 Israeli Conference on Mechanical Engineering with the title "Research Using Behavioral Big Data: A Tour and Why Mechanical Engineers Should Care". This is the first time I'll be presenting to an audience of mechanical engineers and I see it as an important opportunity to foster collaborations between the designers and creators of "things" and those using the data generated by the "things". Mechanical engineering is also embracing the era of big data and IoT - the theme of the conference is "Mechanical Engineering in the Internet of Things and Big Data Era". We're experiencing the convergence of engineering, data analytics, and the social sciences; it's a good idea to figure out the landscape!

When and Where: Technion (Haifa, Israel) Churchill Building, Wed 23/11, ~10am.

November 18, 2016

Paper on trees for addressing self-selection in impact studies now published in MIS Quarterly

Many studies use quasi-experiments, which are similar to randomized experiments except that subjects are not randomly assigned to the treatment and control groups. The result is what's called "self-selection bias", which requires special analysis correction for valid inference about the treatment effect. In a joint paper with Deepa Mani (ISB) and Inbal Yahav (Bar-ilan U) we propose a new method that is based on classification and regression trees: "A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data", MIS Quarterly, vol 40 no 4, pp. 819-848. Useful also for randomized experiments and observational impact studies.

Check out the slide deck for a quick walk-through.

November 3, 2016

Interview on Analytically Speaking

Next week I'll be headed to SAS headquarters in Cary, NC for an interview on Analytically Speaking. I look forward to Anne Milley's thought-provoking questions! The announcement promises to walk through several of my research areas:

In her highly acclaimed paper, To Explain or to Predict?, Galit Shmueli writes “statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description.” But while it is common to conflate explanation and prediction, understanding the distinction is crucial.
On the table for discussion:
Four important tensions between explaining and predicting.
Information quality.
Hacking data mining for causality.
How to spark student curiosity in statistics.
What is behavioral big data?

Expertise: explanatory modeling, causality, predictive modeling, predictive power, statistical strategy, data mining, scientific research

The interview will be recorded. You can register for the free webcast.

November 3, 2016

Talk on "Research with Behavioral Big Data" in Kaohsiung, Taiwan (Nov 4, 2016)

I'm heading south to Kaohsiung's National Sun Yat-sen University to give a talk about "Research Using Behavioral Big Data" at the College of Management, MIS department, tomorrow (Friday, Nov 4, 12:30pm). For more information on what, when, and where, see the announcement page.