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35#

common features in great works of art (e.g. eastern promises):

 1. icc is metaphor for the patriachal-hierarchical society such as russian mafia

 2. our protagonists tattooes catalog his journey (intercept & slope)

 3. this shibboleth will protect you or have your throat slit

     This craving for community of worship
     Chief misery of every man
     Individually &
     Of all Humanity
     From the beginning of time

but this isn’t all that’s common: watch headhunters, and generalize (see #43 below)

36#

  1. epistemolgy

  2. time: intercept, slope

  3. identity (no deus ex machina, please!)

37#

identity -> variance/explanation -> outcomes

  • causal inference deals with identity -> outcomes

    • reflects influence of epidemiology

    • thus association is not causation dictum

    • black box is always present

  • but the hierarchial approach is more robust

    • reflects influence of statistics

    • actually explains variance in outcomes

    • no black box, which is seen with epidemiology

  • rct’s are the gold standard for causal inference

  • but they are expensive and time consuming

  • so we use observational studies

  • but they are prone to confounding

  • so we use propensity score matching

  • but they are prone to selection bias

  • so we use instrumental variables

  • but they are prone to endogeneity

  • so we use structural equation modeling

  • but they are prone to omitted variable bias

  • so we use bayesian networks

  • but they are prone to overfitting

  • so we use regularization

  • but they are prone to bias-variance tradeoff

  • so we use ensemble methods

  • but they are prone to overfitting

  • so we use bagging

  • but they are prone to overfitting

  • so we use boosting

  • but they are prone to overfitting

  • so we use stacking

  • but…

38#

  • bias-variance tradeoff is a fundamental tradeoff in machine learning

  • one is the error due to the model’s inability to capture the true relationship

  • variance is the error due to the model’s sensitivity to small fluctuations in the training set (ie its modelling the noise)

39#

  • hierarchical-patriachical approach to data analysis

  • why is it so important to understand the data generating process?

  • because we can structure the variance in the data & thus explain the outcomes

40#

  • process (hierarchical) vs. variance (assuming icc = 0)

  • fully sociological process (hierarchical) vs. fully biological process (variance)

    • will-to-power, domination, control (hierarchical: intra-class correlation)

    • biology, freedom, autonomy (intra-class variance)

    • also: power/bias/control vs. weakness/variance/explanation

  • explains why some folks intuitive “hate” apple products

Ah, if you’re interested in analytic decisions rather than raters’ reliability, the use of ICC takes on a different flavor. The decision to use hierarchical models or ordinary regression largely depends on the structure of your data and the specific research questions you are asking.

Hierarchical models are particularly useful when your data has multiple levels of grouping, such as patients within hospitals or repeated measurements on the same subjects over time. They allow you to explicitly model variance at each level of the hierarchy, which can give you deeper insights into the data. If the ICC is close to zero, this would suggest that the hierarchical structure might not be providing much information beyond what you’d get with an ordinary regression.

So, given that your interest lies in analytic decisions and not in rater reliability, an ICC of 0 could actually simplify your analytic strategy. It suggests that treating your data hierarchically may not provide additional benefits over a simpler ordinary regression model.

41#

icc has brought us to the end of the road:

  • we can’t go any further with the data

  • need to go back to the data generating process

  • to understand the process that generated the data

this is where we reunite with counterfeiting!

42#

\( \Large \left\{ \begin{array}{ll} \text{Truth/Free/Agent} \\ \text{} \\ \text{Know/John 8:32/Verb} \ \ \left\{ \begin{array}{l} \text{Rigor} \text{} \\ \text{Error} \ \ \ \ \ \ \ \ \ \left\{ \begin{array}{l} \text{Variance} \\ \text{Bias} \end{array} \right. \\ \text{Sloppy} \end{array} \right. \left\{ \begin{array}{l} \text{Information/Control} \end{array} \right. \\ \text{} \\ \text{Morality/Slave/Object} \end{array} \right. \)

  • explain/control

  • literally the end of the road

  • we can’t go any further with the data

    • need to go back to the data generating process (rct at infinitely many levels of individual & group)

    • and therein we find that two roads diverged in a wood (truest approach unbiased, but v.imprecise)

    • one is the road of truth and the other is the road of fraud (selective, emotive reporting, p-hacking, compelling)

43#

  • come full circle these 43 years:

    1. philosophy (explo85) 0-5

    2. knowledge (student) 6-35

    3. error (faculty) 36-43

    4. icc (gtpci) 40-44

    5. back-at-one (tenure) 45-50

      • truth; amoral (one is trained to treat ephors as gods; remove error of man)

      • fraud; moral (spartan ephors in 300; like magesterium in vatican)