Idiographic Dynamics: Measurement & Analysis at the Individual - - PowerPoint PPT Presentation

Idiographic Dynamics: Measurement & Analysis at the Individual Level

Aaron J. Fisher, Ph.D.

Assistant Professor Department of Psychology University of California, Berkeley

id·i·o·graph·ic

adjec&ve ¡ ¡ pertaining ¡to ¡or ¡involving ¡the ¡study ¡or ¡ ¡ explica4on ¡of ¡individual ¡cases ¡or ¡events ¡

¡

nom·o·thet·ic

adjec&ve ¡ ¡ pertaining ¡to ¡or ¡involving ¡the ¡study ¡or ¡ formula4on ¡of ¡general ¡or ¡universal ¡laws ¡

Idiographic vs. Nomothetic

• Our approaches to assessing psychological

constructs are almost exclusively nomothetic (i.e. aggregated across individuals).

• Yet, we are typically interested in individuals
• 1. Idiographic processes should be assessed via

idiographic methodologies.

• 2. We endeavor to be able to generalize to a population.

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Nomothetic Problems I

• Nomothetic analyses generalize to the

population (at best) – not individuals

• Idiosyncrasies are washed out as noise

– But the heterogeneity among individuals is almost certainly meaningful signal

Nomothetic Problems II

• Dynamics

– Analyses reveal how constructs relate across the group – between subjects – Cannot reveal or reflect how constructs relate within individuals – Relative rank-order of depressed mood and anxiety is unrelated to the dynamics of these phenomena within a single individual

• (c.f. Fisher & Boswell, 2016)

Idiographic Problems

• Heterogeneity and Idiosyncrasy

– How best to model these data? – How to leverage individual data to inform models of psychopathology and treatment?

Intra- vs Inter-Individual

• Group-level aggregations of time-varying

phenomena betray the heterogeneous nature of human subjects data (Molenaar, 2004).

– They constrain individual variation over time to group-derived trajectories.

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Intra- vs Inter-Individual

Uher (2011), Harvard Review of Psychiatry

Intra- vs Inter-Individual

Uher (2011), Harvard Review of Psychiatry

Ergodicity

• Wherein the structure of inter-individual

variation ≡ intra-individual variation.

• Electrons in an electromagnetic field ü
• Human behavior?

– Poor empirical evidence to support

• (c.f. Borkenau & Ostendorf, 1998)

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Why Worry About Ergodicity?

• Every process is time-varying
• We want some confidence that behavior

measured at one point will predict later behavior

• If a process is non-ergodic, less confidence

that aggregate estimates will provide reliable (replicable) measurement of an individual’s behavior over time

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Conducting Person- Specific Analyses

Treatment of Repeated Measures

• Longitudinal Analyses

– Sequential measurements have a temporal

• rder.

– For example in a treatment study we might have 5 time points:

• Pre, Post, 6-, 12-, and 24-month follow up

– Statistical tests are related to the sequential

• rder:
• Repeated Measures ANOVA
• Latent Growth Modeling (LGM)
• Mixed-effect Modeling (MLM, HLM, etc.)
• Growth Mixture Modeling

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Treatment of Repeated Measures

• Time Series Analyses

– Requires intensive repeated measurements

• Perhaps a minimum of ~ 30 observations

– Time is considered stationary, that is, statistical parameters do not vary with time.

• Specifically, we want to assume that the mean

and variance are time-invariant.

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Treatment of Repeated Measures

• Time Series Analyses

– Analyses are aggregated across observations and time points do not relate to a specific point in time or observation point.

Ø Instead, analyses represent the relationship between any 2 (3, 4, etc.) points in time.

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Time Series Covariance

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• A block-Toeplitz matrix is a covariance matrix of

time-lagged relationships

• We can divide the matrix into Contemporaneous

and Lagged sets of relationships

t ¡-­‑ ¡1 ¡ t ¡ t ¡-­‑ ¡1 ¡ t ¡ = ¡Contemporaneous ¡ Correla4ons ¡ ¡ = ¡Lagged, ¡Causal ¡ ¡ Rela4onships ¡

Block-Toeplitz Matrix

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Depressed ¡ Mood ¡(t-­‑1) ¡ Anxiety ¡ (t-­‑1) ¡ Depressed ¡ Mood ¡(t) ¡ Anxiety ¡ (t) ¡ Depressed ¡ Mood ¡(t-­‑1) ¡ 1 ¡ Anxiety ¡ (t-­‑1) ¡ .75 ¡ 1 ¡ Depressed ¡ Mood ¡(t) ¡ .84 ¡ .08 ¡ 1 ¡ Anxiety ¡ (t) ¡

• ­‑.37 ¡

.55 ¡ .75 ¡ 1 ¡

Vector-Autoregressive Model

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Depression ¡ (t-­‑1) ¡ Anxiety ¡ (t-­‑1) ¡ Depression ¡ (t) ¡ Anxiety ¡ (t) ¡

.75 ¡ .84 ¡

• ­‑.37 ¡

.55 ¡ .24 ¡