Index Catalog

Registry-backed indices can be selected in screen() / composite(). Response-time helpers use a different input domain and are listed separately.

Inspect the same registry metadata programmatically or from the command line:

from ier import index_catalog

catalog = index_catalog()
print(catalog["evenodd"]["required_options"])

ier indices
ier indices --format json --output indices.json

The catalog reports flag direction and mode, screen/composite availability and defaults, and options that must be configured before an index can run.

Matrix indices

Name	Construct	Flag when	Screen default	Composite	Extra config
irv	Intra-individual response variability	low	yes	yes	—
longstring	Max consecutive identical responses	high	yes	yes	—
longstring_pattern	Repeating response patterns	high	yes	yes	longstring_max_pattern_length
mahad	Mahalanobis distance (multivariate outlier)	high	yes	yes	—
psychsyn	Psychometric synonym consistency	low	yes	yes	psychsyn_critval
psychant	Psychometric antonym consistency	low	no	yes	psychant_critval
person_total	Agreement with the sample item profile	low*	yes	yes	—
markov	Transition entropy	low	yes	yes	—
missing_rate	Missing-response proportion	high	no	yes	optional item subset / applicability mask
u3_poly	Polytomous person-fit / Guttman-like	high	yes	no	scale_min / scale_max
midpoint	Midpoint responding	high	yes	no	scale_min / scale_max, midpoint_tolerance
acquiescence	Agreeing / yea-saying	high	yes	no	scale bounds; optional item lists
guttman	Guttman errors	high	yes	yes	guttman_normalize
individual_reliability	Split-half individual reliability	low	no	yes	reliability_n_splits, seed
onset	Carelessness onset item index	present	no	no	onset_window_size, onset_min_items
evenodd	Even-odd consistency	low	no	yes	evenodd_factors
mad	Mean absolute paired difference	high	no	yes	MAD item lists / optional scale bounds
lz	lz person-fit	low	no	yes	optional IRT params via direct API; overflow-safe logistic kernel
semantic_syn	Predefined synonym consistency	low	no	yes	semantic_item_pairs
semantic_ant	Predefined antonym consistency	low	no	yes	semantic_item_pairs, optional scale bounds
infrequency	Failed attention / bogus items	high	no	yes	item indices, expected responses, missing policy

* person_total flags unusually low correlations with the sample-wide item profile under the default low-direction percentile rule.

individual_reliability(..., random_seed=...) uses an isolated reproducible random stream. It does not reset or advance NumPy's process-wide random state.

The registry's longstring index uses longstring_scores() for numeric response matrices. The standalone longstring() helper analyzes text strings only and rejects numeric or multidimensional arrays.

semantic_ant reverse-scores the second item in each configured pair before computing consistency. Pass scale_min and scale_max through IndexOptions when the matrix does not contain both response-scale endpoints; otherwise the bounds are inferred from the observed data.

mad also reverse-scores the second item in each pair. Provide mad_scale_min and mad_scale_max when observed responses may omit a scale endpoint or use fractional endpoints. Higher MAD values mean greater paired inconsistency. The standalone semantic_syn_flag() and semantic_ant_flag() helpers flag unusually low consistency scores.

missing_rate is opt-in because planned skip logic and matrix preprocessing can create legitimate omissions. Use IndexOptions.missing_item_indices to restrict registry scoring to a fixed required-item subset, or pass the same subset as item_indices to the standalone helper. For respondent-specific skip logic, provide a Boolean missing_applicable_mask through IndexOptions or applicable_mask directly. False cells are excluded from both the numerator and denominator; rows without applicable selected items return NaN and are not flagged. The CLI exposes fixed subsets through --missing-item-indices.

infrequency preserves its historical missing-response behavior with missing="pass": unanswered checks do not count as failures and remain in a proportion denominator. Choose "fail" for conservative scoring, "omit" for available-case proportions, or "propagate" to require complete attention-check data. Configure registry scoring with IndexOptions.infrequency_missing and the CLI with --infrequency-missing. The standalone infrequency_flag() can flag either counts or proportions.

Response-time indices (standalone — not in the registry)

These helpers take timing matrices (durations), not item-response matrices. They are intentionally excluded from screen() / composite() so item scores and timestamps are never mixed by accident. Compute them separately and merge flags in your analysis code if needed.

Function	Signal	Typical flag
response_time	Central tendency of RT	low (too fast)
response_time_consistency	RT coefficient of variation	low (too uniform)
response_time_flag	Percentile / threshold flagging	low
response_time_mixture	Stable mixture P(fast component)	high
response_time_score_flags	Reflag retained direct or mixture scores	low or high

Plot helpers

Requires insufficient-effort[plot]:

• plot_distributions(screen_result)
• plot_flag_counts(screen_result)
• plot_flagged_heatmap(screen_result)
• mahad_qqplot(...)