This Second Edition of The Tao of Statistics: A Path to Understanding (With No Math) provides a reader-friendly approach to statistics in plain English. Unlike other statistics books, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts as well as some of the most complex statistical models in use. The Second Edition adds coverage of big data to better address its impact on p-values and other key concepts; material on small data to show readers how to handle data with fewer data points than optimal; and other new topics like missing data and effect sizes. The book’s two characters (a high school principal and a director of public health) return in the revised edition, with their examples expanded and updated with reference to contemporary concerns in the fields of education and health.

Digging Deeper—Structural Equation Models

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  • Path analysis
  • Latent traits, too
  • More insights

Structural equation modeling is the marriage of path analysis (just seen) with factor analysis (seen just before it). Now, latent traits, such as math ability or propensity to be a compliant patient, can be modeled with all of the benefits of path analysis and factor analysis combined. Structural equation models can also accommodate nested (i.e., hierarchical or grouped) data, but they become difficult to draw on two-dimensional paper at that point. Intellectually, they are appealing models. Graphically, they can be terrific. From a statistical estimation standpoint, they can present a series of problems. As a suggestion, have a large budget for statistical help.

Furthermore, structural equation models have all of the weaknesses of the two combined ...

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