Index

1. Basic Probability Theory

2. Sampling and Asymptotic Theory

3. Hypothesis Testing

4. Psychometrics and Measurement

5. Econometrics and Causal Inference

6. Sociometrics and Social Networks

7. Prediction and Discovery in Machine Learning 

8. It Takes Two to Tango

Introduction

If we miss the desire why and how we want to use statistical knowledge, mathematical equations remain in our minds just as a blurred shadow, soulless test items to memorize. This class proposes how your desire for "management" can be a motivation to learn social statistics. The class won't be easy as we will train ourselves in both statistics and business management. Nevertheless, you will eventually learn that it takes two to tango. 

The first three weeks provide a basic overview of social statistics. The class starts with an introduction to basic probability theory to show you how statistical models can provide you with toolkits to handle uncertainties in business management (Week 1). Then, it illustrates how the observed patterns can provide you with a general insight into the prediction. By understanding the sampling framework, you will get a sense of when you can believe that the past can be extrapolated to the future and when you can't do that (Week 2). We will also explore how we can formularize our business insights into simple sentences. You will see from this part how we can make data-driven decisions but at the same time, you will also see that the data does not speak for itself (Week 3). 

The next four weeks are devoted to the specific processes of social statistics which can benefit from different social scientific disciplines. The first issue is about measurement, or defining and operationalizing the construct behind observations. In specific, Psychometrics illuminates that social science (or any human-related topics including management) engages in the project of measuring things that we cannot directly measure and provides a careful approach to how we can deal with the problem of measurement (Week 4). The second issue is about causality. Econometrics pays rigorous attention to the statement: "Correlation does not imply causation". We will learn about the terms endogeneity and exogeneity. We will learn how we can design experiments or capture the quasi-experimental opportunities from observations. At the same time, I will also show you that these skills become meaningful only when we have a sound business model in our minds (Week 5). Third, we will loosen the statistical assumption of independent observation that we had in the previous approaches. This part includes generic statistical remedies like hierarchical modeling or clustered standard errors but also includes an overview of Sociometrics and when and how interdependence can be the protagonist of the statistical model (Week 6). Lastly, machine learning approaches have been highly mystified as a panacea for data-driven management recently. This class demythologizes it by providing a framework of how you can consider machine learning as a toolkit in your pouch. It will specify its two main utilities: prediction and discovery and will also specify when it works and it won't work (Week 7). 

Again, it takes two to tango! If we lose the balance between data and business insights, we may stuck with ideas that do not follow the real world or we may cling to statistics with no valid implications. The lecture ends with a pragmatic action profile of how you can enact the interdependence between data and insight. The recipe of a balanced approach will provide practical knowledge that empowers you in terms of business management (Week 8).

References

[Books]

• Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing social networks using R. Sage.

• Hansen, B. (2022). Econometrics. Princeton University Press.

• Hansen, B. (2022). Probability and Statistics for Economists. Princeton University Press.

• Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press.

• Isvoranu, A. M., Epskamp, S., Waldorp, L., & Borsboom, D. (Eds.). (2022). Network psychometrics with R: A guide for behavioral and social scientists. Routledge.

• Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis. Pearson.

• Lareau, A. (2021). Listening to people: A practical guide to interviewing, participant observation, data analysis, and writing it all up. University of Chicago Press.

• Martin, J. L. (2017). Thinking through methods: A social science primer. University of Chicago Press.

• Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. Routledge.

[Papers]

• Abadie, A., Athey, S., & Imbens, G. W. (2022). When should you adjust standard errors for clustering? The Quarterly Journal.

• Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11(1), 685–725.

• Ayers, N., King, G., Mukerjee, Z., & Skinnion, D. (2024). Statistical Intuition Without Coding (or Teachers).

• Choudhury, P., Allen, R. T., & Endres, M. G. (2021). Machine learning for pattern discovery in management research. Strategic Management Journal, 42(1), 30–57.

• Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.

• Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., ... & Yarkoni, T. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181-188.

• Imai, K., & Kim, I. S. (2021). On the use of two-way fixed effects regression models for causal inference with panel data. Political Analysis: An Annual Publication of the Methodology Section of the American Political Science Association. 

• Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334.

• Leamer, E. E. (1983). Let’s Take the Con Out of Econometrics. The American Economic Review, 73(1), 31–43.

• Lewbel, A. (2019). The Identification Zoo: Meanings of Identification in Econometrics. Journal of Economic Literature, 57(4), 835–903.

• Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What is your estimand? Defining the target quantity connects statistical evidence to theory. American Sociological Review, 86(3), 532-565.

• Snijders, T. A. (2011). Statistical models for social networks. Annual review of sociology, 37, 131-153.

• Swedberg, R. (2017). Theorizing in Sociological Research: A New Perspective, a New Departure? Annual Review of Sociology, 43(1), 189–206.