SOA Norms Report

  1. Introduction
  2. Collecting the Raw Data
    1. ARIC
    2. GMBI
    3. NACC
    4. MCSA
  3. Summarizing the Raw Data
    1. Demographics
    2. Instruments
  4. Generating the Normative Samples
    1. ARIC
    2. GMBI
    3. NACC
    4. MCSA
    5. Pooled
  5. Summarizing the Normative Data
    1. Demographics
    2. Instruments
    3. Groups by Instruments in the Pooled Sample
  6. Generating Instrument Level Models
    1. Notes on the Trail Making Tests
    2. A Note on the LDST
  7. Generating Domain Level Models
  8. Generating Algorithmic Classifications

Generating Domain Level Models

A domain level model lets us compute a domain level z-Score for a SOA participant using the following idea: \[ \text{Domain} \text{ Level} \text{ z-Score} = \frac{( \text{Observed} \text{ Domain} \text{ Mean}) - ( \text{Expected} \text{ Domain} \text{ Mean})}{\text{Standard Deviation}} .\]

In the equation above, "observed domain mean" refers to the arithmetic mean of the instrument level z-Scores within that domain for the participant.

Meanwhile, "expected domain mean" refers to the mean of the "observed domain means" for the participants in the normative sample.

Finally, "standard deviation" refers to the standard deviation of the "observed domain means" for the participants in the normative sample.

Representing a Domain Level Model

A domain level model is represented by an "expected domain mean" and a "standard deviation", as defined above.